| KEYWORD |
Greedy Optimal Sampling for Solar Inverse Problems
keywords APPROXIMATION THEORY, DATA-DRIVEN LEARNING
Reference persons EMMA PERRACCHIONE
Description Given a set of highly sparse data in the Fourier domain, the inversion of the Fourier transform from limited data is a challenging numerical issue whose success strongly depends on the samples' number and location. On the other hand, in real applications, the
number and locations of such samples are often constrained by the hardware of the instruments. Hence, the thesis ambition is to develop numerical schemes based on greedy approaches for an optimal choice of these samples. Such a theoretical study finds a natural application in the field of astronomical imaging. Specifically, space telescopes for solar hard X-ray imaging principally aim at
observing big explosions on the solar surface (called solar flares) and providing indirect observations (called visibilities) made of sampled Fourier components of the incoming photon flux.
Deadline 27/01/2024
PROPONI LA TUA CANDIDATURA