Gamma-ray detector and mission design simulations

TL;DR

This paper reviews the role of simulations in gamma-ray astronomy, covering detector design, performance evaluation, and data analysis, highlighting their importance and limitations in the field.

Contribution

It provides a comprehensive overview of simulation methods, software tools, and performance metrics used in gamma-ray detector design and analysis.

Findings

Simulations are essential for detector performance characterization.

Various software packages support different aspects of gamma-ray simulations.

Simulations help optimize detector design and data analysis techniques.

Abstract

Detectors for gamma-ray astronomy are complex: they often comprise multiple sub-systems and utilize new and/or custom-developed detector components and readout electronics. Gamma rays are typically not detected directly: ground-based detectors measure extensive air showers of charged particles initiated by cosmic gamma-rays, and even so-called "direct detection" experiments on balloons or satellites usually reconstruct the incoming gamma-ray photons' properties from the secondary particles produced in the detector. At the same time, there are few "standard candles" and no feasible terrestrial sources of high-energy and very-high-energy gamma rays that could be used to calibrate the detectors. Simulations of particles interacting in the atmosphere and/or with the instrument are thus ubiquitous in gamma-ray astronomy. These simulations are used in event reconstruction and data analysis, to characterize detector performance, and to optimize detector design. In this chapter, we give an overview of how and why simulations are used in gamma-ray astronomy, as well as their limitations. We discuss extensive air shower simulations, simulations of gamma rays and secondary particles interacting in the detector, and simulations of the readout electronics. We provide examples for software packages that are used for various aspects of simulations in gamma-ray astronomy. Lastly, we describe the performance metrics and instrument response functions that are generated from these simulations, which are critical to instrument design and data analysis.