Sep 5 – 10, 2021
Europe/Lisbon timezone

Machine Learning for Background Hit Rejection in the Mu2e Straw Tracker

Sep 7, 2021, 6:44 PM


Poster Tests of symmetries and conservation laws Poster Session II


Mr Digvijay Roy Varier (University of California, Berkeley)


The Mu2e experiment at Fermilab will search for charged lepton flavor violation (CLFV) via muon to electron conversion, with a goal of improving the previous upper limit four orders of magnitude and reaching unprecedented single-event sensitivities. The signal of CLFV conversion is a ~105 MeV electron, which is detected using a high-precision straw tracker. Protons produced by muon capture can create highly ionizing straw hits. Identifying and removing these hits can enhance the reconstruction efficiency. Through a poster, I will discuss improving the rejection of this background by replacing a simple cut on the energy deposited in the straw with a TMVA-based machine learning algorithm. In particular, it is found that a neural network using the ADC waveform shape and Time-Over-Threshold significantly improves both the signal electron acceptance and proton rejection efficiency.

Primary author

Mr Digvijay Roy Varier (University of California, Berkeley)


Dr Richard Bonventre (Lawrence Berkeley National Lab )

Presentation materials