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dc.contributor.author | Amrouche, CS | |
dc.contributor.author | Ananiev, V | |
dc.contributor.author | Anastopoulos, C | |
dc.contributor.author | Andari, N | |
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dc.contributor.author | Anders, JK | |
dc.contributor.author | Andrean, SY | |
dc.contributor.author | Andreazza, A | |
dc.contributor.author | Angelidakis, S | |
dc.contributor.author | Angerami, A | |
dc.contributor.author | Anisenkov, AV | |
dc.contributor.author | Annovi, A | |
dc.contributor.author | Antel, C | |
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dc.contributor.author | Aoki, M | |
dc.contributor.author | Pozo, JAA | |
dc.contributor.author | Aparo, MA | |
dc.contributor.author | Bella, LA | |
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dc.date.accessioned | 2024-07-22T10:01:26Z | |
dc.date.available | 2024-07-22T10:01:26Z | |
dc.date.issued | 2024-03-05 | |
dc.identifier.citation | The ATLAS Collaboration. Deep Generative Models for Fast Photon Shower Simulation in ATLAS. Comput Softw Big Sci 8, 7 (2024). https://doi.org/10.1007/s41781-023-00106-9 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/98292 | |
dc.description.abstract | The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques. | en_US |
dc.publisher | Springer Nature | en_US |
dc.relation.ispartof | Computing and Software for Big Science | |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | |
dc.title | Deep Generative Models for Fast Photon Shower Simulation in ATLAS | en_US |
dc.type | Article | en_US |
dc.rights.holder | © The Author(s) 2024 | |
dc.identifier.doi | 10.1007/s41781-023-00106-9 | |
pubs.issue | 1 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
pubs.volume | 8 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
rioxxterms.funder.project | b215eee3-195d-4c4f-a85d-169a4331c138 | en_US |