Contact
Eliot Genton
Position
PhD student
Address
Centre for Cosmology, Particle Physics and Phenomenology - CP3
Université catholique de Louvain
2, Chemin du Cyclotron - Box L7.01.05
B-1348 Louvain-la-Neuve
Belgium
Université catholique de Louvain
2, Chemin du Cyclotron - Box L7.01.05
B-1348 Louvain-la-Neuve
Belgium
Office
UCL member card
Multi-messenger Emission Study of the Solar Activity
In the field of neutrino astronomy, a discipline only a decade old but rapidly advancing, we find the sun as a focal point of study for its abundant neutrino emissions, pivotal in understanding various cosmic processes. This PhD project, under the joint supervision of Prof. de Wasseige from Université Catholique de Louvain and Prof. Argüelles from Harvard University, embarks on an ambitious journey to comprehensively analyze solar neutrino emissions across a wide energy spectrum, ranging from MeV to PeV. This analysis will span over 15 years, encompassing 1.5 solar cycles, and will pivot on three main steps. Initially, the project will conduct a global review of existing solar neutrino emission models, aiming to develop a convoluted, time-dependent prediction of the flux. The next phase involves an innovative approach in event selection, upgrading current methods to an all-flavour, energy-wide technique within the collaborative frameworks of the South Pole IceCube and the burgeoning KM3NeT telescope in the Mediterranean. The culmination of this research lies in constructing a detailed solar neutrino emission spectrum, achieved through multi-detector observations, thus enabling a nuanced, time-dependent multi-messenger study of solar activity, integrating data on gamma rays and cosmic rays. This project not only promises to fill the gaps in our understanding of solar neutrino emissions beyond the MeV range but also sets a precedent for future neutrino astronomy studies.
Dimuon Event Analysis in an IceCube like detector
In my ongoing project, I am utilizing Graph Neural Networks (GNNs) to identify dimuon events in the IceCube neutrino detector, a key component in studying neutrino interactions. These events, involving a muon-antimuon pair, are essential for understanding both standard and beyond standard model processes. My current efforts are focused on developing simulation data for these interactions, which includes integrating dimuons from deep inelastic scattering interactions into the Prometheus program.
The core of this project is to distinguish dimuon tracks from single muons using machine learning, with a particular emphasis on GNNs. These sophisticated models are adept at processing graph-structured data and are instrumental in classifying neutrino interaction data into specific interaction types. A significant challenge lies in structuring this data for the GNN, considering the high energy scales involved and the corresponding intense photon emissions in the detector. Ongoing analysis is leading towards the adoption of a unidirectional graph model, which will incorporate node values relating to the position and charge of photomultiplier tubes, and edge values based on spatial proximity. The eventual implementation of the GNN will likely combine graph convolutional and regular convolutional layers, aiming to efficiently process these complex data structures using the open source GraphNet graph neural network software for neutrino telescopes. This approach aims to demonstrate the IceCube detector's capability in measuring such interactions, offering a contrast to traditional methods that have been less conclusive.
The core of this project is to distinguish dimuon tracks from single muons using machine learning, with a particular emphasis on GNNs. These sophisticated models are adept at processing graph-structured data and are instrumental in classifying neutrino interaction data into specific interaction types. A significant challenge lies in structuring this data for the GNN, considering the high energy scales involved and the corresponding intense photon emissions in the detector. Ongoing analysis is leading towards the adoption of a unidirectional graph model, which will incorporate node values relating to the position and charge of photomultiplier tubes, and edge values based on spatial proximity. The eventual implementation of the GNN will likely combine graph convolutional and regular convolutional layers, aiming to efficiently process these complex data structures using the open source GraphNet graph neural network software for neutrino telescopes. This approach aims to demonstrate the IceCube detector's capability in measuring such interactions, offering a contrast to traditional methods that have been less conclusive.
Projects
Research directions:
Experiments and collaborations:
Active projects
Experiments and collaborations:
Active projects
Multi-messenger studies of astrophysical sources
Giacomo Bruno, Eliot Genton, Karlijn Kruiswijk, Mathieu Lamoureux, Jeff Lazar, Jonathan Mauro, Emile Moyaux, Christoph Raab, Leonardo Ricca, Marco Scarnera, Per Arne Sevle Myhr, Jishnu Suresh, Matthias Vereecken, Gwenhaël Wilberts Dewasseige
This project aims at studying astrophysical phenomena combining different messengers, mainly neutrinos, electromagnetic and gravitational waves.
This project aims at studying astrophysical phenomena combining different messengers, mainly neutrinos, electromagnetic and gravitational waves.
Neutrino physics and astrophysics
Eliot Genton, Karlijn Kruiswijk, Mathieu Lamoureux, Jeff Lazar, Vincent Lemaitre, Emile Moyaux, Christoph Raab, Leonardo Ricca, Marco Scarnera, Per Arne Sevle Myhr, Matthias Vereecken, Gwenhaël Wilberts Dewasseige
This project gathers researchers studying neutrino physics and astrophysics.
This project gathers researchers studying neutrino physics and astrophysics.
Neutrinos from astrophysical transient sources
Eliot Genton, Karlijn Kruiswijk, Mathieu Lamoureux, Jeff Lazar, Jonathan Mauro, Emile Moyaux, Christoph Raab, Leonardo Ricca, Marco Scarnera, Per Arne Sevle Myhr, Matthias Vereecken, Gwenhaël Wilberts Dewasseige
This project aims at optimising neutrino telescopes, especially the IceCube Neutrino Observatory and KM3NeT, to detect GeV and sub-GeV astrophysical neutrinos. The instruments are then used to search for low-energy neutrinos from transient sources, such as solar flares, compact binary mergers, or gamma-ray bursts.
This project aims at optimising neutrino telescopes, especially the IceCube Neutrino Observatory and KM3NeT, to detect GeV and sub-GeV astrophysical neutrinos. The instruments are then used to search for low-energy neutrinos from transient sources, such as solar flares, compact binary mergers, or gamma-ray bursts.
Publications in IRMP
All my publications on Inspire