PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy

Abstract As demonstrated with the Sloan Digital Sky Survey (SDSS), Pan-STARRS, and most recently with Gaia data, broadband near-UV to near-IR stellar photometry can be used to estimate distance, metallicity, and interstellar dust extinction along the line of sight for stars in the Galaxy. Anticipating photometric catalogs with tens of billions of stars from Rubin's Legacy Survey of Space and Time (LSST), we present a Bayesian model and pipeline that build on previous work and can handle LSST-sized datasets. Likelihood computations utilize MIST/Dartmouth isochrones and priors are derived from TRILEGAL-based simulated LSST catalogs from P. Dal Tio et al. The computation speed is about 10 ms per star on a single core for both optimized grid search and Markov Chain Monte Carlo methods; we show in a companion paper by K. Mrakovčić et al. how to utilize neural networks to accelerate this performance by up to an order of magnitude. We validate our pipeline, named PhotoD (in analogy with photo- z , photometric redshifts of galaxies) using both simulated catalogs and SDSS, DECam, and Gaia photometry. We intend to make LSST-based value-added PhotoD catalogs publicly available via the Rubin Science Platform with every LSST data release.

Authors

Publication Details

Journal
The Astronomical Journal
Published
2025-02-04
DOI
https://doi.org/10.3847/1538-3881/ada3c2
Citations
2
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
2.31

Funders

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article

PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy

Bob Abel, Tomislav Jurkić, L. Palaversa, Filip Matković et al.
2 citations
The Astronomical Journal
Stellar, planetary, and galactic studies
2.31
article

PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy

Bob Abel, Tomislav Jurkić, L. Palaversa, Filip Matković, Mario Jurić, Konstantin Malanchev, Željko Ivezić, Oleksandra Razim, Alex I. Malz, Melissa DeLucchi, Sean McGuire, Sandro Campos, Neven Čaplar, D. C. Jones, Karlo Mrakovčić, T. Šarić, Connor Yablonski
article en
2 citations

Abstract

Abstract As demonstrated with the Sloan Digital Sky Survey (SDSS), Pan-STARRS, and most recently with Gaia data, broadband near-UV to near-IR stellar photometry can be used to estimate distance, metallicity, and interstellar dust extinction along the line of sight for stars in the Galaxy. Anticipating photometric catalogs with tens of billions of stars from Rubin's Legacy Survey of Space and Time (LSST), we present a Bayesian model and pipeline that build on previous work and can handle LSST-sized datasets. Likelihood computations utilize MIST/Dartmouth isochrones and priors are derived from TRILEGAL-based simulated LSST catalogs from P. Dal Tio et al. The computation speed is about 10 ms per star on a single core for both optimized grid search and Markov Chain Monte Carlo methods; we show in a companion paper by K. Mrakovčić et al. how to utilize neural networks to accelerate this performance by up to an order of magnitude. We validate our pipeline, named PhotoD (in analogy with photo- z , photometric redshifts of galaxies) using both simulated catalogs and SDSS, DECam, and Gaia photometry. We intend to make LSST-based value-added PhotoD catalogs publicly available via the Rubin Science Platform with every LSST data release.

The Astronomical JournalVol. 169(3)
National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Washington Research Foundation, University of Washington, European Space Agency, Max-Planck-Gesellschaft, Hrvatska Zaklada za Znanost, Institut Ruđer Bošković
Openalex Percentile: Top 13%
Stellar, planetary, and galactic studies
2.31
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