A data-directed paradigm for BSM searches: the bump-hunting example

Sergey Volkovich, Federico De Vito Halevy, Shikma Bressler

Research output: Contribution to journalLetterpeer-review

4 Scopus citations


We propose a data-directed paradigm (DDP) to search for new physics. Focusing on the data without using simulations, exclusive selections which exhibit significant deviations from known properties of the standard model can be identified efficiently and marked for further study. Different properties can be exploited with the DDP. Here, the paradigm is demonstrated by combining the promising potential of neural networks (NN) with the common bump-hunting approach. Using the NN, the resource-consuming tasks of background and systematic uncertainty estimation are avoided, allowing rapid testing of many final states with only a minor degradation in the sensitivity to bumps relative to standard analysis methods.

Original languageEnglish
Article number265
JournalEuropean Physical Journal C
Issue number3
StatePublished - Mar 2022
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by Grant no. 2871/19 from the Israeli Science Foundation (ISF), Grant no. I-1506-303.7/2019 from the German Israeli Foundation (GIF) and by the Sir Charles Clore Prize.

Publisher Copyright:
© 2022, The Author(s).


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