Search Results for author:
Found 5 papers, 2 papers with code
Weakly-Supervised Anomaly Detection in the Milky Way
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
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
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
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 for Resonant New Physics with Machine Learning
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
