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anomalyclr

AnomalyCLR is a self-supervised technique to learn representations for anomaly detection tasks.

anomalyclr-jets

A codebase for AnomalyCLR-jets, learning representations for anomaly detection in LHC jets. Based on a self-supervised contrastive-learning technique developed for JetCLR and AnomalyCLR.

dijet-preprocessing

Scripts and notebooks for pre-processing di-jet event data into different formats, primarily the jet-image and Lund plane formats. The focus is on the LHC Olympics 2020 datasets.

dlpp-tutorials

Tutorials for the Deep Learning in Particle Physics course, spring semester 2022, at the University of Heidelberg.

jet-mixture-vae

Using a mixture model VAE to analyse jet images.

jet-preprocessing

Scripts and notebooks for pre-processing jet data into different formats, primarily the jet-image and Lund plane formats. The focus is on datasets used in the Landscape of Top Taggers challenge.

jetclr

Core codebase for JetCLR, a high-energy-physics tool for the self-supervised contrastive learning of jet representations.

lda-jet-substructure

Code to use the Latent Dirichlet Allocation probabilistic model to extract sub-classes (topics) from collider data.

pythia-dark-jets