Neutrino physics is one of the most exciting topics in contemporary physics, leading to two Nobel prizes in the last 20 years for the detection of cosmic neutrinos and the discovery that neutrinos have mass. The massive nature of neutrinos is arguably the strongest indication of physics beyond the Standard Model of particle physics, opening a number of fundamental questions: What is the mechanism for neutrino mass generation? Are neutrinos responsible of the matter-antimatter imbalance in the universe? Can neutrinos tell us something about the unification of fundamental forces? Do neutrinos feel the quantum nature of space-time?

A new generation of neutrino experiments is in the horizon looking to explore many of these still open questions on neutrino properties and searching for astrophysical sources. Among them is KM3NeT [1], a multi-Megaton detector under construction in the Mediterranean Sea. The detectors consist of arrays of Photo-Multiplier Tubes (PMTs) submerged in the deepest regions of the Mediterranean, where few cosmic rays can reach and the clear seawater provides a huge natural target for neutrino interactions. The detectors are being built in two configurations: a smaller and more densely instrumented detector near Toulon, France, called ORCA, which will focus on observing atmospheric neutrinos to measure fundamental neutrino properties, and a sparser and larger detector near Capo Passero, Italy, called ARCA, which will focus on searching for astrophysical sources of neutrinos. The ORCA detector is currently taking data with 13% of its planned instrumentation in place (15 Detector Units (DUs)), and construction is moving ahead quickly with ~20% expected to be deployed by the end of 2023 and the full detector is planned by 2026. The ARCA detector is also under construction and is expected to be completed by 2027.

The main focus of the proposed research will be the study of atmospheric neutrino oscillations with the ORCA detector. With the detectors growing rapidly, ORCA is expected to perform its first measurement of the Neutrino Mass Ordering (NMO) in the timescale of the thesis. The candidate will play a central role in developing this first NMO analysis. Additionally, they will be responsible for advancing novel Deep Learning techniques for neutrino interaction reconstruction in KM3NeT [2]. These techniques borrow from state-of-the-art computer vision algorithms to analyse the rich structure of neutrino interaction data, looking to reconstruct basic properties such as energy, momentum, flavour, and inelasticity. A key objective of the research will be to implement the concept of domain adaptation to mitigate the impact of mismodelling in the KM3NeT simulation. This work will give continuity to a project developed in partnership with the Data Intelligence Institute of Paris (diiP) in which domain adaptation was first introduce in the framework of the KM3NeT collaboration [3].

The KM3NeT group at APC is strongly involved in the analysis of neutrino oscillations with ORCA, as well as instrumentation activities on the Calibration Unit, characterization of the Digital Optical Modules, and searching for astrophysical neutrinos with ORCA and ARCA. We are one of the largest groups in KM3NeT with high visibility in the collaboration, comprising 10 permanent physicists, 7 technical staff, 2 postdocs and 4 PhD students. The successful candidate will join our team measuring neutrino properties with ORCA. With the experiment already in data-taking mode, the candidate will be expected to take part in the many aspects of data processing, calibration and monitoring. The position is based at APC and will be co-supervised by Dr. Joao Coelho and Prof. Antoine Kouchner. Frequent interaction with the KM3NeT Collaboration is expected, including regular meetings, and contributing to the installation, operation and maintenance of the detectors. Pending the resolution of the current COVID-19 pandemic, many of these interactions will require frequent travel to collaborating institutions and control centres, amounting to 3-4 times a year. Some experience with programming in C++ and/or Python, and particle physics data analysis would be highly appreciated.

References:

[1] KM3NeT Collaboration, Letter of Intent for KM3NeT Phase 2 J.Phys. G43(2016) no.8, 084001

[2] KM3NeT Collaboration, Event reconstruction for KM3NeT/ORCA using convolutional neural networks, JINST 15 P10005 (2020)

[3] S. Liang, J. Coelho and S. P. Martínez, Data/MC adaptation with adversarial training in KM3NeT, https://indico.in2p3.fr/event/27507/contributions/114507/