This study addresses the search for Carbon-Enhanced Metal-Poor (CEMP) stars based on low-resolution spectra and proposes a machine learning solution based on deep learning/neural networks. From the LAMOST DR8 spectral library, this work discovered 12,766 CEMP star candidates. For ease of reference and use, we provide the estimated parameters Teff, log g, [Fe/H], and [C/H] for the targets found in the search.

In this dataset we present plots of the phase light curves of 72,505 period variable stars. These include the original light curves and the fitted light curves. TESS data from the first 67 sectors with 2-minute cadence. We also provide their relevant parameters including period, physical parameters, and light curve parameters in a table.

It is code shared for the paper 'An Ultra-Fast Image Simulation Technique with Spatially Variable Point Spread Functions', which is published in the Astronomical Journal. The code could genereate images with very fast speed and generated images have spatially variable point spread functions. Users could directly refer to the jupyter-notebook file for introductions.

This dataset includes photometric observation data from the CSP Telescope at the Xingming Observatory for 48 eclipsing binary stars from September 2nd to September 22nd, 2021, over a period of 13 days. Each eclipsing binary star has photometric data from both the Sloan r' and Sloan i' bands. Each file contains HJD times and magnitude values.

Table 2 presents the observational results of the radial velocities for 13 eclipsing binary stars obtained from the LAMOST DR9, including the radial velocity values of the primary and secondary components. Table 3 lists parameters derived from the orbital parameter analysis of 48 eclipsing binary stars based on the CSP telescope and TESS survey, such as temperature, orbital inclination, mass ratio, potential energy, radius, and luminosity. Table 4 is a statistical table of the eclipsing times for 26 eclipsing binary stars based on equipment including CSP, TESS, WASP, AAVSO, ASAS-SN, O-C Gateway, and ZTF. Table 5 presents the fitting parameters based on O-C analysis and mass transfer parameters for 26 eclipsing binary stars using equipment including CSP, TESS, WASP, AAVSO, ASAS-SN, O-C Gateway, and ZTF.

This dataset includes: (1) 1697 strong gravitational lens candidates, including 303 previous lenses and candidates (6 known lenses and 297 known candidates), and 1394 new gravitational lens candidates, which include 102 high-quality candidates. The corresponding included right ascension, declination, photometric redshift, spectroscopic redshift, reference and magnitude information in the g, r, and z bands. There are 152 × 152 pixels images synthesized in g, r, and z bands. (2) A list of false positives.

The table presents the classification outcomes, including RV values and associated errors. The columns are: A(J2000); DEC(J2000); Gaia source ID from Gaia DR3; G(mag) is the G magnitude from Gaia; Multiplicity, which is the result of the classification; planID is the plan name from LAMOST-MRS; spID is the spectrograph ID from LAMOST-MRS; fiberID is the fiber ID of the target from LAMOST-MRS; LMJM,showing the local modified Julian Minute at the start of exposure; S/N representing the median S/N of all pixels; RV1 (km/s), RV2 (km/s), and RV3 (km/s), which are the RVs calculated from the first step using the conventional CCF technique and arranged in order of their values. The full version of this table with 30,288 rows.

Introduction: The UPdec-Webb dataset comprises coadditions of JWST NIRCam images created using the UPDC algorithm(doi: 10.1093/mnras/stac2664). Each coaddition goes through 10 iterations. The more iterations, the better the deconvolution effect, but it also amplifies the noise. Users should select the data product with the number of iterations that aligns with their scientific objectives. UPdec-Webb doubles the sampling rate compared to the original exposure images. Following 5 iterations, the spatial resolution is enhanced by at least double. Dataset Composition: The dataset is entirely composed of *.fits.fz files. For each band, the 0th iteration (referred to as Drizzle) fits file includes multiple extensions: SCI, error, context, weight, VAR_POISSON, VAR_RNOISE, VAR_FLAT, HDRTAB, and ASDF_METADATA. The 1st to 10th iterations currently only include the SCI extension. We will incorporate error, context, weight, VAR_POISSON, VAR_RNOISE, VAR_FLAT, HDRTAB, and ASDF_METADATA in future updates. Depending on different scientific objectives, users can choose data products from different iterations to conduct their research.

Here we provide the cross-correlation function curves of the lowest mass ratio contact binary ever found in the universe:TYC 3801-1529-1. "CCF Curve.zip" is the CCF curves for all BOSFC spectra, and "2.4m.gif" represnt the animation of the 25x25 pixels star images, along with a flux distribution map, and the flux distribution fitting using a 2D Gaussian function.

The “lensed_sn_mock.fits" file is a strongly lensed supernovae mock catalog from the paper "Forecast of strongly lensed supernovae rates in the China Space Station Telescope surveys". The "lensed_qso_mock.fits" file is a strongly quasars mock catalog. The velocity dispersion function of the lensing galaxy is from the paper "Revisiting the Lensed Fraction of High-redshift Quasars" (doi: 10.3847/1538-4357/ac409b). The quasar luminosity function is from the paper "Gravitationally lensed quasars and supernovae in future wide-field optical imaging surveys" (doi: 10.1111/j.1365-2966.2010.16639.x).

Including: 1. A catalog of 7,681 cool stars which simultaneously have the HQ measurements of the DIBs λ5780, λ5797 and λ6614; 2. Three catalogs of the HQ samples of the DIBs λ5780, λ5797 and λ6614. The number of measurements of three DIBs are listed as follows: - 5780: 176,831 - 5797: 13,473 - 6614: 110,152 2. Three catalogs of the MQ samples of the DIBs λ5780, λ5797 and λ6614. The number of measurements of three DIBs are listed as follows: - 5780: 27,598 - 5797: 42,735 - 6614: 21,635

This paper encompasses three distinct datasets: an empirical sample consisting of 5105 M-type star spectra along with their stellar labels; a grid of 1365 high Signal-to-Noise ratio (S/N) spectra and their labels, selected from the empirical, which was utilized in the stellar parameter pipeline for M-Type stars (LASPM) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST); and an updated catalog from LASPM detailing the stellar atmospheric parameters, covering 877,570 spectra for 667,483 M-type stars obtained from LAMOST DR11.

The dataset contains all galaxies classified morphologically as ‘REX’, ‘EXP’, ‘DEV’ or ‘SER’, totaling 1,533,107,988 sources. we estimate photometric redshifts for these galaxies using both EAZY and models created by CatBoost. The complete set of predictions is stored across 360 files. In this predicted galaxy redshift catalog, there are 144,155,773 sources with bitmask=0, indicating potential contamination from corrupted imaging pixels, neighboring bright stars, globular clusters, or nearby galaxies. Consequently, the photometric redshifts for these sources are considered unreliable. Additionally, there are 312,960,837 sources meeting the criteria of having at least one exposure in four optical bands g,r,z,i with g 21.5, we cross-match our catalog with the photometric redshift catalog released in \citet{Cosmos2020}, using $lp\_zBEST$ as the true redshift. We retain matches within a 1 arcsecond radius, resulting in 194,197 sources (referred to as DESI+COSMOS) with at least one exposure in four optical bands and true redshifts ranging from 0 to 6.

This paper mentions observations of eight totally eclipsing contact binary stars, 'light curve.xlsx' is the observation data of the eight targets. From 2019 to 2021, we used the 60 cm Ningbo Bureau of Education and Xinjiang Observatory Telescope (NEXT) in China to observe the eight contact binary stars. This telescope is equipped with an FLI PL23042 CCD and has a field of view of 22'× 22'. During the observations, standard Johnson–Cousins filter and Sloan filter were used. And 'Table9.xlsx' is the eclipsing times of them, including data from the O-C Gateway and AAVSO, as well as global photometric surveys ASAS-SN, ZTF, TESS, and SuperWASP.

Based on the open datasets of ASAS-SN, Gaia and ZTF, we construct a compatible light curve dataset named LEAVES for automated recognition of variable stars, which can be used for training and testing new classification algorithms. The dataset contains a total of 977,953 variable and 134,592 non-variable light curves, in which the supported variables are divided into 6 superclasses and 9 subclasses. We validate the compatibility of the data set through experiments and employ it to train a hierarchical random forest classifier. Experimental results prove that the classifier is more compatible than the classifier established based on a single band and a single survey, and has wider applicability while ensuring classification accuracy. Here we provide the light curves of these objects by class in csv files.

This catalog is the first all-sky Fermi-LAT catalog from China, representing a significant milestone in the search for high-energy gamma-ray sources across the entire sky. The raw event files for data analysis are derived from the Fermi-LAT P8R3_V3 survey data. The catalog includes 1,379 newly identified sources with significance levels exceeding 4σ, among which 497 sources have significance levels exceeding 5σ. For each new source, the 4FGL-Xiang dataset provides detailed information, including spatial positions, spectral data, spatial distribution of high-energy radiation, flux measurements, and other key characteristics. The observation period for this dataset spans from August 4, 2008, to December 29, 2023, with an energy range of 500 MeV to 1 TeV. This dataset includes the following files 4FGL-Xiang.fits: This file contains two sub-tables: one listing the new GeV sources and the other listing the new GeV extended sources. 4FGL-Xiang_Region_File.reg: This region file includes all gamma-ray sources across the sky, encompassing our newly discovered GeV sources as well as all sources from 4FGL-DR4. 4FGL-Xiang_Region_File_No_Name.reg: This file contains the same information as 4FGL-Xiang_Region_File.reg but excludes the names of the sources from 4FGL-DR4. 500MeV_TSmap_fits.rar: This archive contains the TS maps of 72 ROIs in FITS format, for energies above 500 MeV. 500MeV_TSmap_eps.rar: This archive contains the TS maps of 72 ROIs in EPS format, for energies above 500 MeV. Table 5.xlsx: This file contains the analysis results of the robustness verification.

Utilizing 3D gas and dust extinction data, we have identified 112 strongly correlated dust-CO clouds, which are well associated with both dust and CO, in regions of low Galactic latitude (|b|<10 deg). For the subset of 112 strongly correlated dust-CO clouds, we have quantified their physical properties using data derived from CO observations and generated a catalog. The catalog includes each MC's Galactic coordinates, distance, boundary extinction, visual extinction, peak CO emission line brightness temperature, peak CO emission line brightness LSR velocity, LSR velocity dispersion, LSR velocity range for the integrated intensity, and total intensity of the CO emission line. We have also detailed the measured HI column densities N_HI, the column density of hydrogen nuclei N_H, and the ratio N_H/A_V in our public database. The database containing these parameters and CO identification maps for these MCs is publicly accessible through this website.

This dataset includes the sample training data for GRRIS (the real-time intra-site observation scheduling scheme for telescope array using graph and reinforcement learning) and data behind the figures in the paper. A ReadMe file describing how the data is used is also provided to reduce the barrier to reuse and reproducibility of these results.

This dataset consists of low-resolution stellar spectra from the LAMOST DR10, predicted based on the MaStar empirical spectral library, covering spectral types O to M and an effective temperature (Teff) range of 3500K-30000K. The dataset provides the recommended values for Teff, log g, [Fe/H], and [α/Fe]. Please refer to the paper for the recommended values of each parameter.

A sample of 320, 028 subgiant stars from LAMOST DR7 and Gaia eDR3. For each star, the catalog contains 61 labels, including RA(J2000) DEC(J2000), stellar age, mass, (log10) distance, atmospheric parameters (Teff, logg, [Fe/H], [alpha/Fe]), absolute magnitude MK, extinction E(B-V), orbital parameters, Gaia and 2MASS photometry, etc. Size of the catalog: 83MB.

A catalog of atmospheric parameters and elemental abundances for about 520,228 stars in the early data release of the DESI spectroscopic survey. The catalog content includes stellar basic atmospheric parameters (Teff, logg, vmic) and abundances for 12 elements (C, N, O, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Ni) derived with the DD-Payne method, along with extinction, distance, and orbital parameters further inferred based on the atmospheric parameters and multi-band photometry. Internal precision of the label estimates are about 20K for Teff, 0.05dex for logg, and 0.05dex for most elemental abundances. Distance of the sample stars spread out to 100 kpc. The catalog is in .FITS format. Size of the catalog: 233MB.

This dataset contains the features obtained from X-ray sources through feature extraction calculations, as well as the corresponding types of each source as labels. This includes the data features of LEIA and EP simulation data. Features include energy spectral features, light curve features, galactic longitude and galactic latitude. Please refer to the paper for details.

