| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 06039 | |
| Number of page(s) | 8 | |
| Section | 6 - Physics Analysis | |
| DOI | https://doi.org/10.1051/epjconf/202024506039 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024506039
New Physics Agnostic Selections For New Physics Searches
1
CERN, CH-1211 Geneva, Switzerland
2
University of Vienna, Austria
3
California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, United States
4
Fermi National Accelerator Laboratory, Batavia, IL 60510, United States
* e-mail: kinga.anna.wozniak@cern.ch
Published online: 16 November 2020
We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an equivalent search on the original dataset.
© The Authors, published by EDP Sciences, 2020
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