Speaker
Simon Cao
(University of Toronto)
Description
In the Super Cryogenic Dark Matter Search (SuperCDMS), we use machine learning algorithms to predict meaningful physical quantities, such as position and energy, from the signals produced by a particle detection event. However, many detectors are broken, resulting in incomplete data. We show that it is still possible to predict physical information about events recorded by a broken detector, bringing data previously thought useless into a new light.
| What area of study best describes your talk? | Analysis |
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Author
Simon Cao
(University of Toronto)
Co-authors
Dr
Madeleine Zurowski
(Department of Physics, University of Toronto)
Prof.
Pekka Sinervo
(Department of Physics, University of Toronto)
Mr
Warren Perry
(Department of Physics, University of Toronto)