Data-Directed Search for New Physics based on Symmetries of the SM

TL;DR

This paper introduces a data-driven approach leveraging symmetries of the Standard Model to efficiently identify potential signals of new physics in experimental data, reducing reliance on simulations.

Contribution

It presents a novel symmetry-based data-directed search method for BSM physics, utilizing simple test statistics and neural networks for rapid data scanning.

Findings

Symmetry-based tests achieve sensitivity close to traditional likelihood methods.

The approach enables efficient scanning of large data sets for anomalies.

Neural networks can enhance the identification of regions of interest.

Abstract

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. Weakly supervised Neural Networks could be used for this purpose as well.