Neutrino reconstruction in the DUNE experiment using Machine Learning



The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino oscillation experiment under construction in the US. A neutrino beam will be produced at the Fermi National Accelerator Laboratory (FNAL) in Illinois, by colliding accelerated protons into a target. Neutrino measurements will be performed with two detectors: a ‘Near Detector’ (ND), installed in FNAL, near the source of the neutrino beam; and a ‘Far Detector’ (FD), installed more than 1 km underground and 1,300 km downstream of the source, at the Sanford Underground Research Facility (SURF) in South Dakota. DUNE will combine the world’s most intense wide-band neutrino beam, a deep underground site, and massive detectors to enable a broad science program addressing some of the most fundamental questions in particle physics. Paramount among these is the origin of the matter-antimatter asymmetry in the Universe.    

Neutrinos are very difficult to study because they pass through most matter without leaving a trace. To identify them, enormous, high resolution detectors are needed, such as the DUNE FD. Next, critical to any physics result is the understanding of the neutrino interactions with the detector material, and separating physics processes versus different sources of background. The proposed PhD thesis project aims to explore two avenues:  to develop innovative algorithms to optimize the reconstruction of neutrino interactions in the DUNE FD, and to analyze data from the DUNE prototype detectors that are built at CERN. These systematic studies and calibrations, done with prototype data sets, are of paramount importance for any published physics result from the DUNE FD. The PhD student will work in close collaboration with fellow students from the University of Chicago, as well as with experts from the AstroParticle and Cosmology (APC) laboratory and the data intelligence institute of Paris (diiP). She/He will also be encouraged to present her/his work at international conferences and workshops, to gain visibility in the community, and get in contact with world experts on relevant topics.

Thesis funded by CNRS/IN2P3 - applications closed.



Camelia Mironov






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