SpecDis: Value Added Distance Catalog for 4 Million Stars from DESI Year-1 Data

Abstract We present the S pec D is value-added stellar distance catalog accompanying DESI Data Release 1. S pec D is trains a feed-forward neural network (NN) with Gaia parallaxes and gets the distance estimates. To build up an unbiased training sample, we do not apply selections on parallax error or signal-to-noise (S/N) of the stellar spectra, and instead, we incorporate parallax error into the loss function. Moreover, we employ principal component analysis to reduce the noise and dimensionality of stellar spectra. Validated by independent external samples of member stars with precise distances from globular clusters, dwarf galaxies, stellar streams, combined with blue horizontal branch stars, we demonstrate that our distance measurements show no significant bias up to 100 kpc, and are much more precise than Gaia parallax beyond 7 kpc. The median distance uncertainties are 23%, 19%, 11%, and 7% for S/N &lt; 20, 20≤ S/N &lt; 60, 60 ≤ S/N &lt; 100, and S/N ≥ 100. Selecting stars with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:mi>g</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mn>3.8</mml:mn> </mml:math> and distance uncertainties smaller than 25%, we have more than 74,000 giant candidates within 50 kpc of the Galactic center and 1500 candidates beyond this distance. Additionally, we develop a Gaussian mixture model to identify unresolvable equal-mass binaries by modeling the discrepancy between the NN-predicted and the geometric absolute magnitudes from Gaia parallaxes and identify 120,000 equal-mass binary candidates. Our final catalog provides distances and distance uncertainties for &gt;4 million stars, offering a valuable resource for Galactic astronomy.

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Publication Details

Journal
The Astronomical Journal
Published
2025-08-19
DOI
https://doi.org/10.3847/1538-3881/adf1a0
Citations
1
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
1.15
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article

SpecDis: Value Added Distance Catalog for 4 Million Stars from DESI Year-1 Data

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1 citations
The Astronomical Journal
Stellar, planetary, and galactic studies
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SpecDis: Value Added Distance Catalog for 4 Million Stars from DESI Year-1 Data

Will J. Percival, Songting Li, Christopher J. Manser, D. Brooks, Rongpu Zhou, S. Juneau, Ting S. Li, Graziano Rossi, N. Palanque‐Delabrouille, Gregory Tarlé, John Della Costa, Jiaxin Han, Benjamin Alan Weaver, Laurent Le Guillou, David Sprayberry, Alexander H. Riley, D. Bianchi, David J. Schlegel, Ignasi Pérez-Ràfols, Axel de la Macorra, Martin Landriau, M. E. Levi, Marc Manera, J. Aguilar, Eusebio Sanchez, Satya Gontcho A Gontcho, Namitha Kizhuprakkat, ENRIQUE GAZTAÑAGA, K. Honscheid, Joseph Harry Silber, Arjun T. Dey, Joan Najita, J. E. Forero-Romero, Steven P. Ahlen, Andrew P. Cooper, Aaron Meisner, Sergey E. Koposov, R. Miquel, Theodore S. Kisner, Carles G. Palau, R. Kehoe, John Moustakas, Leandro José Beraldo e Silva, Claire Poppett, Amanda Byström, Monica Valluri, Mustapha Ishak, Wenting Wang, Hee‐Jong Seo, Youjia Wu, Carlos Allende Prieto, Hu Zou, Michael Schubnell, P. Doel, Francisco Prada, Gaston Gutierrez, T. Claybaugh, Hao Yang
article en
1 citations

Abstract

Abstract We present the S pec D is value-added stellar distance catalog accompanying DESI Data Release 1. S pec D is trains a feed-forward neural network (NN) with Gaia parallaxes and gets the distance estimates. To build up an unbiased training sample, we do not apply selections on parallax error or signal-to-noise (S/N) of the stellar spectra, and instead, we incorporate parallax error into the loss function. Moreover, we employ principal component analysis to reduce the noise and dimensionality of stellar spectra. Validated by independent external samples of member stars with precise distances from globular clusters, dwarf galaxies, stellar streams, combined with blue horizontal branch stars, we demonstrate that our distance measurements show no significant bias up to 100 kpc, and are much more precise than Gaia parallax beyond 7 kpc. The median distance uncertainties are 23%, 19%, 11%, and 7% for S/N &lt; 20, 20≤ S/N &lt; 60, 60 ≤ S/N &lt; 100, and S/N ≥ 100. Selecting stars with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>log</mml:mi> <mml:mi>g</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mn>3.8</mml:mn> </mml:math> and distance uncertainties smaller than 25%, we have more than 74,000 giant candidates within 50 kpc of the Galactic center and 1500 candidates beyond this distance. Additionally, we develop a Gaussian mixture model to identify unresolvable equal-mass binaries by modeling the discrepancy between the NN-predicted and the geometric absolute magnitudes from Gaia parallaxes and identify 120,000 equal-mass binary candidates. Our final catalog provides distances and distance uncertainties for &gt;4 million stars, offering a valuable resource for Galactic astronomy.

The Astronomical JournalVol. 170(3)
Openalex Percentile: Top 17%
Stellar, planetary, and galactic studies
1.15
DOIOpen Access PDFOpenAlex
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