About me

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.

PhD thesis
Master's thesis
Full publication list
Google Scholar profile

First author publications

Ucci et al. (2020)

Astraeus II: Quantifying the impact of cosmic variance during the Epoch of Reionization

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.

Ucci et al. (2019)

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

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.

Ucci et al. (2018)

GAME: GAlaxy Machine learning for Emission lines

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.

Ucci et al. (2017)

Inferring physical properties of galaxies from their emission-line spectra

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.

Publicly available codes


(GAlaxy Machine learning for Emission lines)

Web platform: game.sns.it
GitHub: github.com/grazianoucci/game
Astrophysics Source Code Library: ascl.net/1912.012
Described in: Ucci et al. (2017), Ucci et al. (2018)

Cosmic variance calculator

GitHub: github.com/grazianoucci/cosmic_variance
Described in: Ucci et al. (2020)


(semi-numerical rAdiative tranSfer coupling of galaxy formaTion and Reionization in N-body dArk mattEr simUlationS)

GitHub: github.com/annehutter/astraeus
Astrophysics Source Code Library: ascl.net/2004.006
Described in: Hutter et al. (2020)


  • Address

    Kapteyn Astronomical Institute
    Rijksuniversiteit Groningen
    Landleven 12, 9747 AD
    Groningen (Netherlands)
  • Phone

    +31 (0)50 36 34073
  • Email