Description du sujet de thèse

The neutrino is still one of the less well known particle of the Standard Model. In the last few decades, a large number of experiments have established neutrino flavour mixing and that neutrino are massive particles which which lead to two Nobel Prizes and a Physics Breakthrough price. The picture of neutrino oscillation remains incomplete and a large international effort is underway to complete our understanding of the role of the neutrino in the Universe. In the coming decade new experiments aim to shed more light on the nature of the neutrino, the ordering of the mass states and the CP violation parameters driving matter-antimatter asymmetries.

In this context the KM3NeT/ORCA observatory in the Mediterranean sea aim to measure the mass ordering of the neutrinos using cosmic ray-produced neutrinos in the atmosphere using our planet structure as a way to select upwards high energy neutrinos and the sea as one of the largest instrumented volume of water. The KM3NeT/ORCA detector sits at 2500 m on the sea floor and will comprise 115 lines equipped with photodetector modules to register the light cascades of high energy particle interactions.

We propose an exciting research project to develop the capabilities of the KM3NeT/ORCA analysis with the development of statistical methods to measure the neutrino oscillation parameters and novel machine learning (ML) techniques to reconstruct observable quantities and learn about the detector response as it grows in size and capability.

In this PhD you will play a central role in:
• extending further the work on the neutrino mass ordering analysis, exploring bayesian techniques and other modern statistical tools to improve on the physics reach of the experiment.
• You will learn cutting edge ML techniques to optimise and extend the range of ML tools available to the KM3NeT collaboration.
You will be working in a dynamic, fast-growing group of four experienced researchers, a post-doc and a PhD student. You will be expected to travel to collaboration meetings and to contribute to the construction and operation of the observatory.

You should have a M2 level in subatomic physics or equivalent, should have knowledge in nuclear physics, particle physics and radiation-matter interactions. Good level in mathematics and some knowledge of ML Python libraries would be a good starting point as well as very good Spoken English and communication skills.
During the thesis the student will receive the necessary training in experimental physicists in fundamental physics (neutrino) as well as broader skills in simulation and data analysis.

Contexte de travail

The successful candidate will be assigned to the nFacet team, within the Laboratoire de Physique Corpusculaire de Caen (LPCC - UMR6534).
The LPC CAEN, with about 90 staff, is a joint research unit (UMR6534) depending on three supervisory bodies: the CNRS, the University of Caen Normandy (UNICAEN) and the Ecole Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN). It is located on Campus 2 of the University of Caen Normandy (Campus Côte de Nacre) and is part of the research park of ENSICAEN (www.lpc-caen.in2p3.fr/).

Contraintes et risques

Frequent Travel in France and abroad is expected.

Informations complémentaires

This contract is funded by the "RIN 2022 Chaire Excellence : 00120837-22E03518 _ ALPHA, Région Normandie".