Aug 21 – 25, 2017
University of Washington, Seattle
US/Pacific timezone

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC

Aug 21, 2017, 5:45 PM
107 (Alder Hall)


Alder Hall

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools


Wahid Bhimji (Lawrence Berkeley National Lab. (US))


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 evaluate different approaches to make whole-detector deep-learning tractable. We explore CNNs and alternative architectures 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 both 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.

Primary authors

Wahid Bhimji (Lawrence Berkeley National Lab. (US)) Steven Andrew Farrell (Lawrence Berkeley National Lab. (US)) Thorsten Kurth (Unknown) Mr Evan Racah (LBL) Mr Prabhat (Lawrence Berkeley National Laboratory) Michela Paganini (Yale University (US))

Presentation materials

Peer reviewing