Statistical Tools for Imaging Atmospheric Cherenkov Telescopes

TL;DR

This paper reviews statistical tools used in analyzing data from Imaging Atmospheric Cherenkov Telescopes, highlighting their role in the full analysis process of detecting and characterizing astrophysical gamma-ray sources.

Contribution

It provides a comprehensive summary of the most common statistical methods applied in IACT data analysis, including their applications and references.

Findings

Summarizes key statistical techniques for IACT data analysis

Highlights the importance of image reconstruction and background discrimination

Provides references for detailed methodologies

Abstract

The development of Imaging Atmospheric Cherenkov Telescopes (IACTs) unveiled the sky in the teraelectronvolt regime, initiating the so-called "TeV revolution", at the beginning of the new millennium. This revolution was also facilitated by the implementation and adaptation of statistical tools for analyzing the shower images collected by these telescopes and inferring the properties of the astrophysical sources that produce such events. Image reconstruction techniques, background discrimination, and signal-detection analyses are just a few of the pioneering studies applied in recent decades in the analysis of IACTs data. This (succinct) review has the intent of summarizing the most common statistical tools that are used for analyzing data collected with IACTs, focusing on their application in the full analysis chain, including references to existing literature for a deeper examination.