Gaia Data Release 3 correction Version 1.0 English version ================================================================================ Update history v0.1 Based on CALSPEC (2020 April version) and NGSL v0.2 Based on CALSPEC (2020 April version), NGSL and LAMOST DR7 v0.4 Based on CALSPEC (2020 April version), NGSL, LAMOST DR7 and corrected Gaia DR3 photometry v1.0 Based on CALSPEC (2023 September version), NGSL, LAMOST DR7 ================================================================================ Abstract： 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. Overview: -------------------------------------------------------------------------------- File name Description -------------------------------------------------------------------------------- readme this file Gaia_Correction.py module ****.csv parameters for correction example.py example example_file_coeff.csv The coefficients of Gaia XP spectra for example.py -------------------------------------------------------------------------------- 1 Package Structure GaiaDR3XPspectracorrectionV1 package name Gaia_Correction module name correction function 1 correction_df function 2 2 Dependencies:numpy,pandas,os 3 Quickly start from GaiaDR3XPspectracorrectionV1 import Gaia_Correction_V1 1) Correction for one spectrum [flux_out,caution,C2,C3] = Gaia_Correction_V1.correction(flux_origin,G,error,Truncation=False,absolute_correction=True) 2) Correction for a dataframe from GaiaXPy dataframe_out = Gaia_Correction_V1.correction_df(dataframe_origin,G,have_error=True,Truncation=False,absolute_correction=True) 3) You can also refer to example.py 4 Arguments 1) correction for one spectrum: -------------------------------------------------------------------------------- Input Description Format Default -------------------------------------------------------------------------------- flux_origin original spectrum nparray none G G magnitude float none error flux error nparray False Truncation Optional in GaiaXPy bool False absolute_correction absolute_correction bool True -------------------------------------------------------------------------------- Output Description Format -------------------------------------------------------------------------------- flux_out corrected spectrum float64 nparray caution Reliability of corrections int64 nparray C2 derived from original spectrum float64 C3 derived from original spectrum float64 2) correction for a dataframe from GaiaXPy: -------------------------------------------------------------------------------- Input Description Format Default -------------------------------------------------------------------------------- dataframe_origin dataframe from GaiaXPy dataframe None G G magnitude nparray None have_error Whether to use flux_error when deriving C2 and C3 bool True Truncation Optional in GaiaXPy bool False absolute_correction absolute_correction bool True -------------------------------------------------------------------------------- Output Description Format -------------------------------------------------------------------------------- dataframe_out corrected dataframe dataframe 3) Formats of dataframe_origin and dataframe_out If have_error=True, the dataframe_origin should have at least two columns, flux and flux_error. If have_error=False, the dataframe_origin should have at least one column, flux. -------------------------------------------------------------------------------- dataframe_origin format： -------------------------------------------------------------------------------- index source_id flux flux_error 0 int64 float nparray float nparray 1 int64 float nparray float nparray 2 int64 float nparray float nparray ... -------------------------------------------------------------------------------- dataframe_out format： -------------------------------------------------------------------------------- index source_id flux flux_error flux_cor C2 C3 Caution 0 int64 float nparray no change float nparray float64 float64 int nparray 1 int64 float nparray no change float nparray float64 float64 int nparray 2 int64 float nparray no change float nparray float64 float64 int nparray ... -------------------------------------------------------------------------------- 5 Reliability of corrections 'caution' The three items in the 'caution' correspond to the correction methods for C2, C3, and G terms, respectively. Each item can take one of the four values: 0, 1, 2, or 3. When the caution values are [0 0 0], [0 0 1], or [0 0 2], it indicates that the correction work is within the normal range. Item 1: 0 The correction for the C2 term is within the acceptable range. 1 The correction for the C2 term is within the acceptable range, but it is on the edge of the parameter range. Please be cautious about its reliability. 2 The correction for the C2 term is outside the acceptable range and is deemed unreliable. Item 2: 0 The correction for the C3 term is within the acceptable range. 1 The correction for the C3 term is within the acceptable range, but it is on the edge of the parameter range. Please be cautious about its reliability. 2 The correction for the C3 term is outside the acceptable range and is deemed unreliable. Item 3: 0 The G term is corrected based on empirical spectra. 1 The G term is corrected based on LAMOST spectra, with the correction zero point derived from empirical spectra. 2 The G term is corrected based on LAMOST spectra, but the correction zero point exceeds the range of empirical spectra. 3 The G term is outside the correction range of both empirical spectra and LAMOST (G>17.4 or G<2.703). If G>17.4, then use the correction from LAMOST at G=17.4 and the empirical spectrum at G=17.122 for the zero-point. If G<2.703, then use the correction from empirical spectrum for G=2.703 In case of any ambiguity, the Chinese version shall prevail. ================================================================================ 2024.1.15