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The Gaia All-Sky Stellar Parameters Service (GASPS)

I. McDonald, A. A. Zijlstra, N. J. Cox, J. Bernard-Salas

TL;DR

GASPS addresses the challenge of deriving fundamental stellar parameters for an immense all-sky sample by performing automated SED fitting of Gaia DR3 sources with parallaxes. The methodology combines cross-matched multiwavelength photometry, extinction corrections from a 3D cube, and BT-Settl atmosphere models fitted via Nelder–Mead optimization, with $L = 4\pi R^2\sigma T^4$ and $R \sim \theta d$ used to obtain luminosities. Results include ~240 million stars (~13% of Gaia DR3) with Teff and L; the HR diagram shows canonical evolutionary sequences, and UV/IR excess diagnostics illuminate binary, mass-loss, and dusty-star populations. Future directions include extending reductions to non-parallax stars, refining reddening with Gaia DR4, and pursuing mineralogical dust mapping via extinction curve slope, enabling a Milky Way-wide census and cross-survey stellar classification.

Abstract

Temperature and luminosity are the two key diagnostics of a star, yet these cannot come directly from survey data, but must be imputed by comparing those data to models. SED fitting offers a high-precision method to obtain both parameters for stars where both their distance and extinction are well known. The recent publication of many all-sky or large-area surveys coincides the publication of parallaxes and 3D extinction cubes from the Gaia satellite, making it possible to perform SED fitting of truly large ($>10^8$) numbers of Galactic stars for the first time. The analysis of this data requires a high level of automation. Here, we describe the ongoing Gaia All-Sky Stellar Parameters Service (GASPS): the fitting of 240 million SEDs from Gaia DR3 and the extraction of temperatures and luminosities for the corresponding stars using the PySSED code. We demonstrate the quality of the initial results, and the promise that these data show, from wavelength-specific information such as the ultraviolet and infrared excess of each star, to stellar classification, to expansion of the project beyond our own Galaxy, and mineralogical mapping of the Milky Way's interstellar medium.

The Gaia All-Sky Stellar Parameters Service (GASPS)

TL;DR

GASPS addresses the challenge of deriving fundamental stellar parameters for an immense all-sky sample by performing automated SED fitting of Gaia DR3 sources with parallaxes. The methodology combines cross-matched multiwavelength photometry, extinction corrections from a 3D cube, and BT-Settl atmosphere models fitted via Nelder–Mead optimization, with and used to obtain luminosities. Results include ~240 million stars (~13% of Gaia DR3) with Teff and L; the HR diagram shows canonical evolutionary sequences, and UV/IR excess diagnostics illuminate binary, mass-loss, and dusty-star populations. Future directions include extending reductions to non-parallax stars, refining reddening with Gaia DR4, and pursuing mineralogical dust mapping via extinction curve slope, enabling a Milky Way-wide census and cross-survey stellar classification.

Abstract

Temperature and luminosity are the two key diagnostics of a star, yet these cannot come directly from survey data, but must be imputed by comparing those data to models. SED fitting offers a high-precision method to obtain both parameters for stars where both their distance and extinction are well known. The recent publication of many all-sky or large-area surveys coincides the publication of parallaxes and 3D extinction cubes from the Gaia satellite, making it possible to perform SED fitting of truly large () numbers of Galactic stars for the first time. The analysis of this data requires a high level of automation. Here, we describe the ongoing Gaia All-Sky Stellar Parameters Service (GASPS): the fitting of 240 million SEDs from Gaia DR3 and the extraction of temperatures and luminosities for the corresponding stars using the PySSED code. We demonstrate the quality of the initial results, and the promise that these data show, from wavelength-specific information such as the ultraviolet and infrared excess of each star, to stellar classification, to expansion of the project beyond our own Galaxy, and mineralogical mapping of the Milky Way's interstellar medium.
Paper Structure (5 sections, 1 equation, 2 figures)

This paper contains 5 sections, 1 equation, 2 figures.

Table of Contents

  1. Introduction
  2. GASPS
  3. Results
  4. Uses and future work
  5. Conclusions

Figures (2)

  • Figure 1: A binned Hertzsprung--Russell diagram of sources in GASPS. The plot contains 33 million sources covering the (mostly low extinction) regions near the Galactic Poles. Point shading is proprtional to the logarithm of the number of sources in each bin to highlight areas with fewer stars (i.e., regions with problems and/or rarer stellar types). Bin colour is dictated by the average ratio of observed to modelled flux in the Gaia$G$ band (blue is 0.8, red is 1.25). Similar plots are available for other passbands, and serve as a useful analytical tool of the accuracy of calibration in each survey and of the GASPS reduction overall.
  • Figure 2: A binned Hertzsprung--Russell diagram of sources in GASPS. As Figure \ref{['fig:HRFig']}, but colour-coded to show average goodness-of-fit (left, coloured from blue [0.01] through grey [0.03] to red [0.1]) and extinction (right, coloured by hue from red [$E(B-V)=0$ mag] through green [0.33] and blue [0.67] back to red [1 mag]).