Learning about stars from their colors

Abstract

We pose the question of how much information on the atmospheric parameters of late-type stars can be retrieved purely from colors using standard photometric systems. We carried out numerical experiments using stellar fluxes from model atmospheres, injecting random noise before analyzing them. We examined the presence of degeneracies among atmospheric parameters, and evaluated how well the parameters are extracted depending on the number and wavelength span of the photometric filters available, from the UV GALEX to the mid-IR WISE passbands. We also considered spectrophotometry from the Gaia mission. We find that stellar effective temperatures can be determined accurately ($\sigma \sim$ 0.01 dex or about 150 K) when reddening is negligible or known, based merely on optical photometry, and the accuracy can be improved twofold by including IR data. On the other hand, stellar metallicities and surface gravities are fairly unconstrained from optical or IR photometryy. However, our ability to retrieve these parameters can improve significantly by adding UV photometry. When reddening is considered a free parameter, assuming it can be modeled perfectly, our experiments suggest that it can be disentangled from the rest of the parameters. This theoretical study indicates that combining broad-band photometry from the UV to the mid-IR allows atmospheric parameters and interstellar extinction to be determined with fair accuracy. The use of UV passbands helps substantially to derive metallicities and surface gravities, as well as to break the degeneracy between effective temperature and reddening. The Gaia BP/RP data can disentangle all the parameters, provided the stellar SEDs are modeled reasonably well.

7 pages, 3 figures, to appear in A&A

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Cited by in corpus (7)

• J-PLUS: The Javalambre Photometric Local Universe Survey
• A collection of model stellar spectra for spectral types B to early-M
• Machine learning Applied to Star-Galaxy-QSO Classification and Stellar Effective Temperature Regression
• Information content of BP/RP spectra in Gaia DR3
• Mining S-PLUS for Metal-Poor Stars in the Milky Way
• Chemodynamical Properties and Ages of Metal-Poor Stars in S-PLUS
• An Unsupervised Machine Learning Approach to Identify Spectral Energy Distribution Outliers: Application to the S-PLUS DR4 data