Extracting stellar emissivity via a machine learning analysis of MSX and LAMOST catalog data

Abstract

Many astronomical studies model stars like radiating blackbodies with unit emissivity. Their conclusions should be reconsidered if the stellar spectral emissivity, ${ϵ}_{\ensuremath{\lambda}}$, were to be proven to be appreciably smaller. However, determining ${ϵ}_{\ensuremath{\lambda}}$ from raw observational data poses serious technical challenges. Here, using a machine learning technique, we implemented an inverse model for calculating the stellar spectral radiation flux in a given spectral band emissivity. Radiation flux data in some spectral bands serve as input to determine the unknown model parameters. To this purpose, we chose 411 stars (361 from the Midcourse Space Experiment (MSX) catalog and 50 from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) catalog) as training samples of a stochastic particle swarm optimization algorithm. The mean values of the emissivity estimates thus obtained deviate significantly from the ideal blackbody value. Knowledge of the model parameters then enabled us to calculate the radiation fluxes in other spectral bands to compare with the existing observational data and thus validate our approach. Finally, based on the trained algorithm, we discuss our predictions for spectral bands where astronomical data are unavailable. Besides providing direct evidence against modeling stars as emitting blackbodies, our conclusions also call for more direct investigations of the stellar emissivity.