CaltechAUTHORS
  A Caltech Library Service

Jet Single Shot Detection

Pol, Adrian Alan and Aarrestad, Thea and Govorkova, Katya and Halily, Roi and Kopetz, Tal and Klempner, Anat and Loncar, Vladimir and Ngadiuba, Jennifer and Pierini, Maurizio and Sirkin, Olya and Summers, Sioni (2021) Jet Single Shot Detection. EPJ Web of Conferences, 251 . Art. No. 04027. ISSN 2100-014X. doi:10.1051/epjconf/202125104027. https://resolver.caltech.edu/CaltechAUTHORS:20210930-162946659

[img] PDF - Published Version
Creative Commons Attribution.

1MB
[img] PDF - Accepted Version
Creative Commons Attribution.

1MB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20210930-162946659

Abstract

We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of calorimeter cells and using a Single Shot Detection network, called Jet-SSD. The model performs simultaneous localization and classification and additional regression tasks to measure jet features. We investigate TernaryWeight Networks with weights constrained to {-1, 0, 1} times a layer- and channel-dependent scaling factors. We show that the quantized version of the network closely matches the performance of its full-precision equivalent.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1051/epjconf/202125104027DOIArticle
https://arxiv.org/abs/2105.05785arXivDiscussion Paper
ORCID:
AuthorORCID
Pol, Adrian Alan0000-0002-9034-0230
Aarrestad, Thea0000-0002-7671-243X
Ngadiuba, Jennifer0000-0002-0055-2935
Pierini, Maurizio0000-0003-1939-4268
Additional Information:© The Authors, published by EDP Sciences, 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Published online: 23 August 2021. A. A. P., M. P., S. S. and V. L. are supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no 772369). V. L. is supported by Zenseact under the CERN Knowledge Transfer Group. A. A. P. is supported by CEVA under the CERN Knowledge Transfer Group. We thank Simons Foundation, Flatiron Institute and Ian Fisk for granting access to computing resources used for this project.
Funders:
Funding AgencyGrant Number
European Research Council (ERC)772369
CERNUNSPECIFIED
Simons FoundationUNSPECIFIED
DOI:10.1051/epjconf/202125104027
Record Number:CaltechAUTHORS:20210930-162946659
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210930-162946659
Official Citation:Jet Single Shot Detection. Adrian Alan Pol, Thea Aarrestad, Katya Govorkova, Roi Halily, Tal Kopetz, Anat Klempner, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Olya Sirkin and Sioni Summers. EPJ Web Conf., 251 (2021) 04027; DOI: https://doi.org/10.1051/epjconf/202125104027
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111115
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Oct 2021 20:11
Last Modified:04 Oct 2021 20:11

Repository Staff Only: item control page