50 citations · 263 across the 17 of their papers we have counts for
17 papers
Variational inference for pile-up removal at hadron colliders with diffusion models
Malte Algren, Tobias Golling, Christopher Pollard +1
In this paper, we present a novel method for pile-up removal of interactions using variational inference with diffusion models, called vipr. Instead of using classification me…
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…
Cluster Scanning: a novel approach to resonance searches
Ivan Oleksiyuk, John Andrew Raine, Michael Krämer +2
We propose a new model-independent method for new physics searches called Cluster Scanning. It uses the k-means algorithm to perform clustering in the space of low-level event or j…
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…
Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation
Samuel Klein, John Andrew Raine, Tobias Golling
Normalizing flows are constructed from a base distribution with a known density and a diffeomorphism with a tractable Jacobian. The base density of a normalizing flow can be parame…
ν-Flows: Conditional Neutrino Regression
Matthew Leigh, John Andrew Raine, Knut Zoch +1
We present -Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep in…
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…
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics
Atul Kumar Sinha, Daniele Paliotta, Bálint Máté +4
Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard Gea…
Funnels: Exact maximum likelihood with dimensionality reduction
Samuel Klein, John A. Raine, Sebastian Pina-Otey +2
Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model. We use the SurVAE framework to construct dimension reducing s…