A deep neural network to search for new long-lived particles decaying to jets
Yazarlar (1)
Prof. Dr. Hasan OĞUL Sinop Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Machine Learning: Science and Technology | ||
| Makale Dili | – | Basım Tarihi | 09-2020 |
| Cilt / Sayı / Sayfa | 1 / 3 / 35012–0 | DOI | 10.1088/2632-2153/ab9023/meta |
| Makale Linki | https://iopscience.iop.org/article/10.1088/2632-2153/ab9023/meta | ||
| UAK Araştırma Alanları |
Nükleer Fizik
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| Özet |
| A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length c τ 0 of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The potential performance of the tagger is … |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 96 |