| Autor: |
Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, Katja Krüger, Peter McKeown, Lennart Rustige |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Machine Learning: Science and Technology, Vol 4, Iss 3, p 035044 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2632-2153 |
| DOI: |
10.1088/2632-2153/acefa9 |
| Popis: |
The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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