Data-directed search for new physics based on symmetries of the SM

Author:

Birman MattiasORCID,Nachman Benjamin,Sebbah Raphael,Sela Gal,Turetz Ophir,Bressler Shikma

Abstract

AbstractWe 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.

Funder

Israel Science Foundation

German Israeli Foundation

U.S. Department of Energy (DOE), Office of Science

Yeda-Sela (YeS) Center for Basic Research

Publisher

Springer Science and Business Media LLC

Subject

Physics and Astronomy (miscellaneous),Engineering (miscellaneous)

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