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Topology classification with deep learning to improve real-time event selection at the LHC

Nguyen, Thong Q. and Weitekamp, Daniel, III and Anderson, Dustin and Castello, Roberto and Cerri, Olmo and Pierini, Maurizio and Spiropulu, Maria and Vlimant, Jean-Roch (2018) Topology classification with deep learning to improve real-time event selection at the LHC. . (Submitted) http://resolver.caltech.edu/CaltechAUTHORS:20180730-092555025

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Abstract

We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and high-level features are utilized. In the considered examples, a filter based on the classifier's score can be trained to retain ~99% of the interesting events and reduce the false-positive rate by as much as one order of magnitude for certain background processes. By operating such a filter as part of the online event selection infrastructure of the LHC experiments, one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives. The saved resources could be translated into a reduction of the detector operation cost or into an effective increase of storage and processing capabilities, which could be reinvested to extend the physics reach of the LHC experiments.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://arxiv.org/abs/1807.00083arXivDiscussion Paper
ORCID:
AuthorORCID
Spiropulu, Maria0000-0001-8172-7081
Additional Information:This work is partially supported by a grant from the Swiss National Supercomputing Center (CSCS) under project ID d59. We thank CERN OpenLab for supporting DW during his internship at CERN. We are grateful to Caltech and the Kavli Foundation for their support of undergraduate student research in cross-cutting areas of machine learning and domain sciences. Part of this work was conducted at "iBanks", the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of "iBanks". This project is partially supported by the United States Department of Energy, Office of High Energy Physics Research under Caltech Contract No. de-sc0011925. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no 772369).
Group:CMS@Caltech
Funders:
Funding AgencyGrant Number
Swiss National Supercomputing Centerd59
CERNUNSPECIFIED
CaltechUNSPECIFIED
Kavli FoundationUNSPECIFIED
Department of Energy (DOE)DE-SC0011925
European Research Council (ERC)772369
Record Number:CaltechAUTHORS:20180730-092555025
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180730-092555025
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:88348
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:30 Jul 2018 16:36
Last Modified:30 Jul 2018 16:36

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