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| Hauptseite > Publikationsdatenbank > DeGeSim and the High Granularity Calorimeter for the CMS Experiment at the Large Hadron Collider > print |
| Hauptseite > Publikationsdatenbank > DeGeSim and the High Granularity Calorimeter for the CMS Experiment at the Large Hadron Collider > print |
| 001 | 905718 | ||
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| 037 | _ | _ | |a FZJ-2022-00943 |
| 100 | 1 | _ | |a Scham, Moritz |0 P:(DE-Juel1)187009 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Helmholtz AI virtual conference 2021 |c online |d 2021-04-14 - 2021-04-15 |w Germany |
| 245 | _ | _ | |a DeGeSim and the High Granularity Calorimeter for the CMS Experiment at the Large Hadron Collider |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a We present the beginning work on fast simulations for the new High Granularity Calorimeter (HGCal), which will become part of the CMS experiment at the Large Hadron Colider (LHC) at CERN/Geneva. It will replace the current end-cap calorimeters for data acquisition in 2027. The design of the new calorimeter is driven by the increased particle density following the High Luminosity Upgrade of the LHC. This enforces a complex geometry of the active sensor and a massive increase from about 7k currently to more than 3 million individual channels for each of the two future end-cap calorimeters. Traditionally, particle physics has used detailed simulations of detectors and physics. These simulations are of high precision and can describe minute details. The price for this precision is a large computational power requirement. Since the predicted computational resources will not be sufficient for the traditional stepwise simulations, we will explore deep learning techniques combining fast inference with generative models, e.g., physics-informed GANs-VAEs hybrids for simulations. In particular, Graph Neural Networks are a promising candidate to describe thecomplex geometry of the new detector. This project is part of the Helmholtz AI funded project DeGeSim. |
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| 536 | _ | _ | |a ZT-I-PF-5-3 - Deep Generative models for fast and precise physics Simulation (DeGeSim) (2020_ZT-I-PF-5-3) |0 G:(DE-HGF)2020_ZT-I-PF-5-3 |c 2020_ZT-I-PF-5-3 |x 1 |
| 700 | 1 | _ | |a Bhattacharya, Soham |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Borras, Kerstin |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Fedorko, Wojtek |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Jitsev, Jenia |0 P:(DE-Juel1)158080 |b 4 |e Corresponding author |u fzj |
| 700 | 1 | _ | |a Katzy, Judith |0 P:(DE-HGF)0 |b 5 |e Corresponding author |
| 700 | 1 | _ | |a Krücker, Dirk |0 P:(DE-HGF)0 |b 6 |e Corresponding author |
| 856 | 4 | _ | |u https://www.helmholtz.ai/fileadmin/HAICU/PDF/HelmholtzAIcon21_AbstractBook_DinA4_FV.pdf |
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