Fitting infrared ice spectra with genetic modelling algorithms. Presenting the ENIIGMA fitting tool
Will R. M. Rocha, Giulia Perotti, Lars E. Kristensen, Jes K., J{\o}rgensen

TL;DR
The paper introduces ENIIGMA, an open-source genetic algorithm-based tool for spectral decomposition of infrared ice spectra, enabling identification of molecular compositions in protostellar ice mantles with high accuracy.
Contribution
It presents a novel, automated spectral fitting tool using evolutionary algorithms, specifically designed for analyzing infrared ice spectra from astronomical observations.
Findings
ENIIGMA accurately identifies ice components in known samples.
Successful decomposition of Elias 29 spectrum revealed molecular compositions.
Tentative detection of CH$_3$CH$_2$OH in ice mantle.
Abstract
Context. A variety of laboratory ice spectra simulating different chemical environments, ice morphology as well as thermal and energetic processing are demanded to provide an accurate interpretation of the infrared spectra of protostars. To answer which combination of laboratory data best fit the observations, an automated statistically-based computational approach becomes necessary. Aims. To introduce a new approach, based on evolutionary algorithms, to search for molecules in ice mantles via spectral decomposition of infrared observational data with laboratory ice spectra. Methods. A publicly available and open-source fitting tool, called ENIIGMA (dEcompositioN of Infrared Ice features using Genetic Modelling Algorithms), is introduced. The tool has dedicated Python functions to carry out continuum determination of the protostellar spectra, silicate extraction, spectral decomposition…
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Taxonomy
TopicsSAS software applications and methods
