Large-Scale Deep Learning for Multi-Jet Event Classification

TL;DR

This paper demonstrates large-scale deep learning using HPC for physics analysis, specifically employing CNNs on CMS detector data to improve event classification of RPV SUSY signals over traditional methods.

Contribution

It introduces a scalable CNN approach for particle physics data analysis, leveraging HPC resources for large-scale training and demonstrating improved classification performance.

Findings

CNN outperforms cut-based methods in signal efficiency and significance

Model training achieved scalability up to 1024 HPC nodes

Utilized HPC to handle large-scale deep learning for physics analysis

Abstract

We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level information as images considering the geometry of the CMS detector and use this model to discriminate \textit{R}-parity violating super symmetry (RPV SUSY) events from the background events with inelastic quantum process from the Standard Model (QCD multi-jet). We compare the classification performance of the CNN method with that of the widely used cut-based method. The signal efficiency (and expected significance) of the CNN method is 1.85 (1.2) times higher than that of the cut-based method. To speed-up the training, the model training is conducted using the Nurion HPC system at the Korea Institute of Science and Technology Information, which is equipped with thousands of parallel \texttt{Xeon Phi} CPUs. Notably, our CNN model shows scalability up to 1024 nodes.