The Interstellar Medium of Galaxies: a Machine Learning Approach

Understanding the structure and physical properties of the Interstellar Medium (ISM) in galaxies, especially at high redshift, is one of the major drivers of galaxy formation studies. Measurements of key properties as gas density, column density, metallicity, ionization parameter, and Habing flux, rely on galaxy spectra obtained through the most advanced telescopes (both earth-based and space-borne) and, in particular, on their emission lines. However, finding diagnostics that are free of significant systematic uncertainties remains an unsolved problem. Several attempts have been made to recover ISM physical properties by mean of diagnostics based on small, pre-selected subsets of emission line ratios. Most of these previous works focused on ionized nebulae, and have obtained diagnostics for the physical properties of galaxies based only on the strongest nebular emission lines coming from extra-galactic HII regions and star-forming galaxies. The main purpose of this work is to reconstruct key ISM physical properties of galaxies from their spectra. The aim is to maximize the information that can be extracted from such data by using not only few specific and pre-selected emission lines, but the full information encoded in the spectra. This is now possible thanks to the combination of powerful Supervised Machine Learning (ml) algorithms and large synthetic spectra libraries. In order to achieve this goal, I have developed a code called game (GAlaxy Machine learning for Emission lines), a new fast method to reconstruct the ISM physical properties by using all the information carried by the emission lines intensities present in the available spectrum. The library included in this code covers a very large range of plausible ISM physical properties to accurately describe the physics both of ionized regions and of other phases (i.e neutral, molecular) of the ISM. The strength of the method relies on the fact that the ml algorithm can learn from all the lines present in a spectrum, including the weakest ones as those coming for example from neutral ISM components. I verified that with ml it is possible to set strong constraints on the properties of the different phases from observed spectra. game has been extensively tested, and shown to deliver excellent predictive performances when applied to synthetic spectra. A ml approach will become fundamental with upcoming high-quality spectra, including also faint lines of high-redshift galaxies, from new facilities such as the James Webb Space Telescope (JWST) and the Extremely Large Telescope (ELT). The astrophysical community will be therefore into an era where ml algorithms and Big Data Analytics will become extremely useful tools in the data-mining process. This is already the case for local observations where Integral Field Units (IFUs) are already able to provide observations containing tens of thousands of spaxels. A notable study case is the ISM of local Blue Compact Galaxies (BCGs), a subclass of dwarf galaxies. In fact, since BCGs are low-metallicity, compact, star-forming systems, they are thought to represent local analogues of early galaxies that will become soon observable in greater detail (e.g. with JWST). Thus, ISM studies of local BCGs can be used as benchmarks for understanding the structure, formation, and evolution of highredshift galaxies. In addition to a general description of the ml algorithm and the game code, I will show the first game results concerning the interpretation of high-quality IFU spectra of BCGs.