zELDA. II. Reconstruction of galactic Lyman-alpha spectra attenuated by the intergalactic medium using neural networks

The observed Lyman-alpha ( line profile is a convolution of the complex radiative transfer taking place in the interstellar, circumgalactic, and intergalactic media (ISM, CGM, and IGM, respectively). Discerning the different components of the line is crucial in order to use it as a probe of galaxy formation or the evolution of the IGM. We aim to present the second version of (redshift Estimator for Line profiles of Distant Lyman-Alpha emitters), an open-source Python module focused on modelling and fitting observed line profiles. This new version of focuses on disentangling the galactic from the IGM effects. We built realistic line profiles that include the ISM and IGM contributions by combining the Monte Carlo radiative-transfer simulations for the so-called shell model (ISM) and IGM transmission curves generated from TNG100. We used these mock line profiles to train different artificial neural networks. These use the observed spectrum as input and the outflow parameters of the best fitting `shell model' as output along with the redshift and emission IGM escape fraction of the source. We measured the accuracy of on mock line profiles. We find that is capable of reconstructing the ISM emerging line profile with high levels of accuracy (Kolmogórov-Smirnov<0.1) for 95% of the cases for HST/COS-like observations and 80% for MUSE-WIDE-like observations. is able to measure the IGM transmission with typical uncertainties below 10% for HST/COS and MUSE-WIDE data. This work represents a step forward in the high-precision reconstruction of IGM-attenuated line profiles. allows the disentanglement of the galactic and IGM contribution shaping the line shape and thus allows us to use as a tool to study galaxy and ISM evolution.