Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter
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 (2024) | ||
| Makale Dili | İngilizce | Basım Tarihi | 11-2024 |
| Cilt / Sayı / Sayfa | 19 / 11 / – | DOI | 10.1088/1748-0221/19/11/P11025 |
| Makale Linki | https://iopscience.iop.org/article/10.1088/1748-0221/19/11/P11025/meta | ||
| UAK Araştırma Alanları |
Fen Bilimleri ve Matematik
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| Özet |
| A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data … |
| Anahtar Kelimeler |
| Calorimeters | Pattern recognition, cluster finding, calibration and fitting methods | Performance of High Energy Physics Detectors | Si microstrip and pad detectors |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 4 |
| Google Scholar | 10 |