50 citations · 263 across the 17 of their papers we have counts for
4 papers · 1 filter
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…