Amine Announ

PhD Student

Resarch activities at CPHT: Theory of Plasmas

Thesis : Study and characterisation of the solar atmosphere for the DKIST and Solar Orbiter observations
Advisors :
Tahar Amari (CPHT/CNRS), thesis supervisor
Éric Buchlin (IAS/CNRS), co-responsible for data analysis
Nicolas Tar (NSO/USA), scientific collaborator for the DKIST data

Keywords

Heating the solar corona
Structures of the quiet/active Sun
Magnetic reconnection
Magnetic waves and energy transport
Magnetic field - plasma coupling

Abstract

The solar atmosphere is a highly structured, magnetized plasma system characterized by a steep temperature gradient, rising from approximately 6,000 K at the photosphere to over one million Kelvin in the corona within a few thousand kilometers. This striking thermal profile results from complex and not fully understood heating processes involving the interplay between plasma dynamics, magnetic fields, and energy transport across multiple spatial and temporal scales. The governing physical framework for this system is magnetohydrodynamics (MHD), which models the plasma as a conducting fluid coupled with electromagnetic fields. The solar atmosphere manifests a wide range of dynamic phenomena—such as spicules, jets, magnetic reconnection events, waves, and turbulent cascades—whose interactions shape the energy and contribute to coronal heating. The challenge lies in resolving and understanding these nonlinear, multiscale MHD processes, many of which occur in partially ionized, stratified, and highly anisotropic plasma environments with strong coupling between magnetic and kinetic energies. Direct in situ measurements are limited, especially in the lower atmospheric layers where heating mechanisms are initiated, while remote sensing provides indirect diagnostics constrained by line-of-sight integration and limited magnetic field measurements, primarily at the photosphere. This observational limitation motivates the use of advanced numerical simulations that solve the full set of MHD equations at high resolution, capturing small- to large-scale processes in a self-consistent manner. Moreover, the high dimensionality and complexity of these models necessitate the integration of modern scientific machine learning methods, such as physics-informed neural networks (PINNs) and data-driven surrogate modeling, to enhance computational efficiency, enable parameter inference, and extract interpretable physical insights from both simulations and observational data. This thesis aims to develop and apply MHD modeling frameworks, coupled with Solar Orbiter (ESA) and DKIST (Daniel K. Inouye Solar Telescope) datasets, to characterize and quantify the plasma dynamics and magnetic energy conversion processes responsible for heating the solar atmosphere.