240 citations · 1.9k across the 47 of their papers we have counts for
50 papers
Know What You Don't Flow
Anja Butter, Sascha Diefenbacher, Tilman Plehn +1
Calibrated learned uncertainties are a key requirement also for generative neural networks in LHC physics. For a toy model with an explicit likelihood we show how a heteroscedastic…
Local Conformal Predictions for Calibrated Surrogates
Suprio Dubey, Henning Bahl, Anja Butter +2
Neural network surrogates for LHC scattering amplitudes require trustworthy uncertainty estimates, a challenging task given the non-Gaussian systematics. We target it using conform…
Scaling laws for amplitude surrogates
Henning Bahl, Victor Bresó-Pla, Anja Butter +1
Scaling laws describing the dependence of neural network performance on the amount of training data, the spent compute, and the network size have emerged across a huge variety of m…
Generative Unfolding of Jets and Their Substructure
Antoine Petitjean, Anja Butter, Kevin Greif +4
Unfolding, for example of distortions imparted by detectors, provides suitable and publishable representations of LHC data. Many methods for unbinned and high-dimensional unfolding…
Analysis-ready Generative Unfolding
Anja Butter, Nathan Huetsch, Vinicius Mikuni +2
Machine Learning (ML)-based unfolding methods have enabled high-dimensional and unbinned differential cross section measurements. While a suite of such methods has been proposed, m…
Iterative HOMER with uncertainties
Anja Butter, Ayodele Ore, Sofia Palacios Schweitzer +10
We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap betwe…
Simulation-Prior Independent Neural Unfolding Procedure
Anja Butter, Theo Heimel, Nathan Huetsch +2
Machine learning allows unfolding high-dimensional spaces without binning at the LHC. The new SPINUP method extracts the unfolded distribution based on a neural network encoding th…
Extrapolating Jet Radiation with Autoregressive Transformers
Anja Butter, François Charton, Javier Mariño Villadamigo +3
Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of partic…
Generative Unfolding with Distribution Mapping
Anja Butter, Sascha Diefenbacher, Nathan Huetsch +4
Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We sh…
The Landscape of Unfolding with Machine Learning
Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov +9
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new…
Kicking it Off(-shell) with Direct Diffusion
Anja Butter, Tomas Jezo, Michael Klasen +3
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensi…
Rational Q-systems at Root of Unity I. Closed Chains
Jue Hou, Yunfeng Jiang, Yuan Miao
The solution of Bethe ansatz equations for XXZ spin chain with the parameter being a root of unity is infamously subtle. In this work, we develop the rational -system for th…
Precision-Machine Learning for the Matrix Element Method
Theo Heimel, Nathan Huetsch, Ramon Winterhalder +2
The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integratio…
Artificial Intelligence for the Electron Ion Collider (AI4EIC)
C. Allaire, R. Ammendola, E. -C. Aschenauer +92
The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune t…
Jet Diffusion versus JetGPT -- Modern Networks for the LHC
Anja Butter, Nathan Huetsch, Sofia Palacios Schweitzer +3
We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainti…
Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks
Anja Butter, Michael Krämer, Silvia Manconi +1
About a third of the -ray sources detected by the Fermi Large Area Telescope (Fermi-LAT) remain unidentified, and some of these could be exotic objects such as dark matter subha…
Performance versus Resilience in Modern Quark-Gluon Tagging
Anja Butter, Barry M. Dillon, Tilman Plehn +1
Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show ho…
An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
Mathias Backes, Anja Butter, Monica Dunford +1
The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dim…
MadNIS -- Neural Multi-Channel Importance Sampling
Theo Heimel, Ramon Winterhalder, Anja Butter +5
Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normal…
Modern Machine Learning for LHC Physicists
Tilman Plehn, Anja Butter, Barry Dillon +3
Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do…
Two Invertible Networks for the Matrix Element Method
Anja Butter, Theo Heimel, Till Martini +2
The matrix element method is widely considered the ultimate LHC inference tool for small event numbers. We show how a combination of two conditional generative neural networks enco…
Loop Amplitudes from Precision Networks
Simon Badger, Anja Butter, Michel Luchmann +2
Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and…
Event Generators for High-Energy Physics Experiments
J. M. Campbell, M. Diefenthaler, T. J. Hobbs +209
We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of acti…
Machine Learning and LHC Event Generation
Anja Butter, Tilman Plehn, Steffen Schumann +48
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predicti…