18 citations · 32 across the 5 of their papers we have counts for
5 papers
Improving statistical precision in Monte Carlo samples with negative weights via reweighting and uncertainty quantification
Christopher Palmer, Braden Kronheim
High statistical precision is critical for Monte Carlo (MC) samples in high energy physics and is degraded by negatively weighted events. This paper investigates a procedure to lea…
Implicit Quantile Networks For Emulation in Jet Physics
B. Kronheim, A. Al Kadhim, M. P. Kuchera +2
The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task i…
Implicit Quantile Neural Networks for Jet Simulation and Correction
Braden Kronheim, Michelle P. Kuchera, Harrison B. Prosper +1
Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (…
TensorBNN: Bayesian Inference for Neural Networks using Tensorflow
Braden Kronheim, Michelle Kuchera, Harrison Prosper
TensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models. The posterior density of neural network model parameters is repr…
Bayesian Neural Networks for Fast SUSY Predictions
Braden Kronheim, Michelle Kuchera, Harrison Prosper +1
One of the goals of current particle physics research is to obtain evidence for new physics, that is, physics beyond the Standard Model (BSM), at accelerators such as the Large Had…