531 citations · 7.1k across the 80 of their papers we have counts for
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Machine Learning in the Search for New Fundamental Physics
Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz +2
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new…
Online-compatible Unsupervised Non-resonant Anomaly Detection
Vinicius Mikuni, Benjamin Nachman, David Shih
There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on…
CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
Claudius Krause, David Shih
Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows. Here, we present CaloFlow v2, an improvement…
New Methods and Datasets for Group Anomaly Detection From Fundamental Physics
Gregor Kasieczka, Benjamin Nachman, David Shih
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications. However, it has re…
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
Claudius Krause, David Shih
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorim…
Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection
Jack H. Collins, Pablo Martín-Ramiro, Benjamin Nachman +1
Anomaly detection techniques are growing in importance at the Large Hadron Collider (LHC), motivated by the increasing need to search for new physics in a model-agnostic way. In th…