Bulletin of the American Physical Society
5th Joint Meeting of the APS Division of Nuclear Physics and the Physical Society of Japan
Volume 63, Number 12
Tuesday–Saturday, October 23–27, 2018; Waikoloa, Hawaii
Session HA: Conference Experience for Undergraduates Poster Session (2:00pm - 3:45pm)
Friday, October 26, 2018
Hilton Room: Grand Promenade
Abstract ID: BAPS.2018.HAW.HA.105
Abstract: HA.00105 : Machine Learning and Track Reconstruction for the MOLLER Experiment*
(William & Mary Coll)
(William & Mary Coll)
The purpose of this project was to develop a neural network to reconstruct particle trajectories in the MOLLER experiment. The MOLLER experiment is a collaboration at Jefferson Lab which plans on testing the Standard Model by measuring the parity-violating asymmetry in Moller scattering. If the measurement disagrees with the theoretical value for the asymmetry, then it will provide evidence for physics beyond the Standard Model, and if it agrees, it will restrict many beyond-the-Standard-Model theories being developed.
A necessary aspect of this experiment will be the reconstruction of particle trajectories. Neural networks are efficient pattern recognition tools and are equipped to handle large datasets, so they are a potential solution to this issue. Using data generated by the Geant 4 simulation for MOLLER, I trained a recurrent neural network which connects signals in a series of tracking detectors to signals in the main detector located downstream. It does so by predicting the position of a particle hit in the main detector from that particle's hit positions in each of the tracking planes. The network predicts the main detector hit positions with better than 1% error.
*This work was supported in part by the National Science Foundation under Grant No. PHY-1714792.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.HAW.HA.105
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