A non-parametric peak finder algorithm and its application in searches for new physics

TL;DR

This paper introduces a non-parametric peak detection algorithm designed to identify significant peaks in data with uncertainties, aiding searches for new physics in high-energy collision experiments.

Contribution

The paper presents a novel non-parametric algorithm for peak detection that effectively handles uncertainties, with applications demonstrated in high-energy physics data analysis.

Findings

Successfully detects significant peaks in simulated collision data

Handles both statistical and systematic uncertainties effectively

Facilitates general searches for new physics in invariant-mass spectra

Abstract

We have developed an algorithm for non-parametric fitting and extraction of statistically significant peaks in the presence of statistical and systematic uncertainties. Applications of this algorithm for analysis of high-energy collision data are discussed. In particular, we illustrate how to use this algorithm in general searches for new physics in invariant-mass spectra using pp Monte Carlo simulations.