A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution
Yazarlar (1)
Prof. Dr. Hasan OĞUL Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
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| Dergi Adı | Computing and Software for Big Science | ||
| Dergi ISSN | 2510-2044 Dergi Bilgileri (2020) | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2020 |
| Cilt / Sayı / Sayfa | 4 / 1 / – | DOI | 10.1007/s41781-020-00041-z |
| Makale Linki | https://link.springer.com/content/pdf/10.1007/s41781-020-00041-z.pdf | ||
| UAK Araştırma Alanları |
Fen Bilimleri ve Matematik
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| Özet |
| We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of 13 TeV at the CERN LHC. The algorithm is trained on a large simulated sample of b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb. A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to . |
| Anahtar Kelimeler |
| b jets | CMS | Deep learning | Higgs boson | Jet energy | Jet resolution |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 37 |
| Google Scholar | 144 |