99 citations · 409 across the 21 of their papers we have counts for
26 papers
Domain Adaptation against Background Sculpting in Anomaly Detection at the LHC
Vincent Benne, Marie Hein, Michael Krämer +3
Weakly supervised anomaly detection has been shown to be an effective tool for model-agnostic searches for new physics, especially in the context of resonance searches. However, co…
The fundamental limit of jet tagging: Beyond top jets
Sarah Koller, Humberto Reyes-González
Jet tagging, i.e. determining the origin of high-energy hadronic jets, is a key challenge in particle physics. Machine-learning-based taggers have achieved remarkable progress, rai…
Neural Scaling Laws for Jet Generation
Oz Amram, Darius A. Faroughy, Tjarko Gerdes +5
Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- ar…
Open LHC Monte Carlo Event Generation
Enrico Bothmann, Jon Butterworth, Shu Chen +16
The LHC physics programme involves a vast amount of Monte Carlo event simulation. This paper reviews current efforts towards sharing the generated events as Open Data. Open Event G…
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
Learning to Validate Generative Models: a Goodness-of-Fit Approach
Pietro Cappelli, Gaia Grosso, Marco Letizia +2
Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous…
Reinterpretation and preservation of data and analyses in HEP
Jon Butterworth, Sabine Kraml, Harrison Prosper +145
Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes…
Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics
Oz Amram, Luca Anzalone, Joschka Birk +7
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrat…
The Fundamental Limit of Jet Tagging
Joep Geuskens, Nishank Gite, Michael Krämer +4
Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders…
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…
Les Houches guide to reusable ML models in LHC analyses
Jack Y. Araz, Andy Buckley, Gregor Kasieczka +10
With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis p…
The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows
Humberto Reyes-Gonzalez, Riccardo Torre
We propose the NFLikelihood, an unsupervised version, based on Normalizing Flows, of the DNNLikelihood proposed in Ref.[1]. We show, through realistic examples, how Autoregressive…
Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests
Andrea Coccaro, Marco Letizia, Humberto Reyes-Gonzalez +1
Normalizing flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions but also deliver density…
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem +3
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors. The most c…
Strength in numbers: optimal and scalable combination of LHC new-physics searches
Jack Y. Araz, Andy Buckley, Benjamin Fuks +4
To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in gen…
Testing the boundaries: Normalizing Flows for higher dimensional data sets
Humberto Reyes-Gonzalez, Riccardo Torre
Normalizing Flows (NFs) are emerging as a powerful class of generative models, as they not only allow for efficient sampling, but also deliver, by construction, density estimation.…
Constraining new physics with SModelS version 2
Gaël Alguero, Jan Heisig, Charanjit Khosa +6
We present version 2 of SModelS, a program package for the fast reinterpretation of LHC searches for new physics on the basis of simplified model results. The major novelty of the…
New signatures of Dirac neutralino dark matter
Mark D. Goodsell, Sabine Kraml, Humberto Reyes-González +1
Supersymmetric dark matter has been studied extensively in the context of the MSSM, where gauginos have Majorana masses. Introducing Dirac gaugino masses, we obtain an enriched phe…
New developments in SModelS
Gaël Alguero, Jan Heisig, Charanjit K. Khosa +7
SModelS is an automatized tool enabling the fast interpretation of simplified model results from the LHC within any model of new physics respecting a symmetry. In th…
Constraining Electroweakinos in the Minimal Dirac Gaugino Model
Mark D. Goodsell, Sabine Kraml, Humberto Reyes-González +1
Supersymmetric models with Dirac instead of Majorana gaugino masses have distinct phenomenological consequences. In this paper, we investigate the electroweakino sector of the Mini…
Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2
Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov +139
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpret…
Les Houches 2019 Physics at TeV Colliders: New Physics Working Group Report
G. Brooijmans, A. Buckley, S. Caron +87
This report presents the activities of the `New Physics' working group for the `Physics at TeV Colliders' workshop (Les Houches, France, 10--28 June, 2019). These activities includ…
LHC constraints on the minimal Dirac gaugino model
Guillaume Chalons, Mark Goodsell, Sabine Kraml +2
Most SUSY searches at the LHC are optimised for the MSSM, where gauginos are Majorana particles. By introducing Dirac gauginos, we obtain an enriched phenomenology, from which cons…
LHC limits on gluinos and squarks in the minimal Dirac gaugino model
Guillaume Chalons, Mark D. Goodsell, Sabine Kraml +2
Dirac gauginos are a well-motivated extension of the MSSM, leading to interesting phenomenological consequences. At the LHC, gluino-pair production is enhanced while squark product…