50 citations · 109 across the 12 of their papers we have counts for
13 papers
Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Nilotpal Kakati, Daniel Murnane, Baran Hashemi +5
Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We explore how one model can serve many of those step…
Strong CWoLa: Binary Classification Without Background Simulation
Samuel Klein, Matthew Leigh, Stephen Mulligan +1
Supervised deep learning methods have been successful in the field of high energy physics, and the trend within the field is to move away from high level reconstructed variables to…
Enhancing generalization in high energy physics using white-box adversarial attacks
Franck Rothen, Samuel Klein, Matthew Leigh +1
Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most…
Is Tokenization Needed for Masked Particle Modelling?
Matthew Leigh, Samuel Klein, François Charton +5
In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets releva…
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
Tobias Golling, Lukas Heinrich, Michael Kagan +4
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high e…
Improving new physics searches with diffusion models for event observables and jet constituents
Debajyoti Sengupta, Matthew Leigh, John Andrew Raine +2
We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC. By training diffusion models on side-band data, we show how background…
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
Tobias Golling, Samuel Klein, Radha Mastandrea +2
Many components of data analysis in high energy physics and beyond require morphing one dataset into another. This is commonly solved via reweighting, but there are many advantages…
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…
Decorrelation with conditional normalizing flows
Samuel Klein, Tobias Golling
The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events. Such discriminants become much more useful if they…
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…
Flowification: Everything is a Normalizing Flow
Bálint Máté, Samuel Klein, Tobias Golling +1
The two key characteristics of a normalizing flow is that it is invertible (in particular, dimension preserving) and that it monitors the amount by which it changes the likelihood…
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…
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…