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Bump Hunting in Latent Space
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Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and characterise features in measured spectra as caused by the presence of anomalies in a dataset.
Forward citations
Cited by 2 Pith papers
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Generative Amplification with Surrogate Monte Carlo
An amplitude surrogate trained on a few thousand exact LHC amplitude points statistically outperforms the training data, with largest amplification in sparsely populated kinematic tails of Z+g and Z+4g production.
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Graph theory inspired anomaly detection at the LHC
Sparse globally rigid graph representations of jets, combined with roughly 30 reclustered subjets, improve graph autoencoder anomaly detection on the LHC Olympics benchmark.
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