30 citations · 179 across the 19 of their papers we have counts for
21 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…
NAE, Statistically
Ranit Das, Jonathan Ostertag-Henning, Tilman Plehn +1
Searches for new physics using neural anomaly scores have transformative potential, but suffer from a lack of statistical interpretability. The normalized autoencoder (NAE) provide…
VERaiPHY -- Validation & Evaluation for Robust AI in PHYsics
Gaia Grosso, Ramon Winterhalder, Lydia Brenner +2
Modern machine learning is leading to substantial gains in precision, flexibility, and computational efficiency in fundamental physics. Statistical validation, uncertainty quantifi…
Machine Learning is Good for Physics - and Vice Versa
Michael Krämer, Tilman Plehn
Scientific AI is rapidly transforming fundamental physics research and challenging defining aspects of the fundamental physics methodology. We discuss opportunities and dangers of…
Neural Control Variates at LO and NLO
Theo Heimel, Tilman Plehn, Rebecca Revelli +2
We employ neural control variates to minimize the range of event weights and avoid negative weights for phase-space integration and event generation. A signed control variate, buil…
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…
The Physics Behind ML-based Quark-Gluon Taggers
Sophia Vent, Ramon Winterhalder, Tilman Plehn
Jet taggers provide an ideal testbed for applying explainability techniques to powerful ML tools. For theoretically and experimentally challenging quark-gluon tagging, we first ide…
How to Unfold Top Decays
Luigi Favaro, Roman Kogler, Alexander Paasch +3
Using unfolded top-quark decay data we can measure the top quark mass, as well as search for unexpected kinematic effects. We present a new generative unfolding method for the two…
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…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
Differentiable MadNIS-Lite
Theo Heimel, Olivier Mattelaer, Tilman Plehn +1
Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned…
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…
Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
Radha Mastandrea, Benjamin Nachman, Tilman Plehn
Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and sho…
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…
PINNferring the Hubble Function with Uncertainties
Lennart Röver, Björn Malte Schäfer, Tilman Plehn
The Hubble function characterizes a given Friedmann-Robertson-Walker spacetime and can be related to the densities of the cosmological fluids and their equations of state. We show…
Normalizing Flows for High-Dimensional Detector Simulations
Florian Ernst, Luigi Favaro, Claudius Krause +2
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimete…
Semi-visible jets, energy-based models, and self-supervision
Luigi Favaro, Michael Krämer, Tanmoy Modak +2
We present DarkCLR, a novel framework for detecting semi-visible jets at the LHC. DarkCLR uses a self-supervised contrastive-learning approach to create observables that are approx…
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…
The MadNIS Reloaded
Theo Heimel, Nathan Huetsch, Fabio Maltoni +3
In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS…
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…
Anomalies, Representations, and Self-Supervision
Barry M. Dillon, Luigi Favaro, Friedrich Feiden +2
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR tech…