Biography
I am a postdoctoral researcher at the CP3 in Louvain-la-Neuve. I work on the intersection of particle physics and machine learning. My research aims to fully establish data-driven techniques in high-energy physics and to enhance standard simulation methods with (generative) neural networks.
Education
Research interests
- Generative models like generative adversarial networks and normalizing flows
- Machine learning to tackle problems in particle physics
- Monte-Carlo integration and event generation
- NLO calculations and loop integrals
Publications
This is a list of my publications in reverse-chronological order. All authors are listed alphabetically, following the convention in particle physics. Exceptions occur for some of the papers.
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T. Heimel, R. Winterhalder, A. Butter, J. Isaacson,
C. Krause, F. Maltoni, O. Mattelaer, T. Plehn:
MadNIS — Neural Multi-Channel Importance Sampling.
To be submitted to SciPost
[ArXiv] -
J. M. Campbell, M. Diefenthaler, T. J. Hobbs, S. Höche,
J. Isaacson, F. Kling, S. Mrenna, J. Reuter et al.:
Event Generators for High-Energy Physics Experiments.
Contribution to Snowmass 2021
[ArXiv] -
Anja Butter, Tilman Plehn, Steffen Schumann et al.:
Machine Learning and LHC Event Generation.
Contribution to Snowmass 2021
[ArXiv] -
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman,
Tilman Plehn, David Shih, Ramon Winterhalder:
Ephemeral Learning — Augmenting Triggers with Online-Trained Normalizing Flows.
SciPost Phys. 13, 087 (2022)
[Journal] [ArXiv]
2022
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Ramon Winterhalder, Vitaly Magerya, Emilio Villa, Stephen P. Jones,
Matthias Kerner, Anja Butter, Gudrun Heinrich, Tilman Plehn:
Targeting Multi-Loop Integrals with Neural Networks.
SciPost Phys. 12, 129 (2022)
[Journal] [ArXiv] -
Miguel Arratia, Anja Butter, Mario Campanelli, Vincent Croft, Dag Gillberg,
Aishik Ghosh, Kristin Lohwasser, Bogdan Malaescu, Vinicius Mikuni,
Benjamin Nachman, Juan Rojo, Jesse Thaler, Ramon Winterhalder:
Publishing Unbinned Differential Cross Section Results.
JINST 17 (2022) 01, P01024
[Journal] [ArXiv] -
Ramon Winterhalder, Marco Bellagente, Benjamin Nachman:
Latent Space Refinement for Deep Generative Models.
NeurIPS 2021 Workshop on DGMs and Downstream Applications
[Workshop] [ArXiv]
2021
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Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder:
How to GAN Event Unweighting.
SciPost Phys. 10, 089 (2021)
[PDF] [Journal] [ArXiv] -
Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn,
Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, Ullrich Köthe:
Invertible Networks or Partons to Detector and Back Again.
SciPost Phys. 9, 074 (2020)
[PDF] [Journal] [ArXiv]
2020
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Anja Butter, Tilman Plehn, Ramon Winterhalder:
How to GAN Event Subtraction.
SciPost Phys. Core 3, 009 (2020)
[PDF] [Journal] [ArXiv] -
Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Ramon Winterhalder:
How to GAN away Detector Effects.
SciPost Phys. 8, 070 (2020)
[PDF] [Journal] [ArXiv] -
Anja Butter, Tilman Plehn, Ramon Winterhalder:
How to GAN LHC Events.
SciPost Phys. 7, 075 (2019)
[PDF] [Journal] [ArXiv]