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.

    2022

  • 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]

    2021

  • 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]

    2020

  • 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]

    2019

  • 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]

Contact

  • Address

    Centre for Cosmology, Particle Physics and Phenomenology - CP3
    Université catholique de Louvain
    2, Chemin du Cyclotron - Box L7.01.05
    B-1348 Louvain-la-Neuve
    Belgium