Identification of tau leptons using a convolutional neural network with domain adaptation
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 (2025) | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-12-2025 |
| Cilt / Sayı / Sayfa | 20 / 12 / – | DOI | 10.1088/1748-0221/20/12/P12032 |
| Makale Linki | https://iopscience.iop.org/article/10.1088/1748-0221/20/12/P12032/meta | ||
| UAK Araştırma Alanları |
Nükleer Fizik
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| Özet |
| A tau lepton identification algorithm, DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τh) from quark or gluon jets and electrons and muons that are misreconstructed as τh candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τh candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τh candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in the DeepTau … |
| Anahtar Kelimeler |
| calibration and fitting methods | cluster finding | Large detector-systems performance; Pattern recognition | particle identification methods |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 2 |
| Google Scholar | 13 |