We used hierarchical clustering algorithm to select members for the open cluster NGC 6530 and found multiple structures around the cluster. We found a total of 5 substructures. The data are formatted into 6 columns. The first column is the Gaia DR3 ID, the second and third columns are RA and Dec, the fourth and fifth columns are 2D proper motion, and the sixth column is the substructure id.

This data is the material of the paper. There are four files: Table 1 GWAC flares and properties of stars, Table 2 Flares from TESS or K2 light curves, Table 3 Radial Velocity and LHα/Lbol obtained from spectra, and a zip file including all 163 GWAC superflares from 162 individual stars.

This dataset includes the following components: 1. Code of the GalCenterNet: A deep learning-based object detection algorithm designed to identify low surface brightness galaxies from large-scale astronomical image datasets. The GalCenterNet is trained using composite images from the Sloan Digital Sky Survey (SDSS) Data Release 16, these images are retrieved from the SDSS Science Archive Server and have been processed into 3-color images using the i-r-g to R-G-B color mapping method. 2. Catalog of Low Surface Brightness Galaxy Candidates: A catalog containing low surface brightness galaxies identified by the GalCenterNet. 3. Catalog of Low Surface Brightness Galaxies: Catalogs for both the training and validation sets, consist of confirmed low surface brightness galaxies.

Light curve figures and tables for 2254 multi-mode delta Sct stars from ZTF DR20。Table 3: periods of triple- and quadruple-mode delta Sct stars using ZTF DR20. Table 4: parameters of multi-mode delta Sct stars in ZTF DR20. Table 5: Comparison of the first periods and second periods of multi-mode delta Sct stars determined by ZTF DR20 and the OGLE catalog. g_f.zip and r_f.zip files: g & r band figures for period determination based ZTF DR20. They include light curve and power spectral density distribution diagrams for each radial pulsation period (F, 1O, 2O, 3O).

It is the source code for the paper 'An Image Quality Evaluation and Masking Algorithm Based On Pre-trained Deep Neural Networks', which will be published in the AJ. The code could be used for fast image quality evaluation in different applications.

The aim is to provide the daily sunspot number, pore number, umbra number, penumbra number, hole number, area, umbra area, penumbra area of sunspot drawings obtained from the Chinese six observing stations by the DMT_BGSeg method. We also provide the information of each pore, spot, umbra and hole.

The dataset is used for astronomical knowledge entity extraction research and consists of 30 astrophysical journal articles with their annotated celestial object identifiers and telescope names. The research topics of these articles cover a wide range of subfields in astronomy, including galaxies, stars, planets, etc. They also encompass numerous observation wavelengths such as optics, radio, and X-rays.

This dataset provides information on the positions, magnitudes in g and r bands, classifications, light curves, and periods of nearly 6000 variable sources in the filed of view of the M31 galaxy. It includes 8 microlensing event candidates, 19 flares, and 42 sinusoidal-shaped light curves.

This is the code for the paper 'CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)'. Docker version of the code could be obtained from Peng Jia through email 'robinmartin20@gmail.com'.

Dataset Description: A total of ten datasets are divided into three parts: 1. Stellar Classification: These datasets also have an SNR greater than 5 and greater than 10, respectively; 2. Stellar Subclass Classification: These datasets have an SNR greater than 5 and greater than 10, respectively; 3. Spectral Full Classification: These datasets have no specific limit on the SNR. SC, SS, and FC in the names of the datasets stand for the three tasks of stellar classification, stellar subclass classification, and full classification, respectively. S and L stand for data from SDSS DR18 and LAMOST DR10, respectively. 5 and 10 represent signal-to-noise ratios greater than 5 or 10. Each dataset underwent 5-fold cross-validation, where the testing set for each fold constituted 20% of the data. Additionally, 25% of the training set was used as a validation set, resulting in a final split ratio of 60% for training, 20% for validation, and 20% for testing.

The code uses the clustering method to build response matrix of each pixel in the CMOS according to the ground test data, which could be used for different CMOS cameras in satellites.

This database includes a total of 323 flaring ARs (106 M/X-class flaring ARs and 217 C-class flaring ARs ) appearing from June 2010 until December 2022 observed by the SDO/HMI. Each entry in MagParDB contains the following information: TSTART - flare start time [UT] TPEAK - flare time of peak X-ray flux [UT] TFINAL - flare end time [UT] IXPEAK - peak 1-8 ? X-ray flux [W/M^2] LON - flare heliographic longitude [DEG] LAT - lare heliographic latitude [DEG] ARNUM - active region number Confined/Eruptive - C(confined; not associated with a CME)/E（eruptive; associated with a CME） E_TOTAL - total photospheric free magnetic energy density [erg cm^-1] AREA_HED - area of HED (high free energy density) [CM^2] Flux_HED - total unsigned magnetic flux within HED region [Mx] SHEAR - mean shear angle within HED region [DEG] UJ_MEAN- mean unsigned vertical current density within HED region [mA m^-2] J_TOTAL- total unsigned vertical current within HED region [A] UHC_MEAN- mean unsigned current helicity within HED region [G^2 m^-1] HC_TOTAL- total unsigned current helicity within HED region [G^2 m^-1] GBH_MEAN- mean horizontal gradient of horizontal field within HED region [G m^-1] ALP_MEAN- mean characteristic twist parameter within HED region [Mm^-1] LPIL- length of the polarity inversion lines with a steep horizontal magnetic gradient [Mm]

A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia DR3 quasar candidate catalog with more than 1.5 million sources in the 3π sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia DR3 quasar candidates as stars, galaxies, and quasars. To construct training/validation datasets for the classification, we carefully built two different master stellar samples in addition to the spectroscopic galaxy and quasar samples. An ensemble classification model is obtained by averaging two XGBoost classifiers trained with different master stellar samples. Using a probability threshold of p_QSO_mean > 0.95 in our ensemble classification model and an additional cut on the logarithmic probability density of zero proper motion, we retrieved 1,545,514 reliable quasar candidates from the parent Gaia DR3 quasar candidate catalog. We provide photometric redshifts for all candidates with an ensemble regression model. For a subset of 89,100 candidates, accurate spectroscopic redshifts are estimated with the Convolutional Neural Network from the Gaia BP/RP spectra. The CatNorth catalog has a high purity of > 90% while maintaining high completeness, which is an ideal sample to understand the quasar population and its statistical properties. The CatNorth catalog is used as the main source of input catalog for the LAMOST phase III quasar survey, which is expected to build a highly complete sample of bright quasars with i < 19.5.

A new stellar database of the Ca II Triplet, which serves as an indicator for characterizing the chromospheric activity of stars. It includes R, R^+ index for Ca II IRT, stellar parameters and other valuable information, the uncertainties of the index also be given too. More detail about it please read our paper.

GaiaDR3XPspectracorrectionV1 is an astronomy Python package to provide a correction for the Gaia DR3 XP spectra. The corrected spectra can significantly eliminate the systematic errors of the original Gaia DR3 XP spectra, please refer to our paper (Huang et al. 2024) for more information. We can give robust corrections for sources in the roughly following range: −0.5 < BP − RP < 2 3 < G < 17.5 E(B − V) < 0.8 Sources that do not fall into this range are not tested for the correctness of the corrections given by the correction package.

For LAMOST data release 9 we provide \vsini~for 124,601 stars measured with the medium resolution spectrograph (MRS) and 80,108 from the low resolution spectrograph (LRS). These values are derived based on a $\chi^2$ minimisation between LAMOST spectra and suitably broadened grids of synthetic spectra. The resolution and spectral coverage of LAMOST means that we restrict our \vsini~measurements to stars with 5000 K 50. Our \vsini~values are consistent with those provided by APOGEE17 with a scatter of 8.79 \kms and also consistent with those from the Gaia DR3 and SUN catalogs. We find that for dwarfs and giants with near solar abundances in LAMOST MRS, their \vsini~decreases when \teff~drops, the transition occurs at \teff~$\sim$ 7000~K for dwarfs and 6000~K for giants.

We cross-match the entire Gaia EDR3 data and GALEX GR6+7 AIS data to create an (nearly) all-sky sample. With the GALEX and Gaia data, we have estimated metallicities for about 5 million stars across almost the entire sky, including approximately 4.5 million dwarfs and 0.5 million giants.

49 high-velocity stars (HiVels) with a total velocity in the Galactocentric restframe greater than 70% of the escape velocity.

This paper provides a multi-branch deep neural network for super-resolution of solar magnetograms. The dataset includes the pairs of MDI and HMI magnetograms. It was used to train the deep neural network.

The zipped file has two folds: 1. lag-data, which contains the frequency-resolved lags for various black-hole mass and the Eddington ratio mentioned in the paper 2. Machine-learning, which contains the machine-learning results of the data in the folder lag-data (and a python code example).

This package is a Python package for the Standards of Fundamental Astronomy (SOFA) service of the International Astronomical Union (IAU). It implements the Python package PyMsOfa for SOFA service in three ways: (1) a Python wrapper package based on a foreign function library for Python (ctypes), (2) a Python wrapper package with the foreign function interface for Python calling C code (cffi), (3) a Python package directly written in pure Python codes from SOFA subroutines. It implements all 247 functions in the SOFA service and is based on the latest version released on Oct 11, 2023. This Python package can be suitable for the astrometric detection of habitable planets of the Closeby Habitable Exoplanet Survey (CHES) mission and for the frontier themes of black holes and dark matter related to astrometric calculations and other fields.

These are the data for the paper 'Flares confirmed for the first time to be from the primary component of an eclipsing binary', the paper is published on the Monthly Notices of the Royal Astronomical Society, https://doi.org/10.1093/mnras/stad3251.

This includes the code for the paper 'Perception of Misalignment States for Sky Survey Telescopes with the Digital Twin and the Deep Neural Networks'.

A catalog of the stellar atmosphere parameters of cool dwarfs in Gaia DR3, which is based on multi-band photometry from optical to infrared. The two files of .csv and .mrt contain the same contents. The information of CSV file is as follows: row number: 1,806,921. columns number: 24. columns names: column 1. 'source_id': (Gaia DR3 source_id). columns 2-3. 'RAdeg', 'DEdeg': (Right Ascension and Declination, decimal degrees). columns 4-11. ‘Mrmag', 'Mimag', 'Mzmag', 'MJmag', 'MHmag', 'MKsmag', 'MW1mag', 'MW2mag': (Absolute magnitude in multi bands). columns 12-17. 'Teff-RF', 'e_Teff-RF', '[M/H]-RF', 'e_[M/H]-RF', 'logg-RF', 'e_logg-RF': (parameters and errors by Random Forest algorithm). columns18-23. 'Teff-LGB', 'e_Teff-LGB', '[M/H]-LGB', 'e_[M/H]-LGB', 'logg-LGB', 'e_logg-LGB': (parameters and errors by LightGBM algorithm ). column 24. 'flag': (the boundary of BP - RP = 2.9 mag, flag = 1 while BP - RP > 2.9, otherwise flag = 0). The two tools in this paper are Random Forest (RF) and LightGBM, both of which are machine-learning algorithms based on decision trees.

We added a table of the samples we used in the attachment. Each table corresponds to the samples used in each section of analysis.

The data is the mock catalogs of SDSS DR12 galaxies, mentioned in our paper "Fast generation of mock galaxy catalogues with COLA", which base techniques are Code for Anisotropies in the Microwave Background (CAMB), COmoving Lagrangian Acceleration (COLA) technique, ROCKSTAR halo-finder, Consistent-Trees and Subhalo Abundance Matching technique.

This data contains the first version of the code for the paper 'Image Restoration with Point Spread Function Regularization and Active Learning'.

The zip file contains the fits files for our dual-frequency observations of nine millisecond pulsars, which are folded in both time and frequency. In addition, we also provide separately the fits files (folded in frequency and time) of PSR J1713+0747 at 2250 and 8600MHz in four separate segments ( MJDs 58915-59287, 59426-59427, 59746-59798 and 59801-59857) used for the plots in the article.

The M-type T Tauri Star catalog contains a total of 1094 sources. The catalog contains the following information: sequence number, longitude, latitude, effective temperature, surface gravity, age, mass, spectral type, EW (H\alpha), FWHM of H\alpha, TTS type, whether it is newly discovered, whether it is in accretion, whether it has a Li I absorption line, which star-forming region it belongs to, intrinsic color, color excess, and the disk type.

This dataset contains some basic information for 1,297 SB1 candidates, such as the source_id field of Gaia DR3 catalog and the time span of the observation. It also contains the spectroscopic orbital solutions of 255 SB1s.

