A Likelihood Ratio Framework for Highly Motivated Subdominant Signals

TL;DR

This paper introduces a likelihood ratio framework to evaluate highly motivated new physics models against experimental residuals, especially when deviations are subtle and backgrounds are well-understood.

Contribution

It presents a simple, robust statistical method for testing small signals in background-dominated data, with strategies for simplifying complex background modeling.

Findings

Framework effectively distinguishes subtle signals from backgrounds.

Likelihood ratio test compares null and alternative hypotheses.

Strategies discussed for simplifying background modeling.

Abstract

In particle physics and cosmology, distinguishing subtle new physics signals from established backgrounds is a fundamental and persistent challenge for phenomenologists. This paper discuss a simple and robust statistical framework to evaluate the compatibility of highly motivated (HM) theoretical models with the residuals of experimental results, focusing on scenarios where the data appear consistent with background predictions. A likelihood ratio test is developed that compares null and alternative hypotheses, emphasizing cases where new physics introduces small deviations from the background. The practicality of the framework is highlighted, and in addition to its limitations, strategies to simplify complex background modeling are discussed.