Magnetar formation: from amplification to relaxation of the most extreme magnetic fields
Magnetars are neutron stars with the strongest magnetic fields known in the Universe, observed as high-energy galactic sources. The formation of these objects is one of the most studied scenarios to explain some of the most violent explosions: superluminous supernovae, hypernovae, and gamma-ray bursts. In recent years, our team has succeeded in numerically reproducing magnetic fields of magnetar-like intensities by simulating dynamo amplification mechanisms that develop in the proto-neutron star during the first seconds after the collapse of the progenitor core. However, most observational manifestations of magnetars require the magnetic field to survive over much longer timescales (from a few weeks for super-luminous supernovae to thousands of years for Galactic magnetars). This thesis will consist of developing 3D numerical simulations of magnetic field relaxation initialized from different dynamo states previously calculated by the team, extending them to later stages after the birth of the neutron star when the dynamo is no longer active. The student will thus determine how the turbulent magnetic field generated in the first few seconds will evolve to eventually reach a stable equilibrium state, whose topology will be characterized and compared with observations.
Understanding the origin of the remarkable efficiency of distant galaxy formation
The James Webb Space Telescope is revolutionizing our understanding of the distant universe. A result has emerged that challenges our models: the extremely high efficiency of star formation in distant galaxies. However, this finding is derived indirectly: we measure the mass of stars in galaxies, not their star formation rate. This is the main weakness of the James Webb. The aim of this thesis is to remedy this weakness by using its angular resolution capacity, which has not been taken into account until now, in order to obtain a more robust measurement of the SFR of distant galaxies. We will deduce a law that will improve the robustness of SFR determination using morphological properties and combining data from the James Webb Space Telescope with data from ALMA (z=1-3). We will then apply it to the distant universe (z=3-6, part 2) and use it as a benchmark for numerical simulations (part 3).
Exotic shape of the nucleus: decay spectroscopy of neutron-deficient actinides with the detector SEASON
The question of the limit of stability of nuclei, both in terms of proton/neutron asymmetry and in terms of mass, is an important open question in modern nuclear physics. In the region of heavy nuclei, the neutron-deficient actinides present a great interest. Indeed, strong octupolar deformation, giving a pear shape to the nuclei, are predicted and have event been already observed in some isotopes. These deformations seem to play a key role for nuclear stability, for nuclear decay modes, and may also be related to physics beyond the standard model. The main goal oh this thesis will be to pursue the systematic study of these deformations by making use of the brand-new SEASON detector, whose first experiment will take place at the University of Jyväskylä (Finland) in February 2026. The thesis will focus on the analysis of data from the experimental campaign that will occur in summer 2026. Several experiments are foreseen, making use of different beam-target combinations to produce actinides by fusion-evaporation reaction. These actinides will then be sent inside SEASON to perform their decay spectroscopy. Depending on the plannings, another campaign could be scheduled at Jyväskylä in 2027. Finally, the return of the instrument in France to be set up at GANIL-Spiral2 (Caen) coupled to the S3 spectrometer will certainly take place this the thesis period.
The thesis can be co-directed by the university of Jyväskylä.
TRANSFORMER: from the genealogy of dark matter halos to the baryonic properties of galaxy clusters.
The thesis proposes to predict the baryonic properties of galaxy clusters based on the history of dark matter halo formation, using innovative neural networks (Transformers). The work will involve intensive numerical simulations. This project falls within the general framework of determining cosmological parameters through the observation of galaxy clusters in X-rays. It is directly linked to the international Heritage programme in the XMM-Euclid FornaX deep field.
Machine Learning-Based Algorithms for Real-Time Standalone Tracking in the Upstream Pixel Detector at LHCb
This PhD aims to develop and optimize next-generation track reconstruction capabilities for the LHCb experiment at the Large Hadron Collider (LHC) through the exploration of advanced machine learning (ML) algorithms. The newly installed Upstream Pixel (UP) detector, located upstream of the LHCb magnet, will play a crucial role from Run 5 onward by rapidly identifying track candidates and reducing fake tracks at the earliest stages of reconstruction, particularly in high-occupancy environments.
Achieving fast and highly efficient tracking is essential to fulfill LHCb’s rich physics program, which spans rare decays, CP-violation studies in the Standard Model, and the characterization of the quark–gluon plasma in nucleus–nucleus collisions. However, the increasing event rates and data complexity expected for future data-taking phases will impose major constraints on current tracking algorithms, especially in heavy-ion collisions where thousands of charged particles may be produced per event.
In this context, we will investigate modern ML-based approaches for standalone tracking in the UP detector. Successful applications in the LHCb VELO tracking system already demonstrate the potential of such methods. In particular, Graph Neural Networks (GNNs) are a promising solution for exploiting the geometric correlations between detector hits, allowing for improved tracking efficiency and fake-rate suppression, while maintaining scalability at high multiplicity.