This is a classification catalogue of SDSS galaxy spectra by the GalSpecNet and the model itself, presented in the paper . The galaxies from SDSS are classified as star-forming, composite, AGN, and normal galaxies. The catalogue includes basic information about galaxies (specobjid, ra, dec, redshift, and median SNR), classifications (from galSpecExtra/EmissionLinesPort/SIMBAD/NED), and the predictions made by the GalSpecNet model (including the predicted probability for each type and the final predicted result).

The results of processing 50.24 million SDSS stars without corresponding spectra using convolutional neural networks and photometric images. These stars include 7438 O-type stars, 31433 B-type stars, 201189 A-type stars, 909999 F-type stars, 10986047 G-type stars, 18941101 K-type stars, and 19168337 M-type stars.

This database is a live homogeneous database of solar active regions based on SOHO/MDI and SDO/HMI synoptic magnetograms. It provides several basic parameters, including the latitude and longitude of the flux-weighted centroid of two polarities and the whole AR, area, and flux of each polarity. The CR number and label jointly serve to identify a unique AR. More parameters, including polarity separation, tilt angle, initial dipole moment, final dipole moment, and ARDoR, will be given in the future work of the series. There are 2849 ARs in the database, with 1481 ARs in cycle 23 and 1098 ARs in cycle 24. The database covers a large range of ARs, from 136 μHem to 30122 μHem. The database currently covers the time range from May 5, 1996, to June 14th, 2023 (CRs 1909 - 2271) and includes cycles 23, and 24, and part of cycle 25, with continuous extensions planned. It can be used for not only the long-time variation research of active regions (space climate) but also solar cycle prediction.

The zip file contains the fits files for all 194 of our dual-frequency observations, which are folded for both time and frequency. In addition, we also provide separately the fits files of the four observations (MJD 58502, MJD 59075, MJD 59096 and MJD 59209) used for the plots in the article, which are folded for frequency.

We provide two-dimensional maps and a Python package specifically designed to correct position-dependent systematic errors in PS1 photometry. The corrections can effectively achieve a precision of 1--2 mmag when averaged over 14’ regions. The corrected PS1 photometry not only facilitates high-precision investigations but also serves as a reliable reference for calibrating other surveys.

These are the open cluster catalog based on Gaia DR3 and Galactic precession/ inclinational variation fits code for the paper 'The precession and inclinational variation across the plane of the Milky Way revealed by open clusters', the documents are independently completed by the author, and users can refer to the article. See details in Readme.txt, for more questions, please send the email to the author: Zhihong He, hezh@mail.ustc.edu.cn.

This is a newly discovered symbiotic star candidate. We provide the corresponding right ascension declination and relevant magnitude information.

The Stellar Abundances and Galactic Evolution Survey (SAGES) of the northern sky is a specifically-designed multi-band photometric survey aiming to provide reliable stellar parameters with accuracy comparable to those from low-resolution optical spectra. We have obtained this master catalog for the SAGES DR1 which includes items for u and v passbands.

We present an updated catalog of 46,753 radial velocity (RV) standard stars selected from the APOGEE DR17. These stars cover the Northern and Southern Hemispheres almost evenly, with 62% being red giants and 38% being main-sequence stars. These RV standard stars are stable on a baseline longer than 200 days (54% longer than one year and 10% longer than five years) with a median stability better than 215 m/s. The average observation number of those stars are 5 and each observation is required to have spectral-to-noise-ratio (SNR) greater than 50 and RV measurement error smaller than 500 m/s.

This table presents basic information, flux densities and derived properties of the final AGN sample in the paper. The descriptions of the columns are as follows. Index: Object index. Name: Object name. RA (degree): Right ascension (J2000). DEC (degree): Declination (J2000). z: Redshift. logLbol (erg/s): Logarithm of AGN bolometric luminosity. logLbol_e: Uncertainty of logLbol. logMBH (solar mass): Logarithm of BH mass, which has a fiducial uncertainty of 0.4 dex. logEdd: Logarithm of Eddington ratio, whose uncertainty is dominated by that of BH mass. RFeII: Relative FeII strength, defined as the ratio between the equivalent width of FeII to that of broad Hbeta. RFeII_e: Uncertainty of RFeII. Flux_grizy (mJy): Flux density of AGN host galaxy in the Pan-STARRS1 grizy bands after correcting for Galactic extinction and systematic effects. Flux_grizy_e: Uncertainty of Flux_grizy. Re_arcsec (arcsec): Half-light radius of the AGN host galaxy from image decomposition in the i band after correcting for systematic effects. Re_arcsec_e: Uncertainty of Re_arcsec. Re_kpc (kpc): Half-light radius of the AGN host galaxy from image decomposition in the i band after correcting for systematic effects. Re_kpc_e: Uncertainty of Re_kpc. n_i: Sersic index from image decomposition in the i band after correcting for systematic effects. n_i_e: Uncertainty of n_i. color (mag): Rest-frame SDSS g-r color from SED fitting. color_e: Uncertainty of color. logMs (solar mass): Logarithm of stellar mass from SED fitting. logMs_e: Uncertainty of logMs. Morph: Morphological classification based on n_i.

This article reports a sample of 1830 strong [O iii]λ5007 emission-line compact galaxies discovered with the LAMOST spectroscopic survey and the photometric catalog of SDSS. We newly identify 402 spectra of 346 strong [O iii]λ5007 emission-line compact galaxies by finding compact isolated point sources. Combined with the samples in our previous work (Liu et al. 2022), this returns a sample of 1830 unique strong [O iii]λ5007 emission-line compact galaxies with 2033 spectra of z ≤ 0.53. For the sources with 2σ [O iii]λ4363 detections, we calculate the gas-phase metallicity with the direct-Temethod, and verify that the strong-line metallicity diagnostics calibrated with the direct-Te method also applies to this sample. The strong [O iii]λ5007 emission-line compact galaxies fall below several Te-calibrated mass-metallicity relations. The N/O measurements of the strong [O iii]λ5007 emission-line compact galaxies mainly locate at a plateau at low metallicity, indicating the product of primary nucleosynthesis. The Ne3O2 and O32 relation follows a tight linear relation with no redshift evolution. The Ne3O2 anti-correlates with the stellar mass, and at fixed stellar mass the Ne3O2 increase with the redshift. Eight sources with asymmetric [O iii]λ5007 emission-line profiles have been identified, however with no [O iii]λ4363 detection, which proves the rich metal content and complex ionized gas kinematics within the galaxies. Higher-resolution spectroscopy will be necessary to identify the ionized gas components in detail.

Supplymentary for "Predicting the geoeffectiveness of solar coronal mass ejections with the aid of Sun-as-a-star spectroscopic observations".

LAMOST DR8 MRS CSN Stellar Parameters and Chemical Abundances Catalog provides a catalog of stellar parameters and chemical elements (C, Na, Mg, Si, Ca, Ti, Cr, Mn, Co, Ni, and Cu) of 1.38 million LAMOST-II DR8 MRS spectra. It is the first one based on the ab initio atmospheric model and the first application of Cycle-StarNet to more than 1 million real data sets. The method incorporates the physical model and data-driven approaches to improve the shortcomings of the low S/N of observed spectra and imperfections of theoretical synthetic spectra in the fitting process. The catalog contains information: the identifier for the corresponding star (starid), LAMOST spectrum identifier (medid), Gaia identifier for 69 percent of spectra (Gaia source id), coordinate information (right ascension (RA), declination (Dec)), signal-to-noise of the spectra (S/N of blue and red arm), effective temperature (Teff), surface gravity (log g), metallicity [Fe/H], macroturbulence, microturbulence, elemental abundance ([X/Fe]), their errors and quality flags, and the total quality flag.

This is the source code for the paper 'Observation Strategy Optimization for Distributed Telescope Arrays With Deep Reinforcement Learning', which has been accepted by the AJ.

Source catalogs from the Nuclear Spectroscopic Telescope Array (NuSTAR) extragalactic surveys of the Extended Chandra Deep Field-South (E-CDF-S) and Chandra Deep Field-North (CDF-N).

The fit results plots and corner plots of all 153 HB stars.

This project includes the estimated catalog computed from about 8.21 million low-resolution spectra in LAMOST DR8, code, trained models, and experimental data for astronomical science exploration and data processing algorithm research, respectively.

I present a catalog of distances to 63 molecular clouds located within ~2.5 kpc of the Sun. The cloud distances are derived based on utilizing the Gaia DR3 parallaxes of the young stellar objects (YSOs). By identifying AllWISE YSO candidates (YSOCs) with infrared excesses and combining them with published YSOC catalogs, I compile an all-sky YSOC sample that is devoid of a significant proportion of contaminants. Using Gaia DR3 astrometric measurements, I associate over 3000 YSOCs with 63 local clouds and obtain the average distance to each cloud by fitting the YSOC parallax distribution within the cloud. Here I present the table of cloud distance, table of all-sky YSOCs, and the table of YSOCs associated with local clouds.

ATLAS empirical stellar library is separated into two groups, ATLAS-A and -T. The former contains 5584 spectra covering spectral types from O to M-type and some special types such as A supergiant, blue horizontal-branch (BHB), and Carbon stars. All the spectra have absolutely calibrated fluxes at certain characteristic wavelengths corresponding to the optical passbands with accuracy better than 2.6% by comparing with PanSTARRS1 g, r, and i bands photometry. ATLAS-A contains 5342 directly observed stellar spectra with Teff , logg and [M/H] and 242 without [M/H]. The parameter coverage of ATLAS-A is from logTeff ∼ 3.5 to 4.7, from MG ∼ −9.5 to 12mag, from [M/H]∼−1.5 to 0.5dex, and from logg ∼ 0 to 5dex. ATLAS-T, which are averaged over fluxes of the directly observed spectra with similar parameters, contains 997 stellar spectral templates with Teff logg and [M/H] and 121 without [M/H]. Here we present the ATLAS-A and ATLAS-T libraries and catalogs for users.

The file includes the 43 GWAC light curves used in this paper, and 4 flare movies.

Please see the following two papers for the details of the theory. 1. R. Xu, D. Liang, and L. Shao, Bumblebee black holes in light of Event Horizon Telescope, arXiv: 2302.05671 2. R. Xu, D. Liang, and L. Shao, Static spherical vacuum solutions in the bumblebee gravity model, Phys. Rev. D 107, 024011 (2023) Units: Geometrized (G=c=1) and the radius of the horizon taken as the unit length First column: raidal coordinate Second column: g_{tt} Third column: g_{rr} Fourth column: b_t Specific parameters of each file: Coupling constant \xi = [0.8045, 1.811, 2.817]8\pi (as the names of the files suggest) Mass of the solutions = [1.000, 1.000, 1.000] Bumblebee charge of the solutions = [1.186, 2.986, 4.447]

The TritonEph2020 data set includes Triton's ephemeris, astrometric data from 1847 to 2020, and their residuals. The ephemeris provides Triton's ephemeris using text format. And all observations are distributed into groups by observatory and type.

This is the source code for the paper: A Deep Learning Based Astronomical Target Detection Framework for Multi-Colour Photometry Sky Survey Projects. The code could be used for detection of celestial objects of different size from multi-colour photometry sky survery data. Also, it could be used for other target detection task. The docker version would be provided under request.

Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) is a quasi-meridian reflecting Schmidt telescope with an effective light-collecting aperture of 3.6 m to 4.9 m (depending on pointing direction) and a 5∘ field of view in diameter (Wang et al. 1996; Su & Cui 2004; Cui et al. 2012), located in Xinglong Station of National Astronomical Observatory, China (NAOC). It has 4000 robotic fibers, with 3.3 arcsec diameter, mounted on its focal plane and connected to 16 spectrographs. Each spectrum of the target is split into two channels, red and blue, and then recorded on red and blue cameras, respectively. The blue channel is optimized for 3700–5900 Å, and the red channel for 5700–9000 Å, with 200 Å overlaps between the two channels. The spectral resolution reaches R∼1800 over the entire wavelength range (see Cui et al. 2012). LAMOST regular survey, carried out from September 2012, was designed to have two major components: the LAMOST ExtraGAlactic Survey (LEGAS) and the LAMOST Experiment for Galactic Understanding and Exploration (LEGUE) survey of Milky Way stellar structure (Zhao et al. 2012). The LAMOST quasar survey was conducted under LEGAS, which covers the high Galactic latitude area in the northern sky. This survey aims to make census of quasars at different redshifts for better understanding the growth of supermassive black holes and the co-evolution with their host galaxies.

