11 papers
Pairton: Iterative Reconstruction of Short-Lived Particles
Andreas Hermansen, Chris Scheulen, Tobias Golling
We present Pairton, an iterative framework for reconstructing short-lived particles in high-energy collision events. By formulating particle reconstruction as a masked prediction p…
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…
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…
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…
TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches
Ivan Oleksiyuk, Svyatoslav Voloshynovskiy, Tobias Golling
We introduce a new model for conditional and continuous data morphing called TRansport Adversarial Network for Smooth InTerpolation (TRANSIT). We apply it to create a background da…
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…