Simulation and Track Reconstruction Techniques for the J-PARC muon g-2 experiment

The Muon g-2/EDM proposed experiment at J-PARC is a promising and innovative attempt at the field of Precision Physics. The sensitivity goal of 0.1 ppm will test the limits of our current understanding, and may probe for Beyond the Standard Model observations. This paper seeks out to investigate the computational techniques required by the experiment. The GEANT4 [1] framework was used to simulate the detector setup, according to the experiment’s Conceptual Design Report (CDR) [2]. This allowed to observe the event hierarchy in different energies, generate signal hit data, and construct an event-selection algorithm. ROOT and GDML enabled us to use the geometry and parsed output data in a platform-independent way. Using techniques pertaining to Machine Learning and Image Feature extraction, such as the Canny Edge detection and the Hough Transform, we were able to construct a generic representation of ‘track families’ from each event category. Finally, the modular GENFIT2 [3] framework was used to implement the Kalman Filter [4] along with an Deterministic Annealing Filter (DAF) [5] and the Runge-Kutta stepper to reconstruct tracks from a few digitized, smeared singular event data

Simulation and Track Reconstruction Techniques for the J-PARC muon g-2 experiment

The Muon g-2/EDM proposed experiment at J-PARC is a promising and innovative attempt at the field of Precision Physics. The sensitivity goal of 0.1 ppm will test the limits of our current understanding, and may probe for Beyond the Standard Model observations. This paper seeks out to investigate the computational techniques required by the experiment. The GEANT4 [1] framework was used to simulate the detector setup, according to the experiment’s Conceptual Design Report (CDR) [2]. This allowed to observe the event hierarchy in different energies, generate signal hit data, and construct an event-selection algorithm. ROOT and GDML enabled us to use the geometry and parsed output data in a platform-independent way. Using techniques pertaining to Machine Learning and Image Feature extraction, such as the Canny Edge detection and the Hough Transform, we were able to construct a generic representation of ‘track families’ from each event category. Finally, the modular GENFIT2 [3] framework was used to implement the Kalman Filter [4] along with an Deterministic Annealing Filter (DAF) [5] and the Runge-Kutta stepper to reconstruct tracks from a few digitized, smeared singular event data