Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC
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 | European Physical Journal C (Q1) | ||
| Dergi ISSN | 1434-6044 Dergi Bilgileri (2025) | ||
| Makale Dili | İngilizce | Basım Tarihi | 11-2025 |
| Cilt / Sayı / Sayfa | 85 / 11 / – | DOI | 10.1140/epjc/s10052-025-14713-w |
| Makale Linki | https://link.springer.com/article/10.1140/epjc/s10052-025-14713-w | ||
| UAK Araştırma Alanları |
Fen Bilimleri ve Matematik
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| Özet |
| We propose a neural network training method capable of accounting for the effects of systematic variations of the data model in the training process and describe its extension towards neural network multiclass classification. The procedure is evaluated on the realistic case of the measurement of Higgs boson production via gluon fusion and vector boson fusion in the\({\uptau}{\uptau}\) decay channel at the CMS experiment. The neural network output functions are used to infer the signal strengths for inclusive production of Higgs bosons as well as for their production via gluon fusion and vector boson fusion. We observe improvements of 12 and 16% in the uncertainty in the signal strengths for gluon and vector-boson fusion, respectively, compared with a conventional neural network training based on cross-entropy. |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 10 |