https://doi.org/10.1140/epjc/s10052-022-10454-2
Regular Article - Experimental Physics
Data-directed search for new physics based on symmetries of the SM
1
Department of Particle Physics and Astrophysics, Weizmann Institute of Science, Rehovot, Israel
2
Physics Division, Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA
3
Berkeley Institute for Data Science, University of California, 94720, Berkeley, CA, USA
a
mattias.birman@weizmann.ac.il
Received:
22
March
2022
Accepted:
20
May
2022
Published online:
4
June
2022
We propose exploiting symmetries (exact or approximate) of the Standard Model (SM) to search for physics Beyond the Standard Model (BSM) using the data-directed paradigm (DDP). Symmetries are very powerful because they provide two samples that can be compared without requiring simulation. Focusing on the data, exclusive selections which exhibit significant asymmetry can be identified efficiently and marked for further study. Using a simple and generic test statistic which compares two matrices already provides good sensitivity, only slightly worse than that of the profile likelihood ratio test statistic which relies on the exact knowledge of the signal shape. This can be exploited for rapidly scanning large portions of the measured data, in an attempt to identify regions of interest. We also demonstrate that weakly supervised Neural Networks could be used for this purpose as well.
© The Author(s) 2022
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