I am a Postdoctoral Researcher at the Kapteyn Astronomical Institute in Groningen (Netherlands). My research focuses on the galaxy evolution in the early Universe and on the physical properties of the Interstellar Medium especially at high redshift. In particular, using my expertise on Data Analysis, I am currently working on cosmological simulations to study galaxy formation and evolution during the Epoch of Reionization. I aim to make a difference through my eagerness to learn and my commitment. I deeply believe in a scientific research free, open and transparent.
First author publications
We used the ASTRAEUS framework, that couples galaxy formation and reionization, to estimate the cosmic variance expected in the UV Luminosity Function (UV LF) and the Stellar Mass Function (SMF) in JWST and WFIRST surveys. We studied the UV LF faint-end slope and the environments (in terms of density and ionization fields) of Lyman Break Galaxies (LBGs) during the EoR. We did also provide a public software tool to compute cosmic variance for different redshifts and survey areas.
In this work we applied the Machine Learning 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.
Here we presented an updated and optimized version of the GAME code. The improvements concern (a) an enlarged spectral library including Pop III stars, (b) the inclusion of spectral noise in the Machine Learning training, and (c) an accurate evaluation of uncertainties. We extensively validated GAME and compared its performance against empirical methods and other available emission line codes on a sample of 62 SDSS stacked galaxy spectra and 75 observed HII regions.
In this work, we presented a new approach based on Supervised Machine Learning algorithms to infer key physical properties of galaxies (density, metallicity, column density and ionization parameter) from their emission-line spectra. We introduced and tested extensively a numerical code (GAME, GAlaxy Machine learning for Emission lines) implementing this method.