New likelihoods for shape analysis

TL;DR

This paper introduces a novel likelihood function based on data moments for shape analysis, offering advantages over traditional likelihoods in signal searches, especially with multiple variables, demonstrated through toy models relevant to high-energy physics.

Contribution

The paper develops a moment-based likelihood formalism for shape analysis, providing a self-contained framework that can replace standard likelihoods in non-local signal searches.

Findings

Moment-based likelihoods can replace unbinned likelihoods in signal searches.

The approach simplifies analysis by avoiding Monte Carlo fitting.

Tests on toy models show effectiveness in high-energy physics scenarios.

Abstract

We introduce a new kind of likelihood function based on the sequence of moments of the data distribution. Both binned and unbinned data samples are discussed, and the multivariate case is also derived. Building on this approach we lay out the formalism of shape analysis for signal searches. In addition to moment-based likelihoods, standard likelihoods and approximate statistical tests are provided. Enough material is included to make the paper self-contained from the perspective of shape analysis. We argue that the moment-based likelihoods can advantageously replace unbinned standard likelihoods for the search of non-local signals, by avoiding the step of fitting Monte-Carlo generated distributions. This benefit increases with the number of variables simultaneously analyzed. The moment-based signal search is exemplified and tested in various 1D toy models mimicking typical high-energy signal--background configurations. Moment-based techniques should be particularly appropriate for the searches for effective operators at the LHC.