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SPORTS: Stellar Parameters fOR all The Stars

A RACE for measuring stellar parameters for Milky Way stars

The field of galactic archaeology has entered a golden era, due to the culmination of decades of large-scale spectroscopic efforts such as the HK Survey, the Hamburg/ ESO Survey (HES), the Sloan Digital Sky Survey (SDSS), the Radial Velocity Experiment (RAVE), the Sloan Extension for Galactic Understanding and Exploration (SEGUE), the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), the Galactic Archaeology with HERMES project (GALAH), the Apache Point Observatory Galactic Evolution Experiment (APOGEE), and the Hectochelle in the Halo at High-Resolution survey (H3). Stars covering an enormous range of metallicity, including large numbers of stars with metallicities below the lowest-abundance globular clusters (stars once thought not to exist, on theoretical grounds, as recently as the early 1980s), have been discovered and analyzed in great detail (see reviews by Beers & Christlieb 2005; Ivezić et al. 2012; Frebel & Norris 2015). Putting these discoveries into the context of the stellar populations of the Galaxy has been expedited greatly by the successful Gaia mission and its data releases to date. These surveys, collectively, have enabled astronomers to draw a much clearer picture of the stellar populations of our Milky Way (MW), and significantly advanced our knowledge of its chemical evolution and assembly history.

The location of the Sun in the disk of the Milky Way presents a challenge for the task of obtaining an unbiased, representative sample of stars with available full multidimensional information (stellar abundances, distances, motions, and ages), for a number of reasons. For one, the selection functions of spectroscopic surveys to date are all different from one another, and often complex. Even Gaia satellite mission, which has sampled 1% of the stars of the Milky Way (one billion stars), still exhibits significant spatial patterns due to its observational strategy. The sampling of Galactic spectroscopic surveys is even more sparse. For example, the LAMOST Galactic spectroscopic survey (the largest to date, which has been underway for almost a decade), has collected over eight million spectra with signal-to-noise ratio greater than 10 for over four million unique stars in its latest public data release. Although ongoing and next-generation spectroscopic surveys will greatly expand the numbers of stars examined in the Milky Way, they will still pale in raw numbers of stars compared to the large-scale astrometric surveys such as Gaia, and of course, will still have to deal with the impact of their target-selection criteria in order to extract knowledge and understanding from their data.


We proposed to alleviate this issue of current spectroscopic surveys by deriving stellar parameters for a huge number of stars using narrow/medium-bandwidth photometric surveys (see Table 1 of Huang et al. 2022 for a summary). As a pioneering experiment, we present measurements of stellar parameters, including metallicity, luminosity classification, effective temperature, distance, and stellar age, for nearly 90 million stars (see Figure 1 for a summary), based on the stellar colors from the Stellar Abundances and Galactic Evolution Survey (SAGES) and J-PLUS in the northern sky, and SkyMapper Southern Survey (SMSS) and S-PLUS in the southern sky, as well as the Gaia all-sky data. We also derive alpha and carbon abundance from J/S-PLUS data given their special narrow-filters sited on carbon/magnesium/calcium abosorption features of stars.

Figure 1: The number of stellar parameters we measured from SAGES, SMSS, J-PLUS and S-PLUS.

This ongoing project, named SPORTS (Stellar Parameter fOR all The Stars, PI: Yang Huang & Timothy Beers), symbolizes a race to measure the stellar parameters of all Milky Way stars using data from ongoing/next-generation mega-surveys (see Figure 2 for a summary). The ongoing surveys, including SMSS, J/S-PLUS, and Gaia XP, allow us to continuously expand the number of stars with measured stellar parameters.In the future, two important mega-surveys will not only increase the number of stars with measured parameters but also extend our reach to greater depths, allowing us to probe the outer edges of our Galaxy. The two surveys are J-PAS and the Chinese Space Station Telescope – CSST.

J-PAS: The J-PAS survey (Benitez et al. 2014) equipped with over 50 narrow-band filters will observe about 8500 square degree of the northern sky with a limiting magnitude down to 23-34 mag, by using a purpose-built, dedicated 2.5m telescope and a 4.7 square degree camera with 1.2Gpix.

CSST: It has a high spatial resolution (REE80 < 0.13 arcsec) similar to that of HST but with a much larger field of view (>1.1 square dgree). Within the planned 10-year period, it will image a contiguous part of the sky with an area of 17,500 square degree in 7 filters from the near-ultraviolet to optical to near-infrared wavelengths (NUV + ugrizy) with limiting magnitudes of 25.4, 25.4, 26.3, 26.0, 25.9, 25.2 and 24.4 mag, respectively. It even has the capability of slitless spectroscopic observations, covering the wavelength range from 255 nm to 1 micron with a resolving power of R ~ 200.

The limiting magnitudes of the two surveys are much deeper than the current surveys. Now the construction phases of the two surveys will be finished soon and they will start the survey observations and release data in near future (3-5 years). At that time, we can derive precise stellar parameters even for a large sample of very faint sources to probe the edge of the disk and the very distant halo (even close to the virial radii) of our Galaxy, and then draw a global picture of the Milky Way.

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Figure 2: The pathway we are developing aims to derive stellar parameters for all stars in the Milky Way.

Projects for students:

  • Data processing: photometric zero-point clibrations

How can we achieve millimagnitude-level calibration accuracy? This is crucial for accurate parameter estimation. See a comprehensive review in Chinese: Huang, B-W., Xiao, K., Yuan, H.-B. 2022.

  • Parameter measurements:

    • Try with a new survey data

    • Explore the possibility to measure more stellar parameters

    • Explore new estimate techniques (e.g. using AI)

  • Sciences:

    • Global structures: disk or halo (example)

    • MW substructures and streams (example)

    • VMPs to study eraly Universe (example)

    • ...

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