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

Autor:	Bhimji, Wahid, Farrell, Steven Andrew, Kurth, Thorsten, Paganini, Michela, Prabhat, Racah, Evan
Rok vydání:	2017
Předmět:	High Energy Physics - Experiment Computer Science - Distributed Parallel and Cluster Computing Computer Science - Learning Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	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. Comment: Presented at ACAT 2017 Conference, Submitted to J. Phys. Conf. Ser
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1711.03573 Zobrazit plný text záznamu View this record from Arxiv