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.
Highlights
Any scientific experiment would be incomplete if only the collected data were reported
This technique can be thought of as fitting an ellipse to the pixels: a likelihood function that depends on the Hillas parameters is maximized under the assumption that the Cherenkov light from a shower initiated by a gamma ray would produce an elliptical shape in the camera
The dim and small shower images below 100 GeV can result in parameters values affected by large fluctuations and systematic uncertainties, which is the reason why the instrument response function of Imaging Atmospheric Cherenkov Telescopes (IACTs) deteriorates at lower energies
Summary
Any scientific experiment would be incomplete if only the collected data were reported. Unlike the frequentist approach where the goal is to provide a statement about the long-run performance of a test statistic, in Bayes theory we are not interested in hypothetical infinite experiments but in calculating the probability of hypotheses from the observed data and from our prior knowledge of them. The most common event reconstruction technique is based on the moments (up to the second order) of the pixel amplitudes in the camera, referred to as Hillas parameters [13] This technique can be thought of as fitting an ellipse to the pixels: a likelihood function that depends on the Hillas parameters is maximized under the assumption that the Cherenkov light from a shower initiated by a gamma ray would produce an elliptical shape in the camera. More refined techniques have been developed, aimed at improving the inference analysis on the gamma ray properties starting from the Hillas parameters (see Section 2.5)
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