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

TL;DR

This paper introduces a Bayesian pipeline, PhotoD, for estimating stellar distances, metallicity, and dust extinction using LSST-like photometry, validated with simulations and existing surveys, aiming for public data releases.

Contribution

The paper presents a scalable Bayesian model and pipeline tailored for LSST data, incorporating isochrone models and priors from simulated catalogs, with plans for public release.

Findings

Pipeline achieves ~10 ms per star computation speed.

Validation with simulated and real survey data confirms accuracy.

Neural networks can further accelerate performance by up to tenfold.

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. Mrakov\v{c}i\'c 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.