Speaker
Lyssa Dale-Davies
(University of Toronto)
Description
The Super Cryogenic Dark Matter Search (SuperCDMS) aims to directly detect dark matter using cryogenic crystal detectors and superconducting sensors. Predicting the position and energy of particle detection events is an essential part of the experiment, and the complexity of the data makes machine learning (ML) techniques ideal. We discuss the use of ML tools and detector modelling to make accurate predictions, as well as the challenges presented by the unique geometry and physics of the detectors.
| What area of study best describes your talk? | Analysis |
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Authors
Ms
Cary Kan
(University of Toronto)
Lyssa Dale-Davies
(University of Toronto)
Simon Cao
(University of Toronto)