26 citations · 64 across the 9 of their papers we have counts for
7 papers · 1 filter
Generative Models and Statistical Validation
Sascha Diefenbacher, Sofia Palacios Schweitzer, Gregor Kasieczka
Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work,…
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…
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…
CaloDREAM -- Detector Response Emulation via Attentive flow Matching
Luigi Favaro, Ayodele Ore, Sofia Palacios Schweitzer +1
Detector simulations are an exciting application of modern generative networks. Their sparse high-dimensional data combined with the required precision poses a serious challenge. W…
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…
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…