Biography

I am a postdoctoral researcher at Tif Lab at the University of Milan. 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 GANs, normalizing flows,
    diffusion models and other novel architectures
  • Monte-Carlo integration and event generation
  • Simulation-based inference
  • NLO calculations and loop integrals
  • Anomaly detection
  • ML for cosmology and multi-messenger astronomy

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.

    2024

  • T. Heimel, O. Mattelaer, T. Plehn, R. Winterhalder:
    Differentiable MadNIS-Lite
    Submitted to SciPost
    [ArXiv]

  • S. Dittmaier, C. Schwan, R. Winterhalder:
    Full and approximated NLO predictions for like-sign W-boson scattering at the LHC
    Proceedings to "Loops and Legs in Quantum Field Theory (LL2024)"
    [ArXiv]

    2023

  • T. Heimel, N. Hütsch, F. Maltoni, O. Mattelaer, T. Plehn, R. Winterhalder:
    The MadNIS Reloaded
    SciPost Phys. 17, 023 (2024)
    [Journal] [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]

    2022

  • 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
    SciPost Phys. 16, 130 (2024)
    [Journal] [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]

    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)
    [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]

    2019

  • 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]

Contact

  • Address

    Physics Department
    Università degli Studi di Milano
    Via Celoria 16
    20133 Milan
    Italy