Biography
I am a postdoctoral researcher at the CP3 in Louvain-la-Neuve. I work at 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. Additionally, my interests extend to simulation-based inference, which is crucially dependent on first-principle simulations provided by theory, as well as precision calculations.
Education
Research interests
- Generative models like generative adversarial networks, normalizing flows, diffusion models and other novel architectures
- Monte-Carlo integration and event generation
- Simulation-based inference
- NLO calculations and loop integrals
- LHC phenomenology
- Anomaly detection
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, N. Hütsch, F. Maltoni, O. Mattelaer, T. Plehn, R. Winterhalder:
The MadNIS Reloaded
Submitted to SciPost
[ArXiv] -
T. Heimel, N. Hütsch, R. Winterhalder, T. Plehn, A. Butter:
Precision-Machine Learning for the Matrix Element Method
Submitted to SciPost
[ArXiv] -
S. Dittmaier, P. Maierhöfer, C. Schwan, R. Winterhalder:
Like-Sign W-Boson Scattering at the LHC — Approximations and Full Next-to-Leading-Order Predictions
JHEP 11 (2023) 022
[Journal] [ArXiv] -
B. Nachman and R. Winterhalder:
ELSA — Enhanced latent spaces for improved collider simulations
Eur. Phys. J. C 83, 843 (2023)
[Journal] [ArXiv]
2023
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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
SciPost Phys. 15, 141 (2023)
[Journal] [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
SciPost Phys. 14, 079 (2023)
[Journal] [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)
[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)
[Journal] [ArXiv]
2020
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Anja Butter, Tilman Plehn, Ramon Winterhalder:
How to GAN Event Subtraction
SciPost Phys. Core 3, 009 (2020)
[Journal] [ArXiv] -
Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Ramon Winterhalder:
How to GAN away Detector Effects
SciPost Phys. 8, 070 (2020)
[Journal] [ArXiv] -
Anja Butter, Tilman Plehn, Ramon Winterhalder:
How to GAN LHC Events
SciPost Phys. 7, 075 (2019)
[Journal] [ArXiv]