Tracking in the COMET Experiment using Machine Learning

by Ewen Gillies (Imperial College London)

Thursday 3 May 2018, 10:00 → 11:00 Asia/Shanghai

B326 (IHEP)

The Coherent Muon to Electron Transition (COMET) experiment is designed to search for muon to electron conversion, a process which has very good sensitivity to Beyond the Standard Model physics. The first phase of the experiment is currently under construction at J-PARC. This phase is designed to probe muon to electron conversion 100 times better than the current limit. The experiment will achieve this sensitivity by directing a high intensity muon beam at a stopping target. The detectors probe the resulting events for the signal 105 MeV electron from muon to electron conversion. A boosted decision tree (BDT) algorithm has been developed to find this signal track. This BDT is used to combine energy deposition and timing information with a reweighted inverse hough transform to filter out background hits. The signal hits are then passed to the track fitting algorithm. Results show that using a BDT significantly improves in background hit rejection when compared to traditional, cut-based hit rejection methods. At 99% signal hit retention, a cut on the energy deposition is able to remove 80% of background hits. By combining more multiple features, the BDT is able to remove 99% of background hits while still retaining 99% of signal hits.

CV EG_RESUME_2018.pdf

Slides 2018-05-03_EwenG_IHEP_Seminar_2018.pdf