J-PLUS: Stellar Parameters, C, N, Mg, Ca and [α/Fe] Abundances for Two Million Stars from DR1

Abstract

Context. The Javalambre Photometric Local Universe Survey (J-PLUS) has obtained precise photometry in twelve specially designed filters for large numbers of Galactic stars. Deriving their precise stellar atmospheric parameters and individual elemental abundances is crucial for studies of Galactic structure, and the assembly history and chemical evolution of our Galaxy. Aims. Our goal is to estimate not only stellar parameters (effective temperature, T eff , surface gravity, log g, and metallicity, [Fe/H]), but also [α/Fe] and four elemental abundances ([C/Fe], [N/Fe], [Mg/Fe], and [Ca/Fe]) using data from J-PLUS DR1. Methods. By combining recalibrated photometric data from J-PLUS DR1, Gaia DR2, and spectroscopic labels from LAMOST, we design and train a set of cost-sensitive neural networks, the CSNet, to learn the non-linear mapping from stellar colors to their labels. Special attention is paid to the poorly populated regions of the label space by giving different weights according to their density distribution.Results. We have achieved precisions of δ T eff ∼ 55 K, δ log g ∼ 0.15 dex, and δ [Fe/H] ∼ 0.07 dex, respectively, over a wide range of temperature, surface gravity, and metallicity. The uncertainties of the abundance estimates for [α/Fe] and the four individual elements are in the range 0.04-0.08 dex. We compare our parameter and abundance estimates with those from other spectroscopic catalogs such as APOGEE and GALAH, and find an overall good agreement. Conclusions. Our results demonstrate the potential of well-designed, high-quality photometric data for determinations of stellar parameters as well as individual elemental abundances. Applying the method to J-PLUS DR1, we have obtained the aforementioned parameters for about two million stars, providing an outstanding data set for chemo-dynamic analyses of the Milky Way. The catalog of the estimated parameters is publicly accessible.

J-PLUS: Support Vector Regression to Measure Stellar Parameters

“…We discuss the distribution of the regressed stellar parameters in Sect. 5.4, and compare our result with Yang et al (2021) in Sect. 5.5.…”

“…There may not exist a regressor that can properly fit the data for all the surveys, similar to the no-free-lunch theorem (a machine-learning algorithm that can solve every problem does not exist; Shalev-Shwartz & Ben-David 2014). This implies Bai et al 2018, Yang et al 2021, respectively. Bai et al 2018 only contains effective temperature regression.…”

“…The kernel scales with the lowest RMSEs are used for the training. We also test the mag(n-1)−mag(n) and mag1−mag(n) color (Yang et al 2021), and optimize the kernel scale with Bayesian optimizer. Table 1 presents the adopted kernel scales and the corresponding RMSEs, which are based on 10fold validation.…”

“…To determine the robustness of our predictions, we perform a comparison with the stellar parameter catalog from Yang et al (2021) 7 . We select the reliable stars in both catalog, which are stars in the interpolation catalog and the stars with stellar parameter FLAG=0 in Yang et al (2021). The cross-match yields 2,008,654 stars.…”

“…Lu & Li (2015) developed a scheme to achieve stellar parameters from the LASSO algorithm and Support Vector Regression (SVR) models. Yang et al (2021) designed a cost-sensitive artificial neural network and achieved stellar parameters from two million stars of J-PLUS first data release (DR1). developed a Stellar Parameters Estimation based on the Ensemble Methods pipeline (SPEEM), which is a stack of feature searching, normalization, and multi-output regressor.…”