The first part of my PhD thesis is dedicated to validating the DeepMass method (Deep learning to reconstruct a Bayesian estimate of dark matter maps from weak lensing data) in a Euclid context, and extending it to spherical data.
Indeed one of the current limitations of the method is that it does not account for the curvature of the sky, and is limited to small patches.
A second part of this thesis will be focused on the development of the tools necessary to exploit the cosmological information contained in these maps. The first step will be to compress full-sky maps into a small number of summary statistics, the second step will be to use these statistics to constrain cosmology, using simulation-based inference techniques.
I am part of the Dark Energy Science Collaboration where I contribute studying higher order weak lensing statistics using differentiable simulations.
Conferences & Talks
- IN2P3/IRFU Machine Learning workshop, virtual, Paris, 16-17 March 2021 (talk).
- Rubin-LSST France, virtual, Paris, 28 May 2021 (talk).
- Machine Learning for the study of galaxies and cosmology (PNCG), virtual, 9 June 2021 (talk).
- 2021 July LSST-DESC Collaboration Meeting, virtual, 19-23 July 2021 (poster).
Attended Courses & Workshops
- Summer School in Statistics for Astronomers XVI, virtual, 1-5 June 2021
- Weak Lensing Power Spectrum Tutorial , virtual, 2020
I joined to a double degree program between the University of Naples Federico II and the Shanghai Normal University in 2019.
I graduated in March 2020 and obtained a MSc in Physics, majoring Astrophysics, at University of Naples Federico II. My Master thesis focused on the study of the Concentration-Mass relation of galaxy clusters in the next generation surveys.
From September to December 2019, I had a 3-month research experience as visiting student at Shanghai Normal University, where I developed part of my Master thesis.