Speaker
Samuel Kohn
(Lawrence Berkeley National Lab. (US))
Description
The use of up-to-date machine learning methods, including deep neural networks, running directly on raw data has significant potential in High Energy Physics for revealing patterns in detector signals and as a result improving reconstruction and the sensitivity of the final physics analyses. In this work, we describe a machine-learning analysis pipeline developed and operating at the National Energy Research Scientific Computing Center (NERSC), processing data from the Daya Bay Neutrino Experiment. We apply convolutional neural networks to raw data from Daya Bay in an unsupervised mode where no input physics knowledge or training labels are used.
| Primary Keyword (Mandatory) | Artificial intelligence/Machine learning |
|---|---|
| Secondary Keyword (Optional) | Analysis tools and techniques |
| Tertiary Keyword (Optional) | High performance computing |
Primary author
Mr
Evan Racah
(Lawrence Berkeley National Lab. (US))
Co-authors
Craig Tull
(LBNL/ATLAS)
Dan Dwyer
(Lawrence Berkeley National Lab)
Lisa Gerhardt
(LBNL)
Mr Prabhat
(Lawrence Berkeley National Lab. (US))
Peter Sadowski
(University of California Irvine)
Samuel Kohn
(Lawrence Berkeley National Lab. (US))
Thorsten Kurth
(Lawrence Berkeley National Lab. (US))
Wahid Bhimji
(Lawrence Berkeley National Lab. (US))
