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Generative Adversarial Networks for fast simulation

Carminati, Federico and Khattak, Gulrukh and Loncar, Vladimir and Nguyen, Thong Q. and Pierini, Maurizio and Da Rocha, Ricardo Brito and Samaras-Tsakiris, Konstantinos and Vallecorsa, Sofia and Vlimant, Jean-Roch (2020) Generative Adversarial Networks for fast simulation. Journal of Physics: Conference Series, 1525 . Art. No. 012064. ISSN 1742-6588. doi:10.1088/1742-6596/1525/1/012064.

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Deep Learning techniques are being studied for different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. Here we present updated results on the development of 3DGAN, one of the first examples using three-dimensional convolutional Generative Adversarial Networks to simulate high granularity electromagnetic calorimeters. In particular, we report on two main aspects: results on the simulation of a more general, realistic physics use case and on data parallel strategies to distribute the training process across multiple nodes on public cloud resources.

Item Type:Article
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URLURL TypeDescription
Nguyen, Thong Q.0000-0003-3954-5131
Pierini, Maurizio0000-0003-1939-4268
Vlimant, Jean-Roch0000-0002-9705-101X
Additional Information:© 2021 Published under licence by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Accepted papers received: 03 April 2020; Published online: 07 July 2020.
Record Number:CaltechAUTHORS:20210304-145335977
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Official Citation:Federico Carminati et al 2020 J. Phys.: Conf. Ser. 1525 012064
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108313
Deposited By: Tony Diaz
Deposited On:04 Mar 2021 23:10
Last Modified:16 Nov 2021 19:10

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