A Monte-Carlo Method for Estimating Stellar Photometric Metallicity Distributions
Jiayin Gu, Cuihua Du, Yingjie jing, Wenbo Zuo

TL;DR
This paper introduces a Monte Carlo method for estimating the stellar photometric metallicity distribution function from SDSS data, improving accuracy especially at metallicity extremes and revealing insights into the Milky Way's structure.
Contribution
A novel Monte Carlo approach for more reliable photometric MDF estimation, outperforming previous polynomial-based methods and applied to analyze Galactic stellar populations.
Findings
MDFs fit by multiple Gaussians reveal disk and halo components.
Inner and outer halo components identified through MDF peaks.
Disk-to-halo ratio decreases with height above the Galactic plane.
Abstract
Based on the Sloan Digital Sky Survey (SDSS), we develop a new monte-carlo based method to estimate the photometric metallicity distribution function (MDF) for stars in the Milky Way. Compared with other photometric calibration methods, this method enables a more reliable determination of the MDF, in particular at the metal-poor and metal-rich ends. We present a comparison of our new method with a previous polynomial-based approach, and demonstrate its superiority. As an example, we apply this method to main-sequence stars with , kpc kpc, and in different intervals in height above the plane, . The MDFs for the selected stars within two relatively local intervals ( kpc kpc, kpc kpc) can be well-fit by two Gaussians, with peaks at [Fe/H] and respectively, one associated with the disk system, the other with the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
