Using Monte Carlo to optimize variable cuts
Erik Elfgren
TL;DR
This paper introduces a Monte Carlo-based approach to optimize variable cuts, significantly improving the signal-to-noise ratio compared to manual selection methods.
Contribution
The paper presents a novel Monte Carlo method for optimizing variable cuts, enhancing signal detection efficiency over traditional manual techniques.
Findings
Higher signal-to-noise ratio achieved
Method outperforms manual cut selection
Validated through evaluation
Abstract
A Monte Carlo method to optimize cuts on variables is presented and evaluated. The method gives a much higher signal to noise ratio than does a manual choice of cuts.
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Taxonomy
TopicsScientific Research and Discoveries · Computational Physics and Python Applications · Particle physics theoretical and experimental studies