Variational autoencoders for new physics mining at the Large Hadron Collider
California Institute of Technology
Abstract
Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn’t make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies…
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Authors
1Topics & keywords
- Large Hadron Collider
- Physics
- Physics beyond the Standard Model
- Particle physics
- Event (particle physics)
- Autoencoder
- Machine learning
- Artificial intelligence