The PhD program will first focus on the development of a realistic GEANT4 simulation of the UP detector to generate ML-suitable datasets and study detector performance. The next phase will consist in designing, training, and benchmarking advanced ML algorithms for standalone tracking, followed by their optimization for real-time GPU-based execution within the Allen trigger and reconstruction framework. The most efficient solutions will be integrated and validated inside the official LHCb software stack, ensuring compatibility with existing data pipelines and direct applicability to Run-5 operation.
Overall, the thesis will provide a major contribution to the real-time reconstruction performance of LHCb, preparing the experiment for the challenges of future high-luminosity and heavy-ion running.
Numerical Study of Interstellar Turbulence in the Exascale Era
This PhD project aims to better understand interstellar medium turbulence, a key phenomenon governing the formation of stars and galactic structures. This turbulence—magnetized, supersonic, and multiphase—influences how energy is transferred and dissipated, thereby regulating the efficiency of star formation throughout the history of the Universe. Its study is complex, as it involves a wide range of spatial and temporal scales that are difficult to reproduce numerically. Advances in high-performance computing, particularly the advent of GPU-based exascale supercomputers, now make it possible to perform much more refined simulations.
The Dyablo code, developed at IRFU, will be used to carry out large-scale three-dimensional simulations with adaptive mesh refinement to resolve the regions where energy dissipation occurs. The study will progress in stages: first, simulations of simple isothermal flows will be conducted, followed by models that include heating, cooling, magnetic fields, and gravity. The turbulent properties will be analyzed using power spectra, structure functions, and density distributions, in order to better understand the formation of dense regions that give birth to stars. Finally, the work will be extended to the galactic scale, in collaboration with other French institutes, to investigate the large-scale energy cascade of turbulence across entire galaxies.
Monitoring criticality risk through neutron noise in degraded nuclear environments
Our team at CEA/Irfu is working with ASNR to study the possibility of using neutron noise measurements, i.e., stochastic variations in neutron flux, to estimate the reactivity of subcritical nuclear systems. The aim is to propose this technique for online measurement of the reactivity of the corium at Fukushima Daiichi during future decommissioning operations. The thesis work will focus on evaluating a solution based on Micromegas-type neutron detectors (nBLM detectors) developed by IRFU, which are adapted to the extreme gamma radiation expected in the vicinity of the Fukushima Daiichi corium. The student will participate in experiments at nuclear research facilities in Europe and the United States to test this technical solution and measure neutron noise for a wide range of reactivities. He/she will be responsible for analyzing the data and evaluating the various inversion methods used to estimate reactivity from neutron noise measurements.
Measurement of low lying dipole excitations using neutron inelastic scattering
The pygmy dipole resonance is a vibration mode observed in neutron-rich nuclei and which has initially been described as the oscillation of a neutron skin against a symmetric core in term of proton and neutron numbers. But experimental studies have revealed a more complex structure. Few years ago, we have proposed to take benefit of the high intensity neutron flux from SPIRAL2-NFS to study the pygmy resonance with an original approach: the neutron inelastic scattering. Following the success of the first experiment carried out in 2022, we propose to continue our program in a new region of the nuclear chart. The objective of the thesis is to study the pygmy dipole resonance in 88Sr by inelastic neutron scattering. The thesis will consist of: i) participation in the experiment, ii) data analysis, and iii) interpretation of the results in collaboration with theorists.
Mining LEP data for fragmentation: A TMD-oriented analysis of pi+pi- pairs in e+e- collisions
This project aims to advance our understanding of quark and gluon fragmentation by performing the first-ever extraction of Transverse-Momentum-Dependent Fragmentation Functions (TMDFFs) for charged pions using archived data from LEP experiments like DELPHI or ALEPH.
Fragmentation Functions, which describe how partons form detectable hadrons, are non-perturbative and must be determined from experimental data. TMDFFs provide more detailed information about the transverse momentum of these hadrons. An ideal process to study them is the production of back-to-back pi+pi- pairs in electron-positron annihilations, a measurement surprisingly absent from both past and current experiments.
The project will leverage CERN OpenData initiative to access this historical data. The work is structured in three key steps: first, overcoming the technical challenge of accessing the data using potentially obsolete software; second, extracting relevant physical distributions, such as the transverse momentum of the pion pairs; and third, using Monte Carlo simulations (e.g., Pythia8) to interpret the results.
A crucial part of the analysis will be to identify the observables most sensitive to TMDFFs through simulations. The final data analysis will employ modern techniques to ensure a robust estimate of all uncertainties. Once completed, this pioneering measurement will be incorporated into a global analysis of TMD data, significantly improving the accuracy of TMDFFs and pushing the boundaries of our knowledge of non-perturbative QCD.