50 citations · 263 across the 17 of their papers we have counts for
9 papers · 1 filter
PC-Droid: Faster diffusion and improved quality for particle cloud generation
Matthew Leigh, Debajyoti Sengupta, John Andrew Raine +2
Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds. By leveraging a new diffusion formulat…
Decorrelation using Optimal Transport
Malte Algren, John Andrew Raine, Tobias Golling
Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences. We introduce a novel dec…
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…
-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows
John Andrew Raine, Matthew Leigh, Knut Zoch +1
In this work we introduce -Flows, an extension of the -Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations…
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…
Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting
Malte Algren, Tobias Golling, Manuel Guth +2
We present an alternative to reweighting techniques for modifying distributions to account for a desired change in an underlying conditional distribution, as is often needed to cor…
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.…
Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
Lukas Ehrke, John Andrew Raine, Knut Zoch +2
We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of…
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…