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[ascl:1912.012] GAME: GAlaxy Machine learning for Emission lines

GAME infers different ISM physical properties by analyzing the emission line intensities in a galaxy spectrum. The code is trained with a large library of synthetic spectra spanning many different ISM phases, including HII (ionized) regions, PDRs, and neutral regions. GAME is based on a Supervised Machine Learning algorithm called AdaBoost with Decision Trees as base learner. Given a set of input lines in a spectrum, the code performs a training on the library and then evaluates the line intensities to give a determination of the physical properties. The errors on the input emission line intensities and the uncertainties on the physical properties determinations are also taken into account. GAME infers gas density, column density, far-ultraviolet (FUV, 6–13.6 eV) flux, ionization parameter, metallicity, escape fraction, and visual extinction. A web interface for using the code is available.

Code site:
https://github.com/grazianoucci/game
Described in:
https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.1144U https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1484U
Bibcode:
2019ascl.soft12012U

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ascl:1912.012
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