no code implementations • 11 Jan 2023 • Barry M. Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman Plehn
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021.
1 code implementation • 16 Apr 2021 • Barry M. Dillon, Tilman Plehn, Christof Sauer, Peter Sorrenson
In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.
1 code implementation • 11 Mar 2021 • Blaž Bortolato, Barry M. Dillon, Jernej F. Kamenik, Aleks Smolkovič
Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC.
1 code implementation • 12 Jan 2021 • The DarkMachines High Dimensional Sampling Group, Csaba Balázs, Melissa van Beekveld, Sascha Caron, Barry M. Dillon, Ben Farmer, Andrew Fowlie, Will Handley, Luc Hendriks, Guðlaugur Jóhannesson, Adam Leinweber, Judita Mamužić, Gregory D. Martinez, Pat Scott, Eduardo C. Garrido-Merchán, Roberto Ruiz de Austri, Zachary Searle, Bob Stienen, Joaquin Vanschoren, Martin White
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate.
Bayesian Optimisation High Energy Physics - Phenomenology Computational Physics
2 code implementations • 8 Apr 2019 • Barry M. Dillon, Darius A. Faroughy, Jernej F. Kamenik
We apply techniques from Bayesian generative statistical modeling to uncover hidden features in jet substructure observables that discriminate between different a priori unknown underlying short distance physical processes in multi-jet events.
High Energy Physics - Phenomenology High Energy Physics - Experiment