Jet Single Shot Detection
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.
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.Attached Files
Published - epjconf_chep2021_04027.pdf
Accepted Version - 2105.05785.pdf
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Additional details
- Eprint ID
- 111115
- Resolver ID
- CaltechAUTHORS:20210930-162946659
- 772369
- European Research Council (ERC)
- CERN
- Simons Foundation
- Created
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2021-10-04Created from EPrint's datestamp field
- Updated
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2021-10-04Created from EPrint's last_modified field