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

Code

• github.com/NERSC/pytorch-examples
  pytorch★ 0
• github.com/vmos1/atlas_cnn
  tf★ 0
• github.com/sparticlesteve/pytorch-examples
  pytorch★ 0
• github.com/vpayyar/layered_CNNs_for_ATLAS_data
  tf★ 0
• github.com/sparticlesteve/cori-intml-examples
  tf★ 0

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.

Tasks

Reproductions