Neural-network model trained on lab ice spectra predicts fractional composition of H2O, CO, CO2, CH3OH, NH3, and CH4 from 2.5-10 micron IR absorption with typical 3% error and was validated on two JWST background-star spectra.
I.1.Spectra taken by JWST towards NIR38 in the molecular cloud Cha I, converted to absorbance scale after fitting the stellar continuum and the silicate contribution
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A fast machine learning tool to predict the composition of astronomical ices from infrared absorption spectra
Neural-network model trained on lab ice spectra predicts fractional composition of H2O, CO, CO2, CH3OH, NH3, and CH4 from 2.5-10 micron IR absorption with typical 3% error and was validated on two JWST background-star spectra.