Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
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 (2020) | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2020 |
| Cilt / Sayı / Sayfa | 15 / 6 / – | DOI | 10.1088/1748-0221/15/06/P06005 |
| Makale Linki | https://arxiv.org/abs/2004.08262 | ||
| UAK Araştırma Alanları |
Fen Bilimleri ve Matematik
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| Özet |
| Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at 13 TeV, corresponding to an integrated luminosity of 35.9 fb. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency. |
| Anahtar Kelimeler |
| Large detector-systems performance | Pattern recognition, cluster finding, calibration and fitting methods |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 127 |
| Scopus | 152 |
| Google Scholar | 453 |