| Makale Türü |
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| Dergi Adı | MACHINE LEARNING-SCIENCE AND TECHNOLOGY (Q1) | ||
| Dergi ISSN | 2632-2153 Dergi Bilgileri (2026) | ||
| Makale Dili | – | Basım Tarihi | 08-2026 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 06-07-2026 |
| Cilt / Sayı / Sayfa | 7 / 4 / – | DOI | 10.1088/2632-2153/ae7d87 |
| Makale Linki | https://hal.science/hal-05443385/ | ||
| UAK Araştırma Alanları |
Nükleer Fizik
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| Özet |
| Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles. The effectiveness of these approaches in enhancing sensitivity to various signals is studied and compared using data collected in proton-proton collisions at a center-of-mass energy of 13 TeV. In an example analysis, the capabilities of anomaly detection methods are further demonstrated by identifying large-radius jets consistent with Lorentz-boosted hadronically decaying top quarks in a model-agnostic framework. |
| Anahtar Kelimeler |
| CMS | machine learning | anomaly | dijet | resonance |
| Atıf Sayıları | |
| Google Scholar | 3 |
| Dergi Adı | Machine Learning-Science and Technology |
| Kısa Adı | MACH LEARN-SCI TECHN |
| Yayıncı | IOP Publishing Ltd |
| Açık Erişim | Evet |
| ISSN | 2632-2153 |
| E-ISSN | 2632-2153 |
| Wos Quartile | Q1 |
| Scopus Quartile | Q2 |
| Tarandığı Indeksler | SCIE , Scopus |
| WoS Kategoriler | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MULTIDISCIPLINARY SCIENCES |
| Scopus Kategoriler | ARTIFICIAL INTELLIGENCE | HUMAN-COMPUTER INTERACTION | SOFTWARE |