Search Results for author:
Found 5 papers, 4 papers with code
Anomalies, Representations, and Self-Supervision
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.
Better Latent Spaces for Better Autoencoders
In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.
Bump Hunting in Latent Space
Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC.
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
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
Uncovering latent jet substructure
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
