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

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.

Figures (8)

• Figure 1: Kiel diagrams for the reference dataset, including LAMOST, APOGEE, and MIST (2 times), in order from top to bottom. The upper three panels display observed data from LAMOST and APOGEE, and theoretical models from MIST, limited to $T_{\rm eff}$ between 2000 and 14000 K. The bottom panel shows the full temperature range of the MIST models, from 2212 to 168845 K.
• Figure 2: Kiel diagrams showing the distribution of the nearly 5.6 million stars in our sample, separated by neighborhood. Panels are ordered from 1 ( top panel) to 10 ( bottom panel). The gray symbols represent the reference dataset. The color bar uses the same scale as in Fig. \ref{['Kiel']}, and [Fe/H] values range from $-$4.0 to +0.324 dex
• Figure 3: Bland-Altman diagrams for $T_{\rm eff}$ ( left panel), $\log{g}$ ( middle panel), and [Fe/H] ( right panel), calculated using 105 colors, in comparison to values reported for the validation data. The black dotted horizontal line represents their mean difference and the blue dotted horizontal lines are the mean difference plus or minus 1.96$\sigma$ to illustrate the confidence region. The color scheme represents the quality of the neighborhood proposed in our method, with yellow being the most reliable results (neighborhood 1) and red being the less reliable ones (neighborhood 10). The white contour lines are the densities calculated over the entire sample.
• Figure 4: Histograms of the differences of the stellar parameters estimated with 105 colors and the values reported for the validation data for $T_{\rm eff}$ ( Left panel), $\log{g}$ ( middle panel), and [Fe/H] ( right panel). The color scheme represents the different neighborhoods proposed in our method, with yellow being the most reliable results (neighborhood 1) and the less reliable ones (neighborhood 10). The red vertical line represents a null difference.
• Figure 5: Histograms of the differences in $T_{\rm eff}$ ( left panels), $\log{g}$ ( middle panels), and [Fe/H] ( right panels) between our results, obtained with 105 colors, and those from Wang2022 ( top panels) and Yang2024 ( bottom panels). Red vertical lines represent a null difference. The color bar is the same as the one shown in Fig. \ref{['Histograms105vsValData']}.