The interstellar medium of dwarf galaxies: new insights from Machine Learning analysis of emission-line spectra

Abstract

Dwarf galaxies are ideal laboratories to study the physics of the interstellar medium (ISM). Emission lines have been widely used to this aim. Retrieving the full information encoded in the spectra is therefore essential. This can be efficiently and reliably done using Machine Learning (ML) algorithms. Here, we apply the ML code GAME to MUSE (Multi Unit Spectroscopic Explorer) and PMAS (Potsdam Multi Aperture Spectrophotometer) integral field unit observations of two nearby blue compact galaxies: Henize 2-10 and IZw18. We derive spatially resolved maps of several key ISM physical properties. We find that both galaxies show a remarkably uniform metallicity distribution. Henize 2-10 is a star-forming-dominated galaxy, with a star formation rate (SFR) of about 1.2 M☉ yr-1. Henize 2-10 features dense and dusty (AV up to 5-7 mag) star-forming central sites. We find IZw18 to be very metal-poor (Z = 1/20 Z☉). IZw18 has a strong interstellar radiation field, with a large ionization parameter. We also use models of PopIII stars spectral energy distribution as a possible ionizing source for the He II λ4686 emission detected in the IZw18 NW component. We find that PopIII stars could provide a significant contribution to the line intensity. The upper limit to the PopIII star formation is 52 per cent of the total IZw18 SFR.