Column 1 is the LAMOST IDs of the objects, columns 2-3 are the ID and duplicate observation numbers of stars in the duplicate SP-sample, columns 4-5 are the spectroscopy types classified by the LAMOST 1D pipeline and the LAMOST spectral S/N of g-band, columns 6-10 are the correction factors (k) of each parameter.

This is code and data for the paper "Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms". There is another docker version of this code, which could be obtained from the correpsonding author Peng (robinmartin20@gmail.com).

In this paper, we provide a catalog including 346 CVs of 539 spectra from LAMOST DR9, which contains parameter information of nine columns. Specifically, parameters for the first five columns come from the general catalog of LAMOST DR9, which are the obsid, designation, ra, dec, and snrg extracted from LAMOST Stellar Parameter Pipeline (LASP) respectively. And the last four columns include g magnitude from Gaia, distance, the subtype of cataclysmic variables, and the orbital period.

This dataset provides a catalog containing 185 binaries with independent atmospheric parameters (Teff, logg , [M/H]) and accurate masses and radii as the benchmark of stellar mass and radius. The catalog contains 56 new detached binaries from LAMOST MRS survey in table 3 and 129 detached eclipsing binaries from previous studies in Table 2 (compiled from 2010A&ARv..18...67T,2015ASPC..496..164S,2018MNRAS.479.5491E,2021AJ....161..172D, 2021MNRAS.504.4302W, Pan_2020).

It is the presentation code for the paper "Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers", which is published in the Astronomical Journal. Full datasets and source code could be obtained by email to the corrsponding author Peng Jia (robinmartin20@gmail.com).

Correlating Beijing–Arizona Sky Survey (BASS) data release 3 (DR3) catalogue with the ALLWISE data base, the data from optical and infrared information are obtained. The quasars from Sloan Digital Sky Survey are taken as training and test samples while those from LAMOST are considered as external test sample. We propose two schemes to onstruct the redshift estimation models with XGBoost, CatBoost, and Random Forest. One scheme (namely one-step model) is to predict photometric redshifts directly based on the optimal models created by these three algorithms; the other scheme (namely two-step model) is to first classify the data into low- and high-redshift data sets, and then predict photometric redshifts of these two data sets separately. For one-step model, the performance of these three algorithms on photometric redshift estimation is compared with different training samples, and CatBoost is superior to XGBoost and Random Forest. For two-step model, the performances of these three algorithms on the classification of low and high redshift subsamples are compared, and CatBoost still shows the best performance. Therefore, CatBoost is regarded as the core algorithm of classification and regression in two-step model. In contrast to one-step model, two-step model is optimal when predicting photometric redshift of quasars, especially for high-redshift quasars. Finally, the two models are applied to predict photometric redshifts of all quasar candidates of BASS DR3. The number of high-redshift quasar candidates is 3938 (redshift ≥3.5) and 121 (redshift ≥4.5) by two-step model. The predicted result will be helpful for quasar research and follow-up observation of high-redshift quasars.

This catalogue contains 1,160,568,989 galaxies from DESI DR9 and their photometric redshift by EAZY and CatBoost.

we have identified six samples of candidates with G 10°. By cross-matching with results derived from medium-resolution (R ~ 1800) from LAMOST DR8, we establish that the success rate for identifying VMP stars is 60.1 % for the Gold sample, 39.2 % for the Silver GW sample, 41.3 % for the Silver GK sample, 15.4 % for the Bronze G sample, 31.7 % for the Bronze WK sample, and 16.6 % for the Low b sample, respectively. An additional strict cut on the quality parameter RUWE < 1.1 can further increase the success rate of the Silver GW, Silver GK, and Bronze G samples to 46.9 %, 51.6 %, and 29.3 %, respectively.

The light pollution has had a great impact on star gazing and astronomy observations. Recently, a new crisis has come - Starlink satellites. This tour/video demonstrates the simulation of the disturbance of Starlink on observations, aiming to raise more awareness of protecting the dark and quiet night sky, so that we can savor the beauty of the starry night.

We identify 20 F-type Herbig stars and provide a list of 22 pre-main-sequence candidates from LAMOST DR8. The effective temperature, distance, extinction, stellar luminosity, mass, and radius are derived for each Herbig star based on optical spectra, photometry, Gaia EDR3 parallaxes, and pre-main-sequence evolutionary tracks. According to spectral energy distributions, 19 F-type Herbig stars belong to Class II YSOs, and one belongs to the flat-spectrum class. Four have Spitzer IRS spectra, of which three show extremely weak polycyclic aromatic hydrocarbons emissions, and three with both amorphous and crystalline silicate emissions share the similar parameters and are at the same evolutionary stage. We detect a solar-nearby outbursting EXor Herbig star J034344.48+314309.3, possible precursor of a Herbig Ae star. Intense emission lines of HI, HeI, OI, NaI, and CaII originated from the rapid accretion during the outbursts are detected in its optical spectra, and silicate emission features are detected in its infrared spectrum. We also make a statistic analysis on the disk properties of all known Herbig stars using the defined infrared spectral indices (αJ−Ks and αKs−W3). The proportion of Herbig stars with moderate infrared excesses decreases as effective temperature increases. The majority of the precursors (F-, G-, or K- type) have moderate infrared excesses. Hotter Herbig stars tend to have a larger proportion with large infrared excesses. The trends may be due to the fact that hotter stars have larger areas of re-emitting dust, although there is some scatter due to the particularities of each disk. 'F-type Herbig stars.csv' goes with Table 1 in the paper. 'Parameters of F-type Herbig stars.csv' goes with Table 2 in the paper. '22 PMS candidates.csv' goes with Table A in the paper.

Here provides the downloading service for the quasar candidates catalog of DESI-LIS that were introduced in He & Li 2022 (https://arxiv.org/abs/2207.06792). The main catalogs include a catalog of 1,953,932 Grade-A quasar candidates and another catalog of 22,486, 884 Grade-B quasar candidates. The other two affiliated catalogs separately include the quasar candidates that exist in DECaLS and MzLS+BASS footprints.

Rows from top to bottom are the results for filters $grizy$. The upper panel of the left column shows the radial profile of the surface brightness (open circles with error bars), PSF component (blue), S\'ersic\ (red) component, and total model (purple; PSF $+$ S\'ersic). The $\chi_{\nu}^2$ from \texttt{GALFITM} for each band is shown in the lower-left corner, while that for all five bands is shown in the upper-right corner of the first panel in the bottom row. The lower sub-panel gives the residuals between the data and the model (data$-$model). The images show, from left to right, the original data, best-fit total model, data with the nucleus component modeled by a PSF subtracted, and residuals of the total model. The best-fit S\'ersic index, half-light radius ($R_e$), and host fraction $f_{\rm host} \equiv $ {S\'ersic / (PSF $+$ S\'ersic)} are shown in the third column.

The figure sets present astrometric and photometric data of 886 nearby clusters (Distance < 1.25 kpc), including 270 newly identified Gaia star cluster candidates (ID: CWNU 1001-1270). For each figure, left to right: spatial distribution, parallax histogram, proper motion distribution, CMD with best-fitting isochrone line. The color bar represents the number of responses in the clustering, N_Re, of each cluster member.

734 late-type M dwarfs identified visually from LAMOST DR7 are presented in Table 1. The atmospheric parameters(Teff, Logg, and M/H), radial velocity and signal to noise in SDSS i and z band, and radial velocity from LAMOST DR7 are presented in Table 2. We also listed the flag of the lithium strength in Table 2. In Table 3, we list the kinematic parameters calculated for 571 objects of our ultracool dwarf sample with radial velocity from LAMOST DR7, and photometry and astrometric from Gaia EDR3.

This is a catalog of 11,003 sources with distinct DIB features. The catalog presents information of sources including the star designation, ra, dec, and the RVs, EWs, and FWHMs of the three DIB features, as well as their errors.The “flag” represents the DIB is measured through the spectrum of an early-type star or FGK-type star, 1= ”early-type”, while 0=”FGK-type”.

The LAMOST-RRNet catalog contains stellar atmospheric parameters and elemental abundances for 2,377,510 medium-resolution spectra from LAMOST DR7. This catalog is estimated by Residual Recurrent Neural Network (RRNet). This catalog contains the following information: the identifier for the observation spectrum (obsid), the fits file name corresponding to the observation spectrum (filename), coordinate information (ra, dec), the extension name of the spectrum (extname_blue, extname_red), the S/N of the spectrum (snr_blue, snr_red), effective temperature (Teff[K]), surface gravity (Logg), 15 elemental abundances (XH), 1σ uncertainty of parameters (X_err) and recommended flags (flag).

This is a complied version of digital twin of atmospheric turbulence phase screens based on deep neural networks. It can only generate phase screens with 128*128 pixels. If you need some phase screen with specified properties, feel free to contact the corresponding author: Peng Jia from Taiyuan University of Techonology through his email: robinmartin20@gmail.com or robinmartin@126.com.

We analyze 152 large confined flares (GOES class ≥M1.0 and ≤45◦ from disk center) during 2010−2019, and classify them into two types based on their different dynamic properties. “Type I” flares are atypical confined flares above the filaments, which are characterized by slipping motions of flare loops and ribbons and not associated with any eruption of a filament/flux rope. “Type II” flares are typical confined flares associated with the failed eruptions of the filaments/flux ropes, which can be explained by the classical 2D flare model. A total of 59 flares are “Type I” flares (about 40%) and 93 events are “Type II” flares (about 60%). There are significant differences in distributions of the total unsigned magnetic flux (ΦAR) of active regions (ARs) producing the two types of confined flares, with “Type I” confined flares from ARs with a larger ΦAR than “Type II”. We calculate the mean shear angle ΨHFED within the core of an AR prior to the flare onset, and find that it is slightly smaller for “Type I” flares than that for “Type II” events. The relative non-potentiality parameter ΨHFED/ΦAR has the best performance in distinguishing the two types of flares. About 73% of “Type I” confined flares have ΨHFED/ΦAR

Precise correction of dust reddening is fundamental to obtain the intrinsic parameters of celestial objects. The Schlegel et al. (SFD) and the Planck 2D extinction maps are widely used for the reddening correction. In this work, using accurate reddening determinations of about two million stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) data release 5 (DR5) and Gaia DR2, we check and calibrate the SFD and Planck maps in the middle and high Galactic latitudes. The maps show similar precision in reddening correction. We find small yet significant spatially dependent biases for the four maps, which are similar between the SFD and Planck2014-R maps, and between the Planck2014-Tau and Planck2019-Tau maps. The biases show a clear dependence on the dust temperature and extinction for the SFD and Planck2014-R maps. While those of the Planck2014-Tau and Planck2019-Tau maps have a weak dependence on the dust temperature, they both strongly depend on the dust spectral index. Finally, we present corrections of the SFD and Planck extinction maps within the LAMOST footprint, along with empirical relations for corrections outside the LAMOST footprint. Our results provide important clues for the further improvement of the Galactic all-sky extinction maps and lay an significant foundation for the accurate extinction correction in the era of precision astronomy.

In this paper, we report on 606 S-type stars identified from Data Release 9 of the LAMOST medium-resolution spectroscopic (MRS) survey, and 539 of them are reported for the first time. The discovery of these stars is a three-step process, i.e., selecting with the ZrO band indices greater than 0.25, excluding non-S-type stars with the iterative Support Vector Machine method, and finally retaining stars with absolute bolometric magnitude larger than -7.1. The 606 stars are consistent with the distribution of known S-type stars in the color-magnitude diagram. We estimated the C/Os using the [C/Fe] and [O/Fe] provided by APOGEE and the MARCS model for S-type stars, respectively, and the results of the two methods show that C/Os of all stars are larger than 0.5. Both the locations on the color-magnitude diagram and C/Os further verify the nature of our S-type sample. Investigating the effect of TiO and atmospheric parameters on ZrO with the sample, we found that log g has a more significant impact on ZrO than Teff and [Fe/H], and both TiO and log g may negatively correlate with ZrO. According to the criterion of Tian et al. (2020), a total of 238 binary candidates were found by the zero-point-calibrated radial velocities from the officially released catalog of LAMOST MRS and the catalog of Zhang et al. (2021). A catalog of these 606 S-type stars is available from the following link https://doi.org/10.12149/101097.

