Search Results for author: Jack H. Collins

Found 5 papers, 2 papers with code

Weakly-Supervised Anomaly Detection in the Milky Way

1 code implementation5 May 2023 Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman, David Shih, Matthew R. Buckley, Jack H. Collins

Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches.

Supervised Anomaly Detection Weakly-supervised Anomaly Detection

Machine-Learning Compression for Particle Physics Discoveries

no code implementations20 Oct 2022 Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson

In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis.

An Exploration of Learnt Representations of W Jets

no code implementations22 Sep 2021 Jack H. Collins

I present a Variational Autoencoder (VAE) trained on collider physics data (specifically boosted $W$ jets), with reconstruction error given by an approximation to the Earth Movers Distance (EMD) between input and output jets.

Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection

no code implementations5 Apr 2021 Jack H. Collins, Pablo Martín-Ramiro, Benjamin Nachman, David Shih

We examine the ability of the two methods to identify a new physics signal at different cross sections in a fully hadronic resonance search.

Anomaly Detection

Anomaly Detection for Resonant New Physics with Machine Learning

1 code implementation7 May 2018 Jack H. Collins, Kiel Howe, Benjamin Nachman

Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics.

High Energy Physics - Phenomenology High Energy Physics - Experiment

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