74 citations · 483 across the 22 of their papers we have counts for
23 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…
Agentic Re-Casting using Agentic Re-Simulations
Sascha Diefenbacher, Tilman Plehn, Daniel Schiller +1
Analysis re-casting at the LHC is highly standardized and nevertheless requires resources, time, and physics input. Building on the new MadAgents.v3, we show how a global SFitter a…
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,…
FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology
Biwei Dai, Po-Wen Chang, Wahid Bhimji +15
Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allow…
Fair Universe Higgs Uncertainty Challenge
Ragansu Chakkappai, Wahid Bhimji, Paolo Calafiura +16
This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in cross-section measurement. Partici…
Agents of Discovery
Sascha Diefenbacher, Anna Hallin, Gregor Kasieczka +3
The substantial data volumes encountered in modern particle physics and other domains of fundamental physics research allow (and require) the use of increasingly complex data analy…
Forecasting Generative Amplification
Henning Bahl, Sascha Diefenbacher, Nina Elmer +2
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is import…
Weakly Supervised Anomaly Detection in Events with a Higgs Boson and Exotic Physics
Chi Lung Cheng, Sarah Demers, Sascha Diefenbacher +3
We present a machine learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this meth…
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…
FAIR Universe HiggsML Uncertainty Dataset and Competition
Lisa Benato, Wahid Bhimji, Paolo Calafiura +26
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to comput…
Calibrating Bayesian Generative Machine Learning for Bayesiamplification
Sebastian Bieringer, Sascha Diefenbacher, Gregor Kasieczka +1
Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural netw…
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…
Improving Generative Model-based Unfolding with Schrödinger Bridges
Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni +2
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area: one base…
CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Erik Buhmann, Sascha Diefenbacher, Engin Eren +6
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with ge…
L2LFlows: Generating High-Fidelity 3D Calorimeter Images
Sascha Diefenbacher, Engin Eren, Frank Gaede +4
We explore the use of normalizing flows to emulate Monte Carlo detector simulations of photon showers in a high-granularity electromagnetic calorimeter prototype for the Internatio…
Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka +4
The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set…
Calomplification -- The Power of Generative Calorimeter Models
Sebastian Bieringer, Anja Butter, Sascha Diefenbacher +7
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especia…
Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network
Erik Buhmann, Sascha Diefenbacher, Engin Eren +4
Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulati…
DCTRGAN: Improving the Precision of Generative Models with Reweighting
Sascha Diefenbacher, Engin Eren, Gregor Kasieczka +3
Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (GANs). We introduce a…
GANplifying Event Samples
Anja Butter, Sascha Diefenbacher, Gregor Kasieczka +2
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.…
CapsNets Continuing the Convolutional Quest
Sascha Diefenbacher, Hermann Frost, Gregor Kasieczka +2
Capsule networks are ideal tools to combine event-level and subjet information at the LHC. After benchmarking our capsule network against standard convolutional networks, we show h…
Dark Matter in Anomaly-Free Gauge Extensions
Martin Bauer, Sascha Diefenbacher, Tilman Plehn +2
A consistent model for vector mediators to dark matter needs to be anomaly-free and include a scalar mode from mass generation. For the leading U(1) extensions we review the struct…