Quasars behind the Galactic plane (GPQs) are important astrometric references and valuable probes of the Galactic gas, yet the search of GPQs is difficult due to severe extinction and source crowding in the Galactic plane. In this paper, we present a sample of 204 spectroscopically confirmed GPQs at |b|Description of gpq2_204_GPQ_spec.tar.gz: Name: Spectra of the 204 identified Quasars behind the Galactic PlaneShort name: GPQ SpectraThis tar package includes reduced one-dimensional spectra of the 204 identified GPQs in FITS format. The FITS files are named in the format of "spec_Jhhmmss.ss+ddmmss.s_Telescope.fits". The FITS files can be viewed with the splot task of IRAF or read with other standard FITS I/O routines (e.g. astropy.io.fits, specutils).Description of gpq2_contam_spec.tar.gz: Name: Spectra of the 39 contaminantsShort name: Contam. SpectraThis tar package includes reduced one-dimensional spectra of the 39 contaminants in FITS format. The FITS files are named in the format of "Bspec_Jhhmmss.ss+ddmmss.s_Telescope.fits". The FITS files can be viewed with the splot task of IRAF or read with other standard FITS I/O routines (e.g. astropy.io.fits, specutils).Description of gpq2_main_cat_tableA1.fits: Name: Main spectral catalog of the 204 identified GPQsShort name: GPQ Main Cat.The unmeasurable values are indicated with -999. Description of gpq2_ext_cat_tableA2.fits: Name: Extended spectral catalog of the 204 identified GPQsShort name: GPQ Ext. Cat.The unmeasurable values are indicated with -999.

We present an empirical stellar spectra library created using spectra from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR5. This library represents a uniform data set ranging from 3750 through 8500K in effective temperature (Teff), from −2.5 through +1.0 dex in metallicity ([Fe/H]), and from 0 to 5.0 dex in gravity (log g). The spectra in the library have resolutions R∼1800, with well-calibrated fluxes and rest-framed wavelengths. Using a large number of red stars observed by LAMOST, we generated denser K-type templates to fill in data missing from current empirical spectral libraries, particularly the late K type. For K giants, we calibrated the spectroscopic surface gravities against the asteroseismic surface gravities. To verify the reliability of the parameters labeled for this library, we performed an internal cross-validation using a χ2 minimization method to interpolate the parameters of each individual spectrum using the remaining spectra in the library. We obtained precisions of 41 K, 0.11 dex, and 0.05 dex for Teff, log g, and [Fe/H], respectively, which means the templates are labeled with correct stellar parameters. Through external comparisons, we confirmed that measurements of the stellar parameters through this library can achieve accuracies of approximately 125 K in Teff, 0.1 dex in [Fe/H] and 0.20 dex in log g without systematic offset. This empirical library is useful for stellar parameter measurements because it has large parameter coverage and full wavelength coverage from 3800 to 8900 Å.

In the eighth data release (DR8) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, more than 318,740 low-resolution stellar spectra with types from B to early F and signal-to-noise ratios >50 were released. With this large volume of the early-type stars, we tried machine-learning algorithms to search for class-one and class-two chemical peculiars (CP1 and CP2), and to detect spectral features to distinguish the two classes in low-resolution spectra. We selected the XGBoost algorithm after comparing the classification efficiency of three machine-learning ensemble algorithms. Using XGBoost followed by the visual investigation, we presented a catalog of 20,694 sources, including 17,986 CP1 and 2708 CP2, in which 6917 CP1 and 1652 CP2 are newly discovered. We also list the spectral features to separate CP1 from CP2 discovered through XGBoost. The stellar parameters (including effective temperature (T eff), surface gravity (log g), metallicity [Fe/H]), the spatial distribution in Galactic coordinates, and the color magnitude were provided for all of the entries of the catalog. The T eff for CP1 distributes from ~6000 to ~8500 K, while for CP2 it distributes from ~7000 to ~13,700 K. The log g of CP1 ranges from 2.8 to 4.8 dex, peaking at 4.5 dex, and of CP2 it ranges from 2.0 to 5.0 dex, peaking at 3.6 dex, respectively. The [Fe/H] of CP1 and CP2 are from -1.4 to 0.4 dex, and the [Fe/H] of CP1 are on average higher than that of CP2. Almost all of the targets in our sample locate around the Galactic plane.

Photometric redshift (photo-z) is a fundamental parameter for multi-wavelength photometric surveys, while galaxy clusters are important cosmological probers and ideal ob- jects for exploring the dense environmental impact on galaxy evolution. We extend our pre- vious work on estimating photo-z and detecting galaxy clusters to the latest data releases of the Dark Energy Spectroscopic Instrument (DESI) imaging surveys, Dark Energy Survey (DES), and Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) imaging surveys and make corresponding catalogs publicly available for more extensive scientific applications. The photo-z catalogs include accurate measurements of photo-z and stellar mass for about 320, 293, and 134 million galaxies with r The catalogs including (A) Photo-z and stellar mass desdr2_galaxy_cspcat: total catalog of photo-z and stellar mass for DES DR2 photoz_desdr2: dividing the catalog into smaller files filename: desdr2_galaxy_cspcat_raXXX_YYY.fits, where XXX and YYY is the lower and upper limits of R.A in degrees desidr9_galaxy_cspcat.fits: total catalog of photo-z and stellar mass for DESI DR9 photoz_desidr9: dividing the catalog into smaller files filename: desidr9_galaxy_cspcat_raXXX_YYY.fits, where XXX and YYY is the lower and upper limits of R.A in degrees hscpdr3_wide_galaxy_cspcat.fits: total catalog of photo-z and stellar mass for HSC-SSP PDR3 photoz_hscpdr3: dividing the catalog into smaller files filename: hscpdr3_wide_galaxy_cspcat_raXXX_YYY.fits, where XXX and YYY is the lower and upper limits of R.A in degrees (B) Galaxy clusters galaxy_clusters_desdr2.fits: catalog of galaxy clusters for DES DR2. galaxy_clusters_desidr9.fits: catalog of galaxy clusters for DESI DR9. galaxy_clusters_hscpdr3_wide.fits: catalog of galaxy clusters for HSC-SSP PDR3.

Accurate determinations of stellar parameters and distances for large complete samples of stars are keys for conducting detailed studies of the formation and evolution of our Galaxy. Here we present stellar atmospheric parameters (effective temperature, luminosity classifications, and metallicity) estimates for some 24 million stars determined from the stellar colors of SMSS DR2 and Gaia EDR3, based on training data sets with available spectroscopic measurements from previous high/medium/low-resolution spectroscopic surveys. The number of stars with photometric-metallicity estimates is 4-5 times larger than that collected by the current largest spectroscopic survey to date-LAMOST-over the course of the past decade. External checks indicate that the precision of the photometric-metallicity estimates are quite high, comparable to or slightly better than that derived from spectroscopy, with typical values around 0.05-0.15 dex for both dwarf and giant stars with [Fe/H] > -2.01.0, 0.10-0.20 dex for giant stars with -2.0 < [Fe/H] ≤ -1.0, and 0.20-0.25 dex for giant stars with [Fe/H] ≤ -2.0, and include estimates for stars as metal-poor as [Fe/H] ~ -3.5, substantially lower than previous photometric techniques. Photometric-metallicity estimates are obtained for an unprecedented number of metal-poor stars, including a total of over three million metal-poor (MP; [Fe/H] ≤ -1.0) stars, over half a million very metal-poor (VMP; [Fe/H] ≤ -2.0) stars, and over 25,000 extremely metal-poor (EMP; [Fe/H] ≤ -3.0) stars. Moreover, distances are determined for over 20 million stars in our sample. For the over 18 million sample stars with accurate Gaia parallaxes, stellar ages are estimated by comparing with theoretical isochrones. Astrometric information is provided for the stars in our catalog, along with radial velocities for ~10% of our sample stars, taken from completed/ongoing large-scale spectroscopic surveys.

With the aim of investigating how the magnetic field in solar active regions (ARs) controls flare activity, i.e., whether a confined or eruptive flare occurs, we analyze 106 flares of Geostationary Operational Environmental Satellite class ≥M1.0 during 2010-2019. We calculate mean characteristic twist parameters α FPIL within the "flaring polarity inversion line" region and α HFED within the area of high photospheric magnetic free energy density, which both provide measures of the nonpotentiality of the AR core region. Magnetic twist is thought to be related to the driving force of electric current-driven instabilities, such as the helical kink instability. We also calculate total unsigned magnetic flux (ΦAR) of ARs producing the flare, which describes the strength of the background field confinement. By considering both the constraining effect of background magnetic fields and the magnetic nonpotentiality of ARs, we propose a new parameter α/ΦAR to measure the probability for a large flare to be associated with a coronal mass ejection (CME). We find that in about 90% of eruptive flares, α FPIL/ΦAR and α HFED/ΦAR are beyond critical values (2.2 × 10-24 and 3.2 × 10-24 Mm-1 Mx-1), whereas they are less than critical values in ~80% of confined flares. This indicates that the new parameter α/ΦAR is well able to distinguish eruptive flares from confined flares. Our investigation suggests that the relative measure of magnetic nonpotentiality within the AR core over the restriction of the background field largely controls the capability of ARs to produce eruptive flares.

We derive a catalog of early-type emission-line stars including 30,023 spectra of 25,867 stars from LAMOST Data Release 7, in which 4189 have Simbad records. The spectra are classified into three morphological types (10 subtypes) based on Hα emission-line profiles. Some spectra contaminated by nebula emission lines such as from H II regions are flagged in the catalog. We also provide a specific catalog of 20 stars with stellar winds or accretion flows by calculating the terminal and peak velocities based on P-Cygni or inverse P-Cygni profiles. More important, with two color–color diagrams, (H-Ks, J-H) and (Ks-W1, H-Ks), of a collection of known Herbig Ae/Be stars (HAeBes) and classical Ae/Be stars (CAeBes), we propose an updated criterion to separate HAeBes from CAeBes. By the criterion, we select 118 HAeBe candidates and 2636 CAeBe candidates from the sample. We confirm 71 of the 118 HAeBes based on the data from LAMOST (optical spectra) and WISE (photometry and images), 62 of which are newly identified. The 71 identified HAeBes are compiled into a specific catalog, and their SEDs and MIR images are also presented. The distances of 76% confirmed HAeBes are further than 1 kpc, which enlarges the number of known HAeBes in further distance. Most of the 71 HAeBes are located in the Galactic disk while 6 of them have |Zgal| > 400 pc. Four HAeBes show the forbidden emission lines of [Fe II] and [O I], in which J051425.20+411310.7 is a newly discovered B[e]. In addition, four HAeBes having Spitzer IRS spectra all show PAH features.

Green Pea and Blueberry galaxies are well-known for their compact size, low mass, strong emission lines and analogs to high-z Ly{\alpha} emitting galaxies. In this study, 1547 strong [OIII]{\lambda}5007 emission line compact galaxies with 1694 spectra are selected from LAMOST DR9 at the redshift range from 0.0 to 0.59. According to the redshift distribution, these samples can be separated into three groups: Blueberries, Green Peas and Purple Grapes. Optical [MgII]{\lambda}2800 line feature, BPT diagram, multi-wavelength SED fitting, MIR color, and MIR variability are deployed to identify 23 AGN candidates from these samples, which are excluded for the following SFR discussions. We perform the multi-wavelength SED fitting with GALEX UV and WISE MIR data. Color excess from Balmer decrement shows these strong [OIII]{\lambda}5007 emission line compact galaxies are not highly reddened. The stellar mass of the galaxies is obtained by fitting LAMOST calibrated spectra with the emission lines masked. We find that the SFR is increasing with the increase of redshift, while for the sources within the same redshift bin, the SFR increases with mass with a similar slope as the SFMS. These samples have a median metallicity of 12+log(O/H) of 8.10. The metallicity increases with mass, and all the sources are below the mass-metallicity relation. The direct-derived Te-based metallicity from the [OIII]{\lambda}4363 line agrees with the empirical N2-based empirical gas-phase metallicity. Moreover, these compact strong [OIII]{\lambda}5007 are mostly in a less dense environment.

We combine LAMOST DR7 spectroscopic data and Gaia EDR3 photometric data to construct high-quality giant (0.7 < (BP - RP) < 1.4) and dwarf (0.5 < (BP - RP) < 1.5) samples in the high Galactic latitude region, with precise corrections for magnitude-dependent systematic errors in the Gaia photometry and careful reddening corrections using empirically determined color- and reddening-dependent coefficients. We use the two samples to build metallicity-dependent stellar loci of Gaia colors for giants and dwarfs, respectively. For a given (BP - RP) color, a 1 dex change in [Fe/H] results in about a 5 mmag change in (BP - G) color for solar-type stars. These relations are used to determine metallicity estimates from EDR3 colors. Despite the weak sensitivity, the exquisite data quality of these colors enables a typical precision of about δ [Fe/H] = 0.2 dex. Our method is valid for FGK stars with G ≤ 16, [Fe/H] ≥ -2.5, and E(B - V) ≤ 0.5. Stars with fainter G magnitudes, lower metallicities, or larger reddening suffer from higher metallicity uncertainties. With the enormous data volume of Gaia, we have measured metallicity estimates for about 27 million stars with 10 < G ≤ 16 across almost the entire sky, including over 6 million giants and 20 million dwarfs, which can be used for a number of studies. These include investigations of Galactic formation and evolution, the identification of candidate stars for subsequent high-resolution spectroscopic follow-up, the identification of wide binaries, and to obtain metallicity estimates of stars for asteroseismology and exoplanet research.

