Neural networks technique based signal-from-background separation and design optimization for a W/quartz fiber calorimeter

TL;DR

This paper demonstrates that neural networks can effectively distinguish signal from background in a W/quartz fiber calorimeter, significantly enhancing signal detection efficiency and improving the signal-to-background ratio.

Contribution

The study applies neural network techniques to calorimeter data, achieving high signal efficiency and substantial background suppression, which is a novel approach in this context.

Findings

Neural networks can efficiently separate signal from background.

Signal enhancement over background is of the order of several thousands.

High signal efficiency is maintained during separation.

Abstract

We present a signal-from-background separation study based on neural networks technique applied to a W/quartz fiber calorimeter. Performance results in terms of signal efficiency and improvement of the signal-to-background ratio are presented. We conclude that by using neural networks we can efficiently separate signal from background and achieve a signal enhancement over the background of the order of several thousands at high efficiency.