The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Deep-learned Top Tagging with a Lorentz Layer
Open accessAbstract
We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric.With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information.We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.
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Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Cited by
Larkoski, A. J., Moult, I., & Nachman, B. (2020) · Physics Reports · doi:10.1016/j.physrep.2019.11.001 · Cited by 361
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
- Butter, A., Kasieczka, G., Plehn, T., & Russell, M.
- Venue
- SciPost Physics
- Publisher
- Stichting SciPost
- Published
- 2018-09-26
- Volume
- 5
- Issue
- 3
- ISSN
- 2542-4653 (ISSN-L)
- OpenAlex
- W2787586057
- Cited by
- 193
- References
- 1
- FWCI
- 15.81
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Butter, A., Kasieczka, G., Plehn, T., & Russell, M. (2018). Deep-learned Top Tagging with a Lorentz Layer. SciPost Physics. https://doi.org/10.21468/scipostphys.5.3.028
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