| Autor: |
Woźniak Kinga Anna, Cerri Olmo, Duarte Javier M., Möller Torsten, Ngadiuba Jennifer, Nguyen Thong Q., Pierini Maurizio, Spiropulu Maria, Vlimant Jean-Roch |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
EPJ Web of Conferences, Vol 245, p 06039 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2100-014X |
| DOI: |
10.1051/epjconf/202024506039 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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