Reweighting simulated events using machine-learning techniques in the CMS experiment
Yazarlar (1)
Prof. Dr. Hasan OĞUL Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | European Physical Journal C (Q1) | ||
| Dergi ISSN | 1434-6044 Dergi Bilgileri (2025) | ||
| Makale Dili | İngilizce | Basım Tarihi | 05-2025 |
| Cilt / Sayı / Sayfa | 85 / 5 / – | DOI | 10.1140/epjc/s10052-025-14097-x |
| Makale Linki | https://link.springer.com/article/10.1140/epjc/s10052-025-14097-x | ||
| UAK Araştırma Alanları |
Fen Bilimleri ve Matematik
|
||
| Özet |
| Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These simulations come at a high computational cost, where the detector simulation and reconstruction algorithms have the largest CPU demands. This article describes how machine-learning (ML) techniques are used to reweight simulated samples obtained with a given set of parameters to samples with different parameters or samples obtained from entirely different simulation programs. The ML reweighting method avoids the need for simulating the detector response multiple times by incorporating the relevant information in a single sample through event weights … |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 5 |
| Scopus | 5 |
| Google Scholar | 21 |