Generative Adversarial Networks for fast simulation
Abstract
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.
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.Attached Files
Published - Carminati_2020_J._Phys.__Conf._Ser._1525_012064.pdf
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Additional details
- Eprint ID
- 108313
- Resolver ID
- CaltechAUTHORS:20210304-145335977
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2021-03-04Created from EPrint's datestamp field
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2021-11-16Created from EPrint's last_modified field