Deep learning techniques have been well explored in the transiting exoplanet field; however, previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well-proven object detection framework in the computer vision field. Through training the network on the light curves of the confirmed Kepler exoplanets, our model yields about 90% precision and recall for identifying transits with signal-to-noise ratio higher than 6 (set the confidence threshold to 0.6). Giving a slightly lower confidence threshold, recall can reach higher than 95%. We also transfer the trained model to the TESS data and obtain similar performance. The results of our algorithm match the intuition of the human visual perception and make it useful to find single-transiting candidates. Moreover, the parameters of the output bounding boxes can also help to find multiplanet systems. Our network and detection functions are implemented in the Deep-Transit toolkit, which is an open-source Python package hosted on Github and PyPI.

Context. New observations and new planetary and satellite ephemerides provide opportunities to improve the ephemerides for Triton and Nereid as well as relevant parameters. In particular, the observations include a lot of new accurate Earth-based positions reduced with Gaia astrometic catalogs and accurate positions obtained from Hubble Space Telescope. Aims: We aim to reliably improve the ephemerides for Triton and Nereid along with some parameters by using all the available astrometric data from 1847 to 2020 and by updating the dynamical model. We also aim to improve the geometrical descriptions based on the improved orbits of the two satellites and the pole orientation of Neptune. Methods: The orbits of Triton and Nereid are determined by fitting dynamical and observational model parameters to observations in a weighted least-squares sense. The dynamical model makes use of the new ephemerides from Jet Propulsion Laboratory for planets, DE440, and those for the inner satellites of Neptune, NEP090. For completeness, in addition to the gravitational effects considered by NEP081, the model also includes perturbations from inner satellites and a revised model for the motion of the pole orientation of Neptune. Moreover, model simplifications are investigated to speed up the motion equation integration. Since the pole orientation angles of Neptune at epoch are possibly improvable according to the preliminary post-fit sensitivity analysis, these angles are adjusted together with the satellite state vectors at epoch. Linear mapping of the covariance matrix is a measure of formal uncertainties of our orbit and pole solutions. However, to obtain more reliable accuracy estimations, it is necessary to consider the uncertainties in the observations and the unadjusted model parameters. To accomplish this, a method (BR-RS) that performs bootstrap resampling of observations (BR) and random sampling of unadjusted model parameters (RS) is used. Analytical representations are fitted to the orbit and pole solutions to provide their geometric descriptions. Results: The model we use can be fitted to the observations with their estimated accuracies. The new ephemerides, FORCES-8-MAIN-2020, covering years 1600-2650 are available online in SPICE format. The orbits are well determined with the orbital uncertainties expected to be within 200 km (about 10 mas as seen from the Earth) for Triton and 1000 km (50 mas) for Nereid for the next 100 yr as estimated by the BR-RS method. In particular, the correction in the Nereid mean orbit motion from the NEP081 solution is +4.''9 yr−1, and has a BR-RS uncertainty of 0.''24 yr−1. In the fitting process, we also determine the pole orientation of Neptune. At the initial epoch 1989 September 1 TDB, the right ascension and declination of the new pole orientation referred to the International Celestial Reference System are αp = 299.°339 ± 0.°012 (formal)∕ ± 0.°014 (BR-RS) and δp = 42.°985 ± 0.°016 (formal)∕ ± 0.°045 (BR-RS), respectively. From 1800 to 2200, the motion of the pole orientation is well constrained with a BR-RS uncertainty of about 0.°01-0.°05. We also provide geometrical descriptions for the new orbits and pole orientation.

We cross-matched 1.3 million white dwarf (WD) candidates from Gaia EDR3 with spectral data from LAMOST DR7 within 3″. Applying machine learning described in our previous work, we spectroscopically identified 6190 WD objects after visual inspection. 32 detailed classes were adopted for them, including but not limited to DAB and DB+M. We estimated the atmospheric parameters for the DA and DB type WD using Levenberg-Marquardt least-squares algorithm. Finally, a catalog of WD spectra from LAMOST is provided online.

The Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST)-K2 (LK2) project, initiated in 2015, aims to collect low-resolution spectra of targets in the K2 campaigns, similar to the LAMOST-Kepler project. By the end of 2018, a total of 126 LK2 plates had been observed by LAMOST. After cross-matching the catalog of the LAMOST data release 6 (DR6) with that of the K2 approved targets, we found 160,619 usable spectra of 84,012 objects, most of which had been observed more than once. The effective temperature, surface gravity, metallicity, and radial velocity from 129,974 spectra for 70,895 objects are derived through the LAMOST Stellar Parameter Pipeline (LASP). The internal uncertainties were estimated to be 81 K, 0.15 dex, 0.09 dex, and 5 km s-1, respectively, when derived from a spectrum with a signal-to-noise ratio in the g band (S/Ng) of 10. These estimates are based on results for targets with multiple visits. The external accuracies were assessed by comparing the parameters of targets in common with the APOGEE and Gaia surveys, for which we generally found linear relationships. A final calibration is provided, combining external and internal uncertainties for giants and dwarfs, separately. We foresee that these spectroscopic data will be used widely in different research fields, especially in combination with K2 photometry.

The M dwarf stars are the most common stars in the Galaxy, dominating the population of the Galaxy at faint magnitudes. Precise and accurate stellar parameters for M dwarfs are of crucial importance for many studies. However, the atmospheric parameters of M dwarf stars are difficult to determine. In this paper, we present a catalog of the spectroscopic stellar parameters (Teff and [M/H]) of ∼300,000 M dwarf stars observed by both LAMOST and Gaia using the Stellar LAbel Machine (SLAM). We train a SLAM model using LAMOST spectra with APOGEE Data Release 16 labels with 2800 K < Teff < 4500K and -2 dex < [M/H] < 0.5 dex. The SLAM Teff is in agreement to within ∼50 K compared to the previous study determined by APOGEE observations, and the SLAM [M/H] agrees within 0.12 dex compared to the APOGEE observation. We also set up a SLAM model trained by the BT-Settl atmospheric model with random uncertainties (in cross validation) to 60 K and agreeing within ∼90 K compared to previous studies.

This database includes a total of 719 flares (251 eruptive and 468 confined) of Geostationary Operational Environmental Satellite (GOES) class C5.0 and larger that occurred within 45 degree from the central meridian, from June 2010 until June 2019.

The Beijing-Arizona Sky Survey (BASS) Data Release 3 (DR3) catalogue was released in 2019, which contains the data from all BASS and the Mosaic z-band Legacy Survey (MzLS) observations during 2015 January and 2019 March, about 200 million sources. We cross-match BASS DR3 with spectral databases from the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) to obtain the spectroscopic classes of known samples. Then, the samples are cross-matched with ALLWISE database. Based on optical and infrared information of the samples, we use the XGBoost algorithm to construct different classifiers, including binary classification and multiclass classification. The accuracy of these classifiers with the best input pattern is larger than 90.0 per cent. Finally, all selected sources in the BASS DR3 catalogue are classified by these classifiers. The classification label and probabilities for individual sources are assigned by different classifiers. When the predicted results by binary classification are the same as multiclass classification with optical and infrared information, the number of star, galaxy and quasar candidates is separately 12 375 838 (P_S>0.95), 18 606 073 (P_G>0.95) and 798 928 (P_Q>0.95). For these sources without infrared information, the predicted results can be as a reference. Those candidates may be taken as input catalogue of LAMOST, DESI or other projects for follow up observation. The classified result will be of great help and reference for future research of the BASS DR3 sources.

Context. The method of solar magnetic field calibration for the filter-based magnetograph is normally the linear calibration method under weak-field approximation that cannot generate the strong magnetic field region well due to the magnetic saturation effect. Aims: We try to provide a new method to carry out the nonlinear magnetic calibration with the help of neural networks to obtain more accurate magnetic fields. Methods: We employed the data from Hinode/SP to construct a training, validation and test dataset. The narrow-band Stokes I, Q, U, and V maps at one wavelength point were selected from all the 112 wavelength points observed by SP so as to simulate the single-wavelength observations of the filter-based magnetograph. We used the residual network to model the nonlinear relationship between the Stokes maps and the vector magnetic fields. Results: After an extensive performance analysis, it is found that the trained models could infer the longitudinal magnetic flux density, the transverse magnetic flux density, and the azimuth angle from the narrow-band Stokes maps with a precision comparable to the inversion results using 112 wavelength points. Moreover, the maps that were produced are much cleaner than the inversion results. The method can effectively overcome the magnetic saturation effect and infer the strong magnetic region much better than the linear calibration method. The residual errors of test samples to standard data are mostly about 50 G for both the longitudinal and transverse magnetic flux density. The values are about 100 G with our previous method of multilayer perceptron, indicating that the new method is more accurate in magnetic calibration.

To study the quality of stellar spectra of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and the correctness of the corresponding stellar parameters derived by the LASP (LAMOST Stellar Parameter Pipeline), the outlier analysis method is applied to the archived AFGK stars in the fifth data release (DR5) of LAMOST. The outlier factor is defined in order to sort more than 3 million stellar spectra selected from the DR5 Stellar Parameter catalog. We propose an improved Local Outlier Factor (LOF) method based on Principal Component Analysis and Monte Carlo to enable the computation of the LOF rankings for randomly picked sub-samples that are computed in parallel by multiple computers, and finally to obtain the outlier ranking of each spectrum in the entire dataset. Totally 3,627 most outlier ranked spectra, around one-thousandth of all spectra, are selected and clustered into 10 groups, and the parameter density distribution of them conforms to the parameter distribution of LAMOST DR5, which suggests that in the whole parameter space the probability of bad spectra is uniformly distributed. By cross-matching the 3,627 spectra with APOGEE, we obtain 122 common ones. The published parameters calculated from LASP agree with APOGEE for the 122 spectra although there are bad pixels or bad flux calibrations in them. On the other hand, some outlier spectra show strong nebular contamination warning the corresponding parameters should be carefully used. A catalog and a spectral atlas of all the 3,627 outliers can be found at the link http://paperdata.china-vo.org/LY_paper/dr5Outlier/dr5Outlier_resource.zip.

We present a three-dimensional extinction map in the r band. The map has a spatial angular resolution, depending on latitude, between 3 and 9 arcmin and covers the entire Xuyi Schmidt Telescope Photometric Survey of the Galactic Anticentre (XSTPS-GAC) survey area of over 6000 deg2 for Galactic longitude 140 < l < 240 deg and latitude -60 < b < 40 deg. By cross-matching the photometric catalogue of the XSTPS-GAC with those of 2MASS and WISE, we have built a multiband photometric stellar sample of about 30 million stars and applied spectral energy distribution (SED) fitting to the sample. By combining photometric data from the optical to the near-infrared, we are able to break the degeneracy between the intrinsic stellar colours and the amounts of extinction by dust grains for stars with high photometric accuracy, and trace the extinction as a function of distance for low Galactic latitude and thus highly extincted regions. This has allowed us to derive the best-fitting extinction and distance information of more than 13 million stars, which are used to construct the three-dimensional extinction map. We have also applied a Rayleigh-Jeans colour excess (RJCE) method to the data using the 2MASS and WISE colour (H - W2). The resulting RJCE extinction map is consistent with the integrated two-dimensional map deduced using the best-fitting SED algorithm. However for individual stars, the amounts of extinction yielded by the RJCE method suffer from larger errors than those given by the best-fitting SED algorithm.

