General information
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
SL-DRF-24-0271
Direction
DRF
Thesis topic details
Category
Technological challenges
Thesis topics
Inverse Problems in Astrophysics and Machine Learning
Contract
Thèse
Job description
AI (artificial intelligence) is significantly changing the way we solve inverse problems in astrophysics.
In radio interferometry, the detection of radio sources and their classification require taking into account numerous effects such as non-Gaussian noise, incomplete sampling of Fourier space, and the need to construct a sufficient data set for the training. The difficulty increases when the source to be reconstructed evolves over time. Such examples of temporal variations are found in various inverse problems in astrophysics such as transient objects (supernovae, fast radio burst, etc.). ARGOS is a pilot study for a radio interferometer that will perform real-time continuous wide-field observations in centimetre wavelengths. The combination of a wide field of view with high sensitivity will allow ARGOS to detect transient sources that vary on timescales shorter than one second. ARGOS will be able to detect thousands of fast radio bursts per year. These events will need to be accurately differentiated from other transient sources detected by ARGOS, such as supernovae, gamma-ray bursts, white dwarfs, neutron stars, blazars, etc. Given the short time scales of some of these transient events and the need for quick follow up, ARGOS will require state-of-the-art classification solutions employing cutting-edge machine learning architectures. This thesis consists of developing innovative tools from machine learning to solve image reconstruction and source classification problems.
University / doctoral school
Thesis topic location
Site
Saclay
Requester
Position start date
01/01/2024
Person to be contacted by the applicant
Farrens Samuel samuel.farrens@cea.fr
CEA
DRF/IRFU/DAP/LCS
Ormes des Merisiers
Département d’Astrophysique (DAp) Bât 709
91191 Gif-sur-Yvette
28377
Tutor / Responsible thesis director
STARCK Jean-Luc jstarck@cea.fr
CEA
DRF/IRFU/DAP/LCS
CEA/Saclay, Orme des Merisiers
01 69 08 57 64
En savoir plus
http://jstarck.cosmostat.org
http://www.cosmostat.org