Camilla Penzo

Computer Vision Expert

Camilla Penzo

Thèse de doctorat

Galaxy and Structure Formation in Dynamical and Coupled Dark Energy (2015)

In this thesis I study the effects of different Dark Energy models on galaxy formation via numerical simulations. I investigate systems around and below Milky-Way masses and describe the effects of dark energy at galactic and sub-galactic scales. Firstly, I analyze high-resolution hydrodynamical simulations of three disc galaxies in dynamical dark energy models. While overall stellar feedback remains the driving mechanisms in shaping galaxies, the effect of the dark energy parametrization plays a larger role than previously thought. Secondly, I broaden the galaxy sample by simulating a 80 Mpc/h side cube of our universe using the same dynamical dark energy models. I show that resolution is a crucial ingredient so that baryonic feedback mechanisms can enhance differences between cosmological models. Thirdly, I investigate the effects of dynamical dark energy on dwarf mass scales. I find that there is more variation from object to object (due to the stochasticity of star formation at these scales) than between the same object in different cosmological models, which makes it hard for observations to disentangle different dark energy scenarios. In the second part of this thesis I investigate the effects of coupled dark energy models on galactic and sub-galactic scales via dark matter only high-resolution simulations. I find that coupled models decrease concentrations of (Milky-Way-like) parent haloes and also reduce the number of subhaloes orbiting around them. This improves the agreement with observations and, hence, makes these cosmologies attractive alternatives to a cosmological constant.

Thèmes de recherche


  • hate speech detection models for French language ;
  • detection of deep fake images ;
  • access to data/models for researchers and regulators ;
  • supervising two master students on their internship:
    • study of the alignment of multi-modal models and applications to fake news detection;
    • study of the methods for assessing bias in large language models, with a focus on BLOOM)
  • co-supervising a Ph.D. student, Augustin Godinot, on his work named Auditing the mutations of online AI models


  • dark energy modelling and its effects on galaxy formation ;
  • investigation of alternative models for dark energy ;
  • machine learning models applied to cosmological simulations ;
  • 3D deep learning models for populating halos in cosmological simulations ;
  • applications of deep learning to photoplethysmography (inferring volumetric variations of blood circulation from the video of the face of a person) ;
  • deep learning segmentations of 3D data from CT-scans of liver tumors.

Principales publications

  • Godinot, A., Tredan, G., Taïani, F., Penzo, C., “Change-Relaxed Active Fairness Auditing” in prep.
  • Famularo, S., Maini, C., Bortolotto, M., Penzo, C., “Deep learning for hepatocellular carcinoma, segmentation and recession rate from 3D convolutions.”, in prep.
  • Penzo, C., Macciò, A. V., Baldi, M., Casarini, L., Oñorbe, J., and Dutton, A. A., “Effects of coupled dark energy on the Milky Way and its satellites”, Monthly Notices of the Royal Astronomical Society, vol. 461, no. 3, pp. 2490–2501, 2016. doi:10.1093/mnras/stw1502.
  • Penzo, C., “Galaxy and structure formation in dynamical and coupled dark energy”, PhDT, 2015.
  • Penzo, C., Macciò, A. V., Casarini, L., Stinson, G. S., and Wadsley, J., “Dark MaGICC: the effect of dark energy on disc galaxy formation. Cosmology does matter”, Monthly Notices of the Royal Astronomical Society, vol. 442, no. 1, pp. 176–186, 2014. doi:10.1093/mnras/stu857.