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GANplifying Event Samples

Sep 16, 2020
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn

* 14 pages, 7 figures, fixed two equations, extended acknowledgments 

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Simulation-Assisted Decorrelation for Resonant Anomaly Detection

Sep 04, 2020
Kees Benkendorfer, Luc Le Pottier, Benjamin Nachman

* 17 pages, 7 figures 

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DCTRGAN: Improving the Precision of Generative Models with Reweighting

Sep 03, 2020
Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol, Benjamin Nachman, David Shih

* 14 pages, 8 figures 

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Simulation Assisted Likelihood-free Anomaly Detection

Jan 14, 2020
Anders Andreassen, Benjamin Nachman, David Shih

* 19 pages, 9 figures 

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Anomaly Detection with Density Estimation

Jan 14, 2020
Benjamin Nachman, David Shih

* 28 pages, 11 figures 

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OmniFold: A Method to Simultaneously Unfold All Observables

Nov 20, 2019
Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Jesse Thaler

* 7 pages, 3 figures, 1 table, 1 poem 

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AI Safety for High Energy Physics

Oct 18, 2019
Benjamin Nachman, Chase Shimmin

* 8 pages, 5 figures 

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Neural Networks for Full Phase-space Reweighting and Parameter Tuning

Aug 26, 2019
Anders Andreassen, Benjamin Nachman

* 7 pages, 3 figures; v2 has updated citations and clarifications; v3 has a new appendix with an alternative fitting method 

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Learning to Classify from Impure Samples with High-Dimensional Data

Jul 24, 2018
Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Matthew D. Schwartz

* Phys. Rev. D 98, 011502 (2018) 
* 6 pages, 2 tables, 2 figures. v2: updated to match PRD version 

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Pileup Mitigation with Machine Learning (PUMML)

Jan 08, 2018
Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Matthew D. Schwartz

* JHEP 12 (2017) 051 
* 20 pages, 8 figures, 2 tables. Updated to JHEP version 

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CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

Dec 21, 2017
Michela Paganini, Luke de Oliveira, Benjamin Nachman

* Phys. Rev. D 97, 014021 (2018) 
* 14 pages, 4 tables, 13 figures; version accepted by Physical Review D (PRD) 

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Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters

Dec 21, 2017
Michela Paganini, Luke de Oliveira, Benjamin Nachman

* Phys. Rev. Lett. 120, 042003 (2018) 
* 6 pages, 3 figures; version accepted by Physical Review Letters (PRL) 

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Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters

Nov 23, 2017
Luke de Oliveira, Michela Paganini, Benjamin Nachman

* 7 pages, 5 figures, in proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) 

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Classification without labels: Learning from mixed samples in high energy physics

Nov 18, 2017
Eric M. Metodiev, Benjamin Nachman, Jesse Thaler

* JHEP 10 (2017) 174 
* 18 pages, 5 figures; v2: intro extended and references added; v3: additional discussion to match JHEP version 

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Weakly Supervised Classification in High Energy Physics

Jul 03, 2017
Lucio Mwinmaarong Dery, Benjamin Nachman, Francesco Rubbo, Ariel Schwartzman

* JHEP 05 (2017) 145 
* 8 pages, 4 figures 

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Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

Jun 13, 2017
Luke de Oliveira, Michela Paganini, Benjamin Nachman

* Comput Softw Big Sci (2017) 1: 4 
* 23 pages, 23 figures, 1 table, and appendix; Added new validation metric, acknowledgements, minor corrections 

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Jet-Images -- Deep Learning Edition

Jan 22, 2017
Luke de Oliveira, Michael Kagan, Lester Mackey, Benjamin Nachman, Ariel Schwartzman

* JHEP 07 (2016) 069 
* 32 pages, 24 figures. Version that is published in JHEP 

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Fuzzy Jets

Sep 07, 2015
Lester Mackey, Benjamin Nachman, Ariel Schwartzman, Conrad Stansbury

* JHEP 06 (2016) 010 

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