Signal Modelling with Overdetermined Morphing Technique

TL;DR

This paper introduces an overdetermined morphing technique for more accurate and precise signal modeling in high-energy physics, outperforming traditional methods especially over extended phase space regions.

Contribution

The paper extends the Monte Carlo morphing technique by employing an overdetermined basis, improving statistical power, uniformity, and precision in signal modeling.

Findings

Better statistical power and uniformity in predictions.

Enhanced precision over traditional morphing methods.

More effective modeling of extended phase space regions.

Abstract

Precise modelling of a signal in processes with multiple observables, exhibiting a complex dependency on the underlying parameters, is often a difficult and challenging task. Predicting the results of experimental measurements in high-energy physics reactions serves a good example. The reaction rates and distributions of momenta of the final state particles, depend on the parameters of the underlying physics model in a non-trivial way. The conventional way to predict the experimental of observables is to use the Monte Carlo morphing technique on a finite discrete set of simulated samples. In this article we extend this technique by using the overdetermined morphing basis. We show that our approach yields a better result than the traditional technique in terms of the statistical power and uniformity of the resulting prediction, and delivers better precision. We further demonstrate that the overdetermined morphing is better suited for description of extended regions of phase space than the conventional morphing. We use the modelling of kinematic distributions of the final state particles produced in decays of non-Standard Model Higgs bosons as an example.