The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Deep-learning top taggers or the end of QCD?
Open accessAbstract
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.
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Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Cited by
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
- Kasieczka, G., Plehn, T., Russell, M., & Schell, T.
- Venue
- Journal of High Energy Physics
- Published
- 2017-05
- Volume
- 2017
- Issue
- 5
- ISSN
- 1029-8479 (ISSN-L)
- OpenAlex
- W2586557507
- Cited by
- 276
- References
- 1
- FWCI
- 19.19
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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