GAME: GAlaxy Machine learning for Emission lines

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Ada Coda
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GAME: GAlaxy Machine learning for Emission lines

Post by Ada Coda » Tue Dec 31, 2019 5:57 pm

GAME: GAlaxy Machine learning for Emission lines

Abstract: 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.

Credit: Ucci, Graziano

Site: https://github.com/grazianoucci/game
https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1484U

Bibcode: 2019ascl.soft12012U

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2017MNRAS.465.1144U and https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.1484U

ID: ascl:1912.012
Last edited by Ada Coda on Sun Mar 08, 2020 9:33 pm, edited 1 time in total.
Reason: Updated code entry.

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