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

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 and statistical analysis to calculate confidence intervals and quantify degeneracy. As an assessment of the code, several tests were conducted with known ice samples and constructed mixtures. A complete analysis of the Elias 29 spectrum was performed as well. Results. The ENIIGMA fitting tool can identify the correct ice samples and their fractions in all checks with known samples tested in this paper. Concerning the Elias 29 spectrum, the broad spectral range between 2.5-20 $\mu$m was successfully decomposed after continuum determination and silicate extraction. This analysis allowed the identification of different molecules in the ice mantle, including a tentative detection of CH$_3$CH$_2$OH. Conclusions. The ENIIGMA is a toolbox for spectroscopy analysis of infrared spectra that is well-timed with the launch of the James Webb Space Telescope. Additionally, it allows for exploring the different chemical environments and irradiation fields in order to correctly interpret astronomical observations.