Improving missing transverse momentum estimation with a deep neural network
Yazarlar (2)
CMS Collaboration
Prof. Dr. Hasan OĞUL Sinop Üniversitesi, Türkiye
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Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | PHYSICAL REVIEW D (Q1) | ||
| Dergi ISSN | 2470-0010 Dergi Bilgileri (2026) | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2026 |
| Kabul Tarihi | 20-03-2026 | Yayınlanma Tarihi | 21-04-2026 |
| Cilt / Sayı / Sayfa | 113 / 7 / 72010–0 | DOI | 10.1103/c4z7-tqvc |
| Makale Linki | https://doi.org/10.1103/c4z7-tqvc | ||
| UAK Araştırma Alanları |
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| Özet |
| At hadron colliders, the net transverse momentum of particles that do not interact with the detector (missing transverse momentum, ) is a crucial observable in many analyses. In the standard model, originates from neutrinos. Many beyond-the-standard-model particles, such as dark matter candidates, are also expected to leave the experimental apparatus undetected. This paper presents a novel deep neural network based estimator, deepmet, developed by the CMS Collaboration at the LHC. The deepmet algorithm produces a weight for each reconstructed particle based on its properties. The estimator is based on the negative vector sum of the weighted transverse momenta of all reconstructed particles in an event. Compared with other estimators currently employed by CMS, deepmet improves the resolution by 10%–30%, shows improvement for a wide range of final states, is easier to train … |
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BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 9 |