115 citations · 322 across the 12 of their papers we have counts for
6 papers · 1 filter
The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
Tobias Golling, Gregor Kasieczka, Claudius Krause +6
Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD metho…
CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation
Debajyoti Sengupta, Samuel Klein, John Andrew Raine +1
Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC. W…
The Mass-ive Issue: Anomaly Detection in Jet Physics
Tobias Golling, Takuya Nobe, Dimitrios Proios +8
In the hunt for new and unobserved phenomena in particle physics, attention has turned in recent years to using advanced machine learning techniques for model independent searches.…
PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
Matthew Leigh, Debajyoti Sengupta, Guillaume Quétant +3
In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with tra…
CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
John Andrew Raine, Samuel Klein, Debajyoti Sengupta +1
We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called CURTAINs, uses…
Dilepton constraints in the Inert Doublet Model from Run 1 of the LHC
G. Belanger, B. Dumont, A. Goudelis +3
Searches in final states with two leptons plus missing transverse energy, targeting supersymmetric particles or invisible decays of the Higgs boson, were performed during Run 1 of…