Stellar characterization with photometric colors from J-PLUS and 2MASS surveys

TL;DR

This paper presents a machine learning method to derive stellar atmospheric parameters from photometric data of millions of stars, achieving accuracy comparable to spectroscopic surveys and enabling efficient analysis even with incomplete data.

Contribution

It introduces a semi-supervised machine learning approach combining k-means and k-nearest neighbors to estimate stellar parameters from photometry without prior training.

Findings

Estimated parameters for over 5.6 million stars with good agreement to spectroscopic data.

Reduced computation time and data requirements by using a subset of 11 colors.

Method remains reliable even with incomplete or lower-quality photometric data.

Abstract

Aims. We aim at deriving stellar atmospheric parameters based on the photometric data from the Javalambre Photometric Local Universe Survey (J-PLUS) in addition to near-infrared photometry from the Two Micron All-Sky Survey (2MASS). Methods. Our method consists of a semi-supervised machine learning approach based on the k-means method combined with a modified k-nearest neighbors algorithm. This method compares the observed photometry to a set of reference data to estimate the stellar effective temperature ($T_{\rm eff}$), surface gravity ($\log{g}$), and metallicity ([Fe/H]) of stars from J-PLUS Data Release 3 (DR3). Results. We estimated $T_{\rm eff}$, $\log{g}$, and [Fe/H], for approximately 5.6 million stars from J-PLUS DR3, along with their errors.Our results were in agreement with spectroscopic estimates from LAMOST and APOGEE.We also applied a dimension reduction method, seeking greater efficiency by reducing the computation time and minimizing the needed information for calculating the stellar parameters, resulting in a subset of 11 colors. From this approach, stellar parameters were obtained for approximately six million stars. Conclusions. Our results demonstrated the potential of using a method built from machine learning algorithms that do not require prior training. Additionally, it was shown that the proposed method allowed estimating reliable atmospheric parameters even when the available photometry did not fulfill all photometric quality criteria. We defined a neighborhood parameter, which assesses the reliability of our estimations and indicates that objects with smaller neighborhoods values have lower uncertainties.