Research

Stellar feedback in nearby galaxies

Nearby galaxies are ideal laboratories to study the processes of star formation and stellar feedback at high spatial resolution and therefore uncover the small-scale physics driving galactic evolution. I study the matter cycle in nearby galaxies following a multi-wavelength approach, mostly within the PHANGS (Physics at high angular resolution in nearby galaxies) international collaboration.

PHANGS has recently obtained high-resolution observational data for nearby massive, main-sequence galaxies across the electromagnetic spectrum via a variety of large legacy programs on ALMA (sub-mm), MUSE/VLT (optical spectroscopy), HST (UV-optical imaging), and JWST (near and mid-IR imaging). To complement PHANGS I am also collecting data on Local Group (M33) and low-metallicity dwarf galaxies.

I use observations of nearby galaxies to study stellar feedback, the physics of outflows, the origin of the diffuse ionized gas, tracers of star formation on cloud scales, scaling relation between gas mass, stellar mass and star formation rate, and determination of accurate chemical abundances via auroral lines.

Team members involved: Matilde Brazzini (MSci student, now PhD student INAF-TS), Anna Feltre (postdoc), Francesco Chiti (MSci student)
Key Collaborators: Eric Emsellem (ESO), Eva Schinnerer (MPIA), Kathryn Kreckel (Heidelberg), Adam Leroy (OSU)

Recent Team Papers:

• Calibrating mid-infrared emission as a tracer of obscured star formation on H II-region scales in the era of JWST, Belfiore et al. (2023), A&A 678, A129.
• Metallicity calibrations based on auroral lines from PHANGS–MUSE data, Brazzini et al. (2024), A&A 691, A173.

Galaxies at Cosmic Noon

The process of star formation was most vigorous during a period know as Cosmic Noon (z = 1-2). Despite their enhanced star formation rate (SFR) and clumpy appearance, galaxies at Cosmic Noon commonly host rotating discs, albeit with large velocity dispersion. They live in a state of self-regulated equilibrium, as demonstrated by the existence of a tight relation between mass, SFR, molecular gas, and gas-phase metallicity.

Integral field spectroscopy targeting rest-frame optical emission lines has been the key tool in unveiling the star formation, dynamical, and chemical properties of galaxies at Cosmic Noon. In collaboration with the infrared team at MPE, I am leading an INAF GTO program with the ERIS instrument on the VLT to study star formation and chemical abundances at kpc-scales in Cosmic Noon galaxies.

To gain a more detailed understanding of the ISM in Cosmic Noon galaxies I am also using some of the deepest spectroscopic data acquired by JWST as part of the MARTA program (PI: Curti).

Team members involved: Avinanda Chakraborty (postdoc), Elisa Cataldi (PhD student), Bianca Moreschini (PhD student).
Key Collaborators: Giovanni Cresci (INAF), Filippo Mannucci (INAF), Natascha Förster-Schreiber (MPE), Mirko Curti (ESO)

Recent Team Papers:

• MARTA: Temperature-temperature relationships and strong-line metallicity calibrations from multiple auroral lines detections at cosmic noon, Cataldi et al. (2025).

Big data and Machine Learning

My work is fundamentally grounded in the analysys of large astronomical datasets. I am an expert in large spectroscopic surveys (e.g. SDSS-IV MaNGA, PHANGS-MUSE) and I have played a leading role in the development of data analysis tools for spectroscopic data. I am involved in planning future spectroscipic surveys including the MOONRISE survey, and future facilities like the Wide-field Spectroscopic Telescope.

I work on applying machine learning algorithms to the analysis of spectral data, both in 1d and 3d (integral field spectroscopy). As part of a collaborative team at INAF-Arcetri I am exploring the use of invertible neural networks and domain adpation techniques to obtain reliable uncertainties, cross the gap between data and models, and infer accurate physical parameters from data.

Team members involved: Caterina Bracci (PhD student)
Key Collaborators: Michele Ginolfi (UniFI), Germano Sacco (INAF), Nils Candebat (INAF).

Recent Team Papers:

• Classifying spectra of emission-line regions with neural networks -- An application to integral field spectroscopic data of M33, Bracci et al. (2025), accepted.
• Machine learning the gap between real and simulated nebulae: A domain-adaptation approach to classify ionised nebulae in nearby galaxies, Belfiore et al. (2025), A&A, 694, A212.