GalEffNet Predicted Stellar Mass and sSFR Catalog from DESI DR9
Li-Li Wang ; Jia-Bao feng
This catalog is constructed from galaxies selected from the DESI Legacy Imaging Surveys DR9, applying criteria of redshift z < 0.3 and z-band magnitude mag_z < 21. For each galaxy, we provide deep learning–based predictions of stellar mass (M*) and specific star formation rate (sSFR) using our GalEffNet model, along with basic photometric and morphological information. The catalog is designed to facilitate studies of galaxy properties and evolutionary trends at low redshift.
Column descriptions:
release: Camera and filter set identifier from DESI-LIS DR9.
brickid: Unique Brick ID from DESI-LIS DR9.
objid: Object number within a given brick.
ra, dec: Right ascension and declination (J2000).
source_type: Morphological type, one of {DEV, SER, REX, EXP}.
z_phot: Photometric redshift from Zhou et al. (2021).
mag_g, mag_r, mag_z: Magnitudes in the g, r, and z bands, converted from DR9 fluxes.
pred_mass: Stellar mass, predicted by GalEffNet.
pred_ssfr: Specific star formation rate, predicted by GalEffNet.
Files
.. mass_sSFRforDESIphotoimage.csv
1.66 GB
..
.. mass_sSFRforDESIphotoimage.fits
1.60 GB
..
Paper Information
Paper Title:
Prediction of stellar mass and star formation rate for low-redshift galaxies in the DESI Legacy Imaging Surveys using deep learning
Publication:
Research in Astronomy and Astrophysics
Identifiers
CSTR:
11379.11.101677
DOI:
10.12149/101677
VO Identifier:
ivo://China-VO/paperdata/101677
Publication Date:
2025-11-11
Usage Statistics
Total Downloads
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Citations
Li-Li Wang et al. 2025. GalEffNet Predicted Stellar Mass and sSFR Catalog from DESI DR9. Version 1.0. https://doi.org/10.12149/101677
@misc{10.12149/101677,
doi = {10.12149/101677},
url = {https://doi.org/10.12149/101677},
author = {Li-Li Wang},
title = {GalEffNet Predicted Stellar Mass and sSFR Catalog from DESI DR9},
version = {1.0},
publisher = {Nataional Astronomical Data Center of China},
year= {2025}
}
Versions
Version 1.0 (current)
2025-11-11
Main
This DOI represents all versions, and will always resolve to the latest one.
2025-11-11