The point spread function (PSF) reflects states of a telescope and plays an important role in the development of data-processing methods, such as PSF-based astrometry, photometry, and image restoration. However, for wide field small aperture telescopes (WFSATs), estimating PSF in any position of the whole field of view is hard, because aberrations induced by the optical system are quite complex and the signal-to-noise ratio of star images is often too low for PSF estimation. In this paper, we further develop our deep neural network (DNN)-based PSF modelling method and show its applications in PSF estimation. During the telescope alignment and testing stage, our method collects system calibration data through modification of optical elements within engineering tolerances (tilting and decentring). Then, we use these data to train a DNN (Tel-Net). After training, the Tel-Net can estimate PSF in any field of view from several discretely sampled star images. We use both simulated and experimental data to test performance of our method. The results show that the Tel-Net can successfully reconstruct PSFs of WFSATs of any states and in any positions of the field of view (FoV). Its results are significantly more precise than results obtained by the compared classic method - inverse distance weight interpolation. Our method provides foundations for developing deep neural network-based data-processing methods for WFSATs, which require strong prior information of PSFs.

The ESA's X-ray Multi-mirror Mission (XMM-Newton) created a new high-quality version of the XMM-Newton serendipitous source catalogue, 4XMM-DR9, which provides a wealth of information for observed sources. The 4XMM-DR9 catalogue is correlated with the Sloan Digital Sky Survey (SDSS) DR12 photometric data base and the AllWISE data base; we then get X-ray sources with information from the X-ray, optical, and/or infrared bands and obtain the XMM-WISE, XMM-SDSS, and XMM-WISE-SDSS samples. Based on the large spectroscopic surveys of SDSS and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), we cross-match the XMM-WISE-SDSS sample with sources of known spectral classes, and obtain known samples of stars, galaxies, and quasars. The distribution of stars, galaxies, and quasars as well as all spectral classes of stars in 2D parameter space is presented. Various machine-learning methods are applied to different samples from different bands. The better classified results are retained. For the sample from the X-ray band, a rotation-forest classifier performs the best. For the sample from the X-ray and infrared bands, a random-forest algorithm outperforms all other methods. For the samples from the X-ray, optical, and/or infrared bands, the LogitBoost classifier shows its superiority. Thus, all X-ray sources in the 4XMM-DR9 catalogue with different input patterns are classified by their respective models that are created by these best methods. Their membership of and membership probabilities for individual X-ray sources are assigned. The classified result will be of great value for the further research of X-ray sources in greater detail.

RS Ser, V449 Per, MR Del, V593 Cen, and V1095 Her are short-period eclipsing binaries. We made photometric observations on 38 nights using four 1-m-class telescopes and plotted eight light curves. We determined the spectral type of V449 Per as K0(±2)V using low-resolution spectra from the Lijiang 2.4-m telescope. We found cyclic variation in the orbital periods for RS Ser and V1095 Her, and confirmed the cyclic variation of MR Del. The periods of the hypothetical third bodies are close to the duration of observation, and the detected cycles are questionable. For V593 Cen, we followed the previously published suggestion that it probably has a black hole with a minimum mass of 3.68 M⊙. We used the orbital period decreasing at a rate of 8.2(0.1) × 10-8 d yr-1 to explain it. There are two alternative interpretations, and hence the black hole candidate of V593 Cen remains questionable because the minimum points are concentrated in four clusters. The period of V449 Per increases continuously at a rate of 9.5 × 10-8 d yr-1, which can be attributed to mass transfer from the less massive component to the more massive component. For MR Del, we used a new light curve and the published radial velocity to revise its absolute parameters. Furthermore, we revised the photometric solution of V593 Cen and confirmed it as an early-type contact binary with a higher contact factor. We obtained preliminary photometric parameters for RS Ser, V1095 Her, and V449 Per.

Quasars behind the Galactic plane (GPQs) are important astrometric references and useful probes of Milky Way gas. However, the search for GPQs is difficult due to large extinctions and high source densities in the Galactic plane. Existing selection methods for quasars developed using high Galactic latitude (high-b) data cannot be applied to the Galactic plane directly because the photometric data obtained from high-b regions and the Galactic plane follow different probability distributions. To alleviate this data set shift problem for quasar candidate selection, we adopt a transfer-learning framework at both the data and algorithm levels. At the data level, to make a training set in which a data set shift is modeled, we synthesize quasars and galaxies behind the Galactic plane based on SDSS sources and the Galactic dust map. At the algorithm level, to reduce the effect of class imbalance, we transform the three-class classification problem for stars, galaxies, and quasars into two binary classification tasks. We apply the XGBoost algorithm to Pan-STARRS1 (PS1) and AllWISE photometry for classification and an additional cut on Gaia proper motion to remove stellar contaminants. We obtain a reliable GPQ candidate catalog with 160,946 sources located at ∣b∣ ≤ 20° in the PS1-AllWISE footprint. Photometric redshifts of GPQ candidates achieved with the XGBoost regression algorithm show that our selection method can identify quasars in a wide redshift range (0 < z ≲ 5). This study extends the systematic searches for quasars to the dense stellar fields and shows the feasibility of using astronomical knowledge to improve data mining under complex conditions in the big-data era.

This paper reports 209 O-type stars found with LAMOST. All 135 new O-type stars discovered so far with LAMOST are given. Among them, 94 stars are first presented in this sample. There are 1 Iafpe star, 5 Onfp stars, 12 Oe stars, 1 Ofc stars, 3 ON stars, 16 double-lined spectroscopic binaries, and 33 single-lined spectroscopic binaries. All O-type stars are determined based on LAMOST low-resolution spectra (R ∼ 1800), with their LAMOST median-resolution spectra (R ∼ 7500) as supplements.

Context. Developing high-precision ephemerides for Neptunian satellites requires not only the continuation of observing campaigns but also the collection and improvement of existing observations. So far, no complete catalogs of observations of Neptunian satellites are available. Aims: We aim to provide new, precise positions, and to compile a catalog including all available ground-based astrometric observations of Neptunian satellites. The observations are tabulated in a single and consistent format and given in the same timescale, the Terrestrial Time (TT), and reference system, the International Celestial Reference System (ICRS), including necessary changes and corrections. Methods: New CCD observations of Triton and Nereid were made at Lijiang 2.4-m and Yaoan 0.8-m telescopes in 2013–2019, and then reduced based on Gaia-DR2. Furthermore, a catalog called OCNS2019 (Observational Catalog of Neptunian Satellites (2019 version)) was compiled, after recognizing and correcting errors and omissions. Furthermore, in addition to what was considered for the COSS08 catalog for eight main Saturnian satellites, all observed absolute and relative coordinates were converted to the ICRS with corrections for star catalog biases with respect to Gaia-DR2. New debiasing tables for both the modern and old star catalogs, which were previously not provided based on Gaia-DR2, are developed and applied. Treatment of missing positions of comparison bodies in conversions of observed relative coordinates are proposed. Results: OCNS2019 and the new debiasing tables are publicly available online. OCNS2019 includes 24996 observed coordinates of 11 Neptunian satellites obtained over 3741 nights from 1847 to 2019. All observations are given in TT and ICRS. The star catalog biases are removed, which are significant for Nereid and outer satellites. We obtained 880 (5% of total now available) new coordinates for Triton over 41 nights (1% of total observation nights so far), and 790 (14%) for Nereid over 47 nights (10%). The dispersions of these new positions are about 0.″03 for Triton and 0.″06 for Nereid. Conclusions: OCNS2019 should be useful in improving ephemerides for the above-mentioned objects.

The Shack-Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However, if strong atmospheric turbulence exists or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected. In this work, we propose a compressive Shack-Hartmann wavefront sensing method. Instead of reconstructing wavefronts with slope measurements of all subapertures, our method reconstructs wavefronts with slope measurements of subapertures that have spot images with high signal-to-noise ratio. We further propose to use a deep neural network to accelerate the wavefront reconstruction speed. During the training stage of the deep neural network, we propose to add a drop-out layer to simulate the compressive sensing process, which could increase the development speed of our method. After training, the compressive Shack-Hartmann wavefront sensing method can reconstruct wavefronts at high spatial resolution with slope measurements from only a small number of subapertures. We integrate the straightforward compressive Shack-Hartmann wavefront sensing method with an image deconvolution algorithm to develop a high-order image restoration method. We use images restored by the high-order image restoration method to test the performance of our compressive Shack-Hartmann wavefront sensing method. The results show that our method can improve the accuracy of wavefront measurements and is suitable for real-time applications.

We applied the revised M subdwarf classification criteria discussed in Zhang et al. to Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR7 and combined the result with the M subdwarf sample from Savcheva et al. to construct a new M subdwarf sample for further study. The atmospheric parameters for each object were derived from fitting to the PHOENIX grid, and the sources with available astrometry and photometry from Gaia DR2 were combined for further analysis. The relationship between the gravity and metallicity was explored according to the locus both in the color-absolute magnitude diagram and the reduced proper motion diagram. Objects that have both the largest gravity and the lowest metallicity are located away from the main-sequence cloud and may be considered as the intrinsic M subdwarfs, which can be classified as luminosity class VI. Another group of objects whose spectra show typical M subdwarf characteristics have lower gravity and relatively moderate metal deficiency and occupy part of the ordinary M dwarf region in both diagrams. The Galactic U, V, W space velocity components and their dispersion show that the local Galactic halo population sampled in the solar neighborhood is represented by objects of high gravity and an inconspicuous bimodal metallicity distribution, with a fraction of prograde orbits. The other M subdwarfs seem to belong in part to the thick disk component, with a significant fraction of thin disk, moderately metal-poor objects intricately mixed with them. However, selection effects, especially the favored anticenter direction of investigation in the LAMOST subsample, as well as contamination by multiplicity and parameter coupling, could play important roles and need to be investigated further.

We present a three-dimensional (3D) extinction map of the southern sky. The map covers the SkyMapper Southern Survey (SMSS) area of ∼14,000 deg2 and has spatial resolutions between 6'9 and 27'. Based on the multi-band photometry of SMSS, the Two Micron All Sky Survey, the Wide-Field Infrared Survey Explorer Survey, and the Gaia mission, we have estimated values of the r-band extinction for ∼19 million stars with the spectral energy distribution analysis. Together with the distances calculated from the Gaia data release 2 (DR2) parallaxes, we have constructed a 3D extinction map of the southern sky. By combining our 3D extinction map with those from the literature, we present an all-sky 3D extinction map, and use it to explore the 3D distribution of the Galactic dust grains. We use two different models, one consisting of a single disk and another of two disks, to fit the 3D distribution of the Galactic dust grains. The data is better fitted by a two-disk model, yielding smaller values of the Bayesian Information Criterion. The best-fit model has scale heights of 73 and 225 pc for the "thin" and "thick" dust disks, respectively.

Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data-driven image restoration methods. However, most of these methods belong to supervised learning algorithms, which require paired images either from real observations or simulated data as training set. For some applications, it is hard to get enough paired images from real observations and simulated images are quite different from real observed ones. In this paper, we propose a new data-driven image restoration method based on generative adversarial networks with option-driven learning. Our method uses several high-resolution images as references and applies different learning strategies when the number of reference images is different. For sky surveys with variable observation conditions, our method can obtain very stable image restoration results, regardless of the number of reference images.

In this paper, we report 591 high-velocity star candidates (HiVelSCs) selected from over 10 million spectra of Data Release 7 (DR7) of the Large Sky Area Multi-object Fiber Spectroscopic Telescope and the second Gaia data release, with three-dimensional velocities in the Galactic rest frame larger than 445 km s-1. We show that at least 43 HiVelSCs are unbound to the Galaxy with escape probabilities larger than 50%, and this number decreases to eight if the possible parallax zero-point error is corrected. Most of these HiVelSCs are metal-poor and slightly α-enhanced inner halo stars. Only 14% of them have [Fe/H] > -1, which may be the metal-rich "in situ" stars in the halo formed in the initial collapse of the Milky Way or metal-rich stars formed in the disk or bulge but kinematically heated. The low ratio of 14% implies that the bulk of the stellar halo was formed from the accretion and tidal disruption of satellite galaxies. In addition, HiVelSCs on retrograde orbits have slightly lower metallicities on average compared with those on prograde orbits; meanwhile, metal-poor HiVelSCs with [Fe/H] < -1 have an even faster mean retrograde velocity compared with metal-rich HiVelSCs. To investigate the origins of HiVelSCs, we perform orbit integrations and divide them into four types, i.e., hypervelocity stars, hyper-runaway stars, runaway stars and fast halo stars. A catalog for these 591 HiVelSCs, including radial velocities, atmospheric parameters, Gaia astrometric parameters, spatial positions, and velocities, etc., is available in the China-VO PaperData Repository at doi:10.12149/101038.

