Identification of hadronic tau lepton decays using a deep neural network
Yazarlar (1)
Prof. Dr. Hasan OĞUL Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Journal of Instrumentation (Q3) | ||
| Dergi ISSN | 1748-0221 Dergi Bilgileri (2022) | ||
| Makale Dili | İngilizce | Basım Tarihi | 07-2022 |
| Cilt / Sayı / Sayfa | 17 / 7 / – | DOI | 10.1088/1748-0221/17/07/P07023 |
| Makale Linki | http://www.scopus.com/inward/record.url?eid=2-s2.0-85137022092&partnerID=MN8TOARS | ||
| UAK Araştırma Alanları |
Fen Bilimleri ve Matematik
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| Özet |
| A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (τ h) that originate from genuine tau leptons in the CMS detector against τ h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a τ h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine τ h to pass the discriminator against jets increases by 10–30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient τ h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and … |
| Anahtar Kelimeler |
| calibration and fitting methods | cluster finding | Large detector systems for particle and astroparticle physics | Particle identification methods | Pattern recognition |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 72 |
| Scopus | 70 |
| Google Scholar | 296 |