Hyperspectral data fusion and source separation for X-ray astrophysics

Julia Lascar (CEA Paris Saclay)
June 07, 2024 — 11:00 — "new L2S location (IBM building), 3rd floor room" (and Teams)

Abstract

In astrophysics, X-ray telescopes can collect cubes of data called hyperspectral images. These data cubes have two spatial dimensions, and one spectral dimension. The subject of my thesis is to implement algorithms to analyze X-ray hyperspectral images of supernovae remnants. In particular, I have implemented a non-stationary unmixing algorithm (unmixing where each endmember varies spectrally), and a fusion algorithm to obtain the best resolutions from two generation of telescopes. The aim of my thesis is to combine the two, and thus obtain an algorithm that unmixes and fuses hyperspectral data simultaneously.

Article: https://arxiv.org/abs/2404.03490

Bio

Julia Lascar is a 2nd year PhD student working at the CEA with Jérôme Bobin and Fabio Acero. Her research focuses on implementing signal processing algorithms applied to hyperspectral X-ray astrophysics, with a particular interest in supernova remnants. She has a Master’s degree in astrophysics from McGill University, Montreal, and a M2 in statistics from ENSAI, Rennes. Before that, she studied Physics and Philosophy at King’s College London.