Binaries play key roles in determining stellar parameters and exploring stellar evolution models. We build a catalog of 88 eclipsing binaries with spectroscopic information, taking advantage of observations from both the Large Sky Area Multi-Object fiber Spectroscopic Telescope and the Palomar Transient Factory surveys. A software pipeline is constructed to identify binary candidates by examining their light curves. The orbital periods of binaries are derived from the Lomb-Scargle method. The key distinguishing features of eclipsing binaries are recognized by a new filter, Flat Test. We classify the eclipsing binaries by applying a Fourier analysis on the light curves. Among all the binary stars, 13 binaries are identified as eclipsing binaries for the first time. The catalog contains the following information: the position, primary eclipsing magnitude and time, eclipsing depth, the number of photometry and radial velocity observations, largest radial velocity difference, binary type, the effective temperature of the observable star Teff, and surface gravity of the observable star log g. The false-positive probability is calculated by using both a Monte Carlo simulation and real data from the Sloan Digital Sky Survey Stripe 82 Standard Catalog. The binaries in the catalog are mostly with a period of less than one day. The period distribution shows a 0.22 day cutoff, which is consistent with the low probability of an eclipsing binary rotating with such a period.

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is a major facility to carry out spectroscopic surveys for cosmology and galaxy evolution studies. The seventh data release of the LAMOST ExtraGAlactic Survey (LEGAS) is currently available and including redshifts of 193 361 galaxies. These sources are spread over $\sim 11\, 500$ deg2 of the sky, largely overlapping with other imaging (SDSS and HSC) and spectroscopic (BOSS) surveys. The estimated depth of the galaxy sample, r ∼ 17.8, the high signal-to-noise ratio, and the spectral resolution R = 1800, make the LAMOST spectra suitable for galaxy velocity dispersion (VD) measurements, which are invaluable to study the structure and formation of galaxies and to determine their central dark matter content. We present the first estimates of central VD of $\sim 86\, 000$ galaxies in LAMOST footprint. We have used a wrap-up procedure to perform the spectral fitting using PPXF, and derive VD measurements. Statistical errors are also assessed by comparing LAMOST VD estimates with the ones of SDSS and BOSS over a common sample of $\sim 51\, 000$ galaxies. The two data sets show a good agreement, within the statistical errors, in particular when VD values are corrected to 1 effective radius aperture. We also present a preliminary mass-σ relation and find consistency with previous analyses based on local galaxy samples. These first results suggest that LAMOST spectra are suitable for galaxy VD measurements to complement the available catalogues of galaxy internal kinematics in the Northern hemisphere. We plan to expand this analysis to next LAMOST data releases.

We establish the largest eruptive/confined flare database to date and analyze 322 flares of Geostationary Operational Environmental Satellite class M1.0 and larger that occurred during 2010-2019, i.e., almost spanning all of solar cycle 24. We find that the total unsigned magnetic flux ( ${{\rm{\Phi }}}_{\mathrm{AR}}$ ) of active regions (ARs) is a key parameter governing the eruptive character of large flares, with the proportion of eruptive flares exhibiting a strong anticorrelation with ${{\rm{\Phi }}}_{\mathrm{AR}}$ . This means that an AR containing a large magnetic flux has a lower probability that the large flares it produces will be associated with a coronal mass ejection (CME). This finding is supported by the high positive correlation we obtained between the critical decay index height and ${{\rm{\Phi }}}_{\mathrm{AR}}$ , implying that ARs with a larger ${{\rm{\Phi }}}_{\mathrm{AR}}$ have a stronger magnetic confinement. Moreover, the confined flares originating from ARs larger than $1.0\times {10}^{23}$ Mx have several characteristics in common: stable filament, slipping magnetic reconnection, and strongly sheared post-flare loops. Our findings reveal new relations between the magnetic flux of ARs and the occurrence of CMEs in association with large flares. The relations obtained here provide quantitative criteria for forecasting CMEs and adverse space weather, and have important implications for "superflares" on solar-type stars and stellar CMEs.

Cross-matching catalogues from radio surveys to catalogues of sources at other wavelengths is extremely hard, because radio sources are often extended, often consist of several spatially separated components, and often no radio component is coincident with the optical/infrared host galaxy. Traditionally, the cross-matching is done by eye, but this does not scale to the millions of radio sources expected from the next generation of radio surveys. We present an innovative automated procedure, using Bayesian hypothesis testing, that models trial radio-source morphologies with putative positions of the host galaxy. This new algorithm differs from an earlier version by allowing more complex radio-source morphologies, and performing a simultaneous fit over a large field. We show that this technique performs well in an unsupervised mode.

We introduce a new kinematic method to investigate the structure of open star clusters. We adopt a hierarchical clustering algorithm that uses the celestial coordinates and the proper motions of the stars in the field of view of the cluster to estimate a proxy of the pairwise binding energy of the stars and arrange them in a binary tree. The cluster substructures and their members are identified by trimming the tree at two thresholds, according to the σ-plateau method. Testing the algorithm on 100 mock catalogs shows that, on average, the membership of the identified clusters is (91.5 ± 3.5)% complete and the fraction of unrelated stars is (10.4 ± 2.0)%. We apply the algorithm to the stars in the field of view of the Perseus double cluster from the Data Release 2 of Gaia. This approach identifies a single structure, Sub1, that separates into two substructures, Sub1-1 and Sub1-2. These substructures coincide with h Per and χ Per: the distributions of the proper motions and the color-magnitude diagrams of the members of Sub1-1 and Sub1-2 are fully consistent with those of h Per and χ Per reported in the literature. These results suggest that our hierarchical clustering algorithm can be a powerful tool to unveil the complex kinematic information of star clusters.

Seeing—the angular size of stellar images blurred by atmospheric turbulence—is a critical parameter used to assess the quality of astronomical sites at optical/infrared wavelengths. Median values at the best mid-latitude sites are generally in the range of 0.6-0.8 arcseconds1-3. Sites on the Antarctic plateau are characterized by comparatively weak turbulence in the free atmosphere above a strong but thin boundary layer4-6. The median seeing at Dome C is estimated to be 0.23-0.36 arcseconds7-10 above a boundary layer that has a typical height of 30 metres10-12. At Domes A and F, the only previous seeing measurements have been made during daytime13,14. Here we report measurements of night-time seeing at Dome A, using a differential image motion monitor15. Located at a height of just 8 metres, it recorded seeing as low as 0.13 arcseconds, and provided seeing statistics that are comparable to those at a height of 20 metres at Dome C. This indicates that the boundary layer was below 8 metres for 31 per cent of the time, with median seeing of 0.31 arcseconds, consistent with free-atmosphere seeing. The seeing and boundary-layer thickness are found to be strongly correlated with the near-surface temperature gradient. The correlation confirms a median thickness of approximately 14 metres for the boundary layer at Dome A, as found from a sonic radar16. The thinner boundary layer makes it less challenging to locate a telescope above it, thereby giving greater access to the free atmosphere.

We estimate the Milky Way (MW) halo properties using satellite kinematic data including the latest measurements from Gaia DR2. With a simulation-based 6D phase-space distribution function (DF) of satellite kinematics, we can infer halo properties efficiently and without bias, and handle the selection function and measurement errors rigorously in the Bayesian framework. Applying our DF from the EAGLE simulation to 28 satellites, we obtain an MW halo mass of $M={1.23}_{-0.18}^{+0.21}\times {10}^{12}{M}_{\odot }$ and a concentration of $c={9.4}_{-2.1}^{+2.8}$ with the prior based on the M-c relation. The inferred mass profile is consistent with previous measurements but with better precision and reliability due to the improved methodology and data. Potential improvement is illustrated by combining satellite data and stellar rotation curves. Using our EAGLE DF and best-fit MW potential, we provide much more precise estimates of the kinematics for those satellites with uncertain measurements. Compared to the EAGLE DF, which matches the observed satellite kinematics very well, the DF from the semi-analytical model based on the dark-matter-only simulation Millennium II (SAM-MII) over-represents satellites with small radii and velocities. We attribute this difference to less disruption of satellites with small pericenter distances in the SAM-MII simulation. By varying the disruption rate of such satellites in this simulation, we estimate a ∼5% scatter in the inferred MW halo mass among hydrodynamics-based simulations.

Wide-field small aperture telescopes are widely used for optical transient observations. Detection and classification of astronomical targets in observed images are the most important and basic step. In this paper, we propose an astronomical target detection and classification framework based on deep neural networks. Our framework adopts the concept of the Faster R-CNN and uses a modified Resnet-50 as a backbone network and a feature pyramid network to extract features from images of different astronomical targets. To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network. After training, the neural network could detect and classify astronomical targets automatically. We test the performance of our framework with simulated data and find that our framework has almost the same detection ability as that of the traditional method for bright and isolated sources and our framework has two times better detection ability for dim targets, albeit all celestial objects detected by the traditional method can be classified correctly. We also use our framework to process real observation data and find that our framework can improve 25% detection ability than that of the traditional method when the threshold of our framework is 0.6. Rapid discovery of transient targets is quite important and we further propose to install our framework in embedded devices such as the Nvidia Jetson Xavier to achieve real-time astronomical targets detection and classification abilities.

Ground-based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instrument design and image restoration method development. Because the point-spread function can reflect performance of the whole optic system, it is appropriate to use the point-spread function to describe atmospheric turbulence induced aberrations. Assuming point-spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a nonparametric point-spread function -- PSF-NET. The PSF-NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF-NET with simulated and real observation data, we find that a well trained PSF-NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF-NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF-NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF-NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would benefit the development of new observation methods for ground-based optical telescopes.

The fundamental stellar atmospheric parameters (Teff and log g) and 13 chemical abundances are derived for medium-resolution spectroscopy from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Medium Resolution Survey (MRS) data sets with a deep-learning method. The neural networks we designed, named SPCANet, precisely map LAMOST MRS spectra to stellar parameters and chemical abundances. The stellar labels derived by SPCANet have precisions of 119 K for Teff and 0.17 dex for log g. The abundance precision of 11 elements including [C/H], [N/H], [O/H], [Mg/H], [Al/H], [Si/H], [S/H], [Ca/H], [Ti/H], [Cr/H], [Fe/H], and [Ni/H] are 0.06 ∼ 0.12 dex, while that of [Cu/H] is 0.19 dex. These precisions can be reached even for spectra with signal-to-noise ratios as low as 10. The results of SPCANet are consistent with those from other surveys such as APOGEE, GALAH, and RAVE, and are also validated with the previous literature values including clusters and field stars. The catalog of the estimated parameters is available at doi:10.12149/101012.

We have compiled an extensive catalog of candidate wide binaries selected from Gaia DR2 in the solar neighborhood with distances $d<4$~kpc, following a procedure similar to that of El-Badry & Rix(2018). This initial candidate catalog consists of 807,611 possible binaries. Its contamination rates are lower than 10% at $a<20,000$~AU; however, the contamination rates quickly increase beyond 20,000~AU, until up to 100% at the largest separation bin, i.e., a = 1.0 pc. To address this, we subsequently applied additional selection criteria, tailored towards three kinematically-selected, presumably pure subsamples: 4361 disk-like, 10090 intermediate, and 4351 halo-like binaries.

The radial velocity (RV) is a basic physical quantity that can be determined through the Doppler shift of the spectrum of a star. The precision of the RV measurement depends on the resolution of the spectrum we used and the accuracy of wavelength calibration. In this work, radial velocities of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope-II (LAMOST-II) medium-resolution (R ∼ 7500) spectra are measured for 1,594,956 spectra (each spectrum has two wavebands) through matching with templates. A set of RV standard stars are used to recalibrate the zero point of the measurement, and some reference sets with RVs derived from medium-/high-resolution observations are used to evaluate the accuracy of the measurement. By comparing with reference sets, the accuracy of our measurement can get 0.0277 km s-1 with respect to radial velocities of standard stars. The intrinsic precision is estimated with the multiple observations of single stars, which can be achieved to 1.36 km s-1, 1.08 km s-1, and 0.91 km s-1 for the spectra at signal-to-noise levels of 10, 20, and 50, respectively.

Texture is one of the most obvious characteristics in solar images and it is normally described by texture features. Because textures from solar images of the same wavelength are similar, we assume that texture features of solar images are multi-fractals. Based on this assumption, we propose a pure data-based image restoration method: with several high-resolution solar images as references, we use the Cycle-Consistent Adversarial Network to restore blurred images of the same steady physical process, in the same wavelength obtained by the same telescope. We test our method with simulated and real observation data and find that our method can improve the spatial resolution of solar images, without loss of any frames. Because our method does not need a paired training set or additional instruments, it can be used as a post-processing method for solar images obtained by either seeing-limited telescopes or telescopes with ground-layer adaptive optic systems.