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Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC

Nguyen, T. Q. and Weitekamp, D., III and Anderson, D. and Castello, R. and Cerri, O. and Pierini, M. and Spiropulu, M. and Vlimant, J. R. (2019) Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC. Computing and Software for Big Science, 3 (1). Art. No. 12. ISSN 2510-2036. doi:10.1007/s41781-019-0028-1.

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We show how an event topology classification based on deep learning could be used to improve the purity of data samples selected in real time 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 more than one order of magnitude. 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 translate 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:Article
Related URLs:
URLURL TypeDescription ReadCube access Paper
Nguyen, T. Q.0000-0003-3954-5131
Anderson, D.0000-0001-8173-3182
Cerri, O.0000-0002-2191-0666
Pierini, M.0000-0003-1939-4268
Spiropulu, M.0000-0001-8172-7081
Vlimant, J. R.0000-0002-9705-101X
Additional Information:© Springer Nature Switzerland AG 2019. Received: 1 August 2018; Accepted: 21 August 2019; Published online: 31 August 2019. This work is supported by Grants from the Swiss National Supercomputing Center (CSCS) under project ID d59, the United States Department of Energy, Office of High Energy Physics Research under Caltech Contract No. DE-SC0011925, and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement no. 772369). T.N. would like to thank Duc Le for valuable discussions during the earlier stage of this project. We thank CERN OpenLab for supporting D.W. 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”. On behalf of all authors, the corresponding author states that there is no conflict of interest.
Funding AgencyGrant Number
Swiss National Supercomputing Centerd59
Department of Energy (DOE)DE-SC0011925
European Research Council (ERC)772369
Kavli FoundationUNSPECIFIED
Subject Keywords:Trigger; Deep learning; Topology classification; LHC
Issue or Number:1
Record Number:CaltechAUTHORS:20180730-092555025
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Official Citation:Nguyen, T.Q., Weitekamp, D., Anderson, D. et al. Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC. Comput Softw Big Sci 3, 12 (2019).
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:88348
Deposited By: Tony Diaz
Deposited On:30 Jul 2018 16:36
Last Modified:16 Nov 2021 00:26

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