Parametrizing the Detector Response with Neural Networks

TL;DR

This paper introduces a neural network approach to parameterize detector response summary statistics, including the mode, to improve calibration and analysis sensitivity in high energy physics experiments.

Contribution

It presents a novel neural network-based method for learning detector response summary statistics, especially the mode, which has not been previously combined with deep learning.

Findings

Neural networks can effectively learn the mode of detector response distributions.

The approach improves calibration accuracy in high energy jet measurements.

Neural network parameterization enhances data utility in physics analysis.

Abstract

In high energy physics, characterizing the response of a detector to radiation is one of the most important and basic experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper discusses how to parameterize summary statistics of the detector response using neural networks. In particular, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.