Benjamin Remy
PHD STUDENT
| Contact Information | |
| E-mail: | benjamin.remy [at] cea [dot] fr |
| Phone: | |
| Office: | 274 |
| Affiliation: | IRFU/DAp-AIM |
| Supervisors: | François Lanusse, Jean-Luc Starck |
Research Interests
Joint Estimation of Cosmic Shear, PSF, and Galaxy Morphologies
The goal of my PhD thesis is to develop a hierarchical probabilistic model of the observed Euclid images combining physical models with Deep Learning components accounting for unknowns factors. In particular, I aim to build a forward model of Euclid field of views accounting for the PSF, cosmic shear, and galaxy morphology. Fitting this model to observed exposures is a theoretically optimal way to jointly estimate the cosmic shear field and perform the calibration.
Variational Inference and Hierarchical models
So far, solving such inference problem at scale was intractable. I am very interested in efficient optimization-based inference approaches, such as Variational Inference, replacing expensive Markov Chain Monte Carlo methods, to solve this problem.
Publications
- Probabilistic Mass-Mapping with Neural Score Estimation
Benjamin Remy, François Lanusse, Niall Jeffrey, Jia Liu, and Jean-Luc Starck, Ken Osato, Tim Schrabback
accepted at Astronomy & Astrophysics
(arXiv, code)
Workshops proceedings
- Towards solving model bias in cosmic shear forward modeling
Benjamin Remy, François Lanusse and Jean-Luc Starck.
Machine Learning and the Physical Sciences Workshop, NeurIPS 2022.
(arXiv) - Neural Posterior Estimation with Differentiable Simulators
Justine Zeghal, François Lanusse, Alewandre Boucaud, Benjamin Remy and Eric Aubourg
Workshop on Machine Learning for Astrophysics, ICML 2022.
(arXiv) - Probabilistic Mapping of Dark Matter by Neural Score Matching
Benjamin Remy, François Lanusse, Zaccharie Ramzi, Jia Liu, Niall Jeffrey and Jean-Luc Starck.
Machine Learning and the Physical Sciences Workshop, NeurIPS 2020.
(arXiv, code, poster) - Denoising Score-Matching for Uncertainty Quantification in Inverse Problems
Zaccharie Ramzi, Benjamin Remy, François Lanusse, Jean-Luc Starck and Philippe Ciuciu
Deep Learning and Inverse Problems Workshop, NeurIPS 2020.
(arXiv)
Talks
-
Astromerique speaker series, University of Montreal, 27th Sep 2022 (invited)
-
Learning to Discover, Institut Pascal, Saclay, 29th Apr 2022.
-
Likelihood-free in Paris, Paris, 21st Apr 2022.
-
CosmoClub, ETH Zurich Cosmology group, remote, Feb 2022 (invited)
- Recontres de Moriond Cosmology, La Thuile, 29th Jan 2022.
-
Udopia Doctoral Students Day, Central Supélec, Gif-sur-Yvette, Dec 2021.
- Machine learning in astronomical surveys conference. IAP, Paris, Oct 2021.
- IN2P3/IRFU Machine Learning Workshop, March 17th, online. Slides
- Euclid Workshop on Machine Learning and Deep learning. December, 14th 2020, online
- Denoising Score Matching for Uncertainty Quantification in Inverse Problems: Application to gravitational lensing and Magnetic Resonance Imaging, with Zaccharie Ramzi. Machine Learning Club, Nov 18th 2020, online.
Set of slides at https://github.com/b-remy/talks.