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Context. A variety of laboratory ice spectra simulating different chemicalenvironments, ice morphology as well as thermal and energetic processing aredemanded to provide an accurate interpretation of the infrared spectra ofprotostars. To answer which combination of laboratory data best fit theobservations, an automated statistically-based computational approach becomesnecessary. Aims. To introduce a new approach, based on evolutionary algorithms,to search for molecules in ice mantles via spectral decomposition of infraredobservational data with laboratory ice spectra. Methods. A publicly availableand open-source fitting tool, called ENIIGMA (dEcompositioN of Infrared Icefeatures using Genetic Modelling Algorithms), is introduced. The tool hasdedicated Python functions to carry out continuum determination of theprotostellar spectra, silicate extraction, spectral decomposition andstatistical analysis to calculate confidence intervals and quantify degeneracy.As an assessment of the code, several tests were conducted with known icesamples and constructed mixtures. A complete analysis of the Elias 29 spectrumwas performed as well. Results. The ENIIGMA fitting tool can identify thecorrect ice samples and their fractions in all checks with known samples testedin this paper. Concerning the Elias 29 spectrum, the broad spectral rangebetween 2.5-20 $\mu$m was successfully decomposed after continuum determinationand silicate extraction. This analysis allowed the identification of differentmolecules in the ice mantle, including a tentative detection of CH$_3$CH$_2$OH.Conclusions. The ENIIGMA is a toolbox for spectroscopy analysis of infraredspectra that is well-timed with the launch of the James Webb Space Telescope.Additionally, it allows for exploring the different chemical environments andirradiation fields in order to correctly interpret astronomical observations.

Fitting infrared ice spectra with genetic modelling algorithms. Presenting the ENIIGMA fitting tool