47 citations · 75 across the 6 of their papers we have counts for
6 papers
Look everywhere effects in anomaly detection
Marie Hein, Benjamin Nachman, David Shih
Machine learning-based anomaly detection methods are able to search high-dimensional spaces for hints of new physics with much less theory bias than traditional searches. However,…
Unifying Simulation and Inference with Normalizing Flows
Haoxing Du, Claudius Krause, Vinicius Mikuni +3
There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector si…
Anomaly detection with flow-based fast calorimeter simulators
Claudius Krause, Benjamin Nachman, Ian Pang +2
Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using CaloFlow as an example, we show that t…
Normalizing Flows for High-Dimensional Detector Simulations
Florian Ernst, Luigi Favaro, Claudius Krause +2
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimete…
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows
David Shih, Marat Freytsis, Stephen R. Taylor +2
Pulsar timing arrays (PTAs) perform Bayesian posterior inference with expensive MCMC methods. Given a dataset of ~10-100 pulsars and O(10^3) timing residuals each, producing a post…
Boosted Tagging with Jet Charge and Deep Learning
Yu-Chen Janice Chen, Cheng-Wei Chiang, Giovanna Cottin +1
We demonstrate that the classification of boosted, hadronically-decaying weak gauge bosons can be significantly improved over traditional cut-based and BDT-based methods using deep…