The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Jet-images — deep learning edition
Open accessAbstract
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physicallymotivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.
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Citations
Cites
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Cited by
Deep learning and its application to LHC physics
Guest, D., Cranmer, K., & Whiteson, D. (2018) · Annual Review of Nuclear and Particle Science · doi:10.1146/annurev-nucl-101917-021019 · Cited by 417
QCD-aware recursive neural networks for jet physics
Louppe, G., Cho, K., Becot, C., & Cranmer, K. (2019) · Journal of High Energy Physics · doi:10.1007/jhep01(2019)057 · Cited by 123
Piscopo, M. L., Spannowsky, M., & Waite, P. (2019) · Physical Review D · doi:10.1103/physrevd.100.016002 · Cited by 117
Metadata
- Authors
- de Oliveira, L., Kagan, M., Mackey, L., Nachman, B., & Schwartzman, A.
- Venue
- Journal of High Energy Physics
- Published
- 2016-07
- Volume
- 2016
- Issue
- 7
- ISSN
- 1029-8479 (ISSN-L)
- OpenAlex
- W2257617748
- Cited by
- 282
- References
- 1
- FWCI
- 14.69
- Type
- journal article
- Language
- en
- Open access
- diamond
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