Name
Title | ||
|---|---|---|
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC |
Abstract | ||
|---|---|---|
There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter, and for identifying objects or particular particle types. We explore approaches that use the entire calorimeter, combined with track information, for directly conducting physics analyses: i.e. classifying events as known-physics background or new-physics signals. We use an existing RPV-Supersymmetry analysis as a case study and explore CNNs on multi-channel, high-resolution sparse images: applied on GPU and multi-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes) on the Cori supercomputer at NERSC. We compare statistical performance of our approaches with selections on high-level physics variables from the current physics analyses, and shallow classifiers trained on those variables. We also compare time-to-solution performance of CPU (scaling to multiple KNL nodes) and GPU implementations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1088/1742-6596/1085/4/042034 | Journal of Physics Conference Series |
Field | DocType | Volume |
Calorimeter,Large Hadron Collider,Supercomputer,Xeon Phi,Particle physics experiments,Computational science,Atlas (anatomy),Artificial neural network,Detector,Physics,Particle physics | Journal | 1085 |
ISSN | Citations | PageRank |
1742-6588 | 1 | 0.35 |
References | Authors | |
1 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wahid Bhimji | 1 | 26 | 4.06 |
| Steven Andrew Farrell | 2 | 1 | 0.35 |
| Thorsten Kurth | 3 | 57 | 8.36 |
| Michela Paganini | 4 | 9 | 3.08 |
| Prabhat | 5 | 456 | 34.79 |
| Evan Racah | 6 | 54 | 5.35 |