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Event Generation and Statistical Sampling with Deep Generative Models / Otten, Sydney (speaker) (Radboud Universiteit Nijmegen)
We present a study for the generation of events from a physical process with generative deep learning. To simulate physical processes it is not only important to produce physical events, but also to produce the events with the right frequency of occurrence (density). [...]
2019 - 1502. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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(91)

Fast simulation methods in ATLAS / Raine, Johnny (Universite de Geneve (CH)) /ATLAS Collaboration
The ATLAS physics program relies on very large samples of GEANT4 simulated events, which provide a highly detailed and accurate simulation of the ATLAS detector. However, this accuracy comes with a high price in CPU, and the sensitivity of many physics analyses is already limited by the available Monte Carlo statistics and will be even more so in the future. [...]
ATL-SOFT-SLIDE-2019-817.- Geneva : CERN, 2019 - 19 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019

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(85)

Deep Generative Models for Fast Shower Simulation with the ATLAS Experiment / Raine, Johnny (Universite de Geneve (CH)) /ATLAS Collaboration
Modeling the physics of a detector's response to particle collisions is one of the most CPU intensive and time consuming aspects of LHC computing. With the upcoming high-luminosity upgrade and the need to have even larger simulated datasets to support physics analysis, the development of new faster simulation techniques but with sufficiently accurate physics performance is required. [...]
ATL-SOFT-SLIDE-2019-774.- Geneva : CERN, 2019 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019

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(80)

Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents / Drefs, Jakob (Oldenburg U.) ; Guiraud, Enrico (CERN) ; Panagiotou, Filippos (Oldenburg U.) ; Lücke, Jörg (Oldenburg U.)
Many types of data are generated at least partly by discrete causes. Deep generative models such as variational autoencoders (VAEs) with binary latents consequently became of interest. [...]
2023
In : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022), Grenoble, France, 19 - 23 Sep 2022, pp.357–372

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(61)

Deep generative models for fast shower simulation in ATLAS / Salamani, Dalila (Departement de Physique Nucleaire et Corpusculaire, Universite de Geneve) ; Golling, Tobias (Departement de Physique Nucleaire et Corpusculaire, Universite de Geneve) ; Gadatsch, Stefan (Departement de Physique Nucleaire et Corpusculaire, Universite de Geneve) ; Stewart, Graeme (European Laboratory for Particle Physics, CERN) ; Rousseau, David (LAL, Univ. Paris-Sud, IN2P3/CNRS, Universite Paris-Saclay) ; Ghosh, Aishik (LAL, Univ. Paris-Sud, IN2P3/CNRS, Universite Paris-Saclay) /ATLAS Collaboration
Modeling the physics of the detector response to particle collisions is one of the most CPU intensive and time consuming aspects of LHC computing. With the upcoming high-luminosity upgrade and need for ever larger simulated datasets to support physics analysis, the development of new faster simulation techniques is required. [...]
ATL-SOFT-SLIDE-2018-983.- Geneva : CERN, 2018 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 14th eScience IEEE International Conference, Amsterdam, Netherlands, 29 Oct - 1 Nov 2018

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(54)

Scalable Unsupervised Learning for Deep Discrete Generative Models / Guiraud, Enrico
Efficient, scalable training of probabilistic generative models is a highly sought after goal in the field of machine learning [...]
CERN-THESIS-2020-356 - University of Oldenburg : University of Oldenburg, 2021-02-02. - 136 p.

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(51)

Fast simulation methods in ATLAS: from classical to generative models / ATLAS Collaboration
The ATLAS physics program relies on very large samples of \textsc{Geant4} simulated events, which provide a highly detailed and accurate simulation of the ATLAS detector. [...]
ATL-SOFT-PROC-2020-035. - 2020. - 7 p.
Original Communication (restricted to ATLAS) - Full text - Fulltext from publisher

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(49)

THE SUCCESS OF DEEP GENERATIVE MODELS / Tomczak, Jakub (speaker) (University of Amsterdam)
Deep generative models allow us to learn hidden representations of data and generate new examples. There are two major families of models that are exploited in current applications: Generative Adversarial Networks (GANs), and Variational Auto-Encoders (VAE). [...]
2018 - 3232. EP-IT Data science seminars External link: Event details In : THE SUCCESS OF DEEP GENERATIVE MODELS

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(48)

Deep Generative Models for Fast Shower Simulation in ATLAS / Salamani, Dalila (Geneva U.) ; Gadatsch, Stefan (Geneva U.) ; Golling, Tobias (Geneva U.) ; Stewart, Graeme Andrew (CERN) ; Ghosh, Aishik (Orsay, LAL) ; Rousseau, David (Orsay, LAL) ; Hasib, Ahmed (Edinburgh U.) ; Schaarschmidt, Jana (Washington U., Seattle)
Detectors of High Energy Physics experiments, such as the ATLAS dectector [1] at the Large Hadron Collider [2], serve as cameras that take pictures of the particles produced in the collision events. One of the key detector technologies used for measuring the energy of particles are calorimeters. [...]
2018 - 1 p. - Published in : 10.1109/eScience.2018.00091
In : 14th eScience IEEE International Conference, Amsterdam, Netherlands, 29 Oct - 1 Nov 2018, pp.348

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(44)

Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors / Chang, Su Yeon (CERN ; EPFL, Lausanne, LPPC) ; Vallecorsa, Sofia (CERN) ; Combarro, Elías F. (Oviedo U.) ; Carminati, Federico (CERN)
Deep Neural Networks (DNNs) come into the limelight in High Energy Physics (HEP) in order to manipulate the increasing amount of data encountered in the next generation of accelerators. [...]
arXiv:2101.11132. - 4 p.
Fulltext

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