Performance of a proposed event-type based analysis for the Cherenkov Telescope Array

TL;DR

This paper evaluates an event-type based analysis approach for the Cherenkov Telescope Array, using machine learning to classify events by quality, aiming to improve sensitivity and resolution over traditional data cuts.

Contribution

It introduces a machine learning classification method for event types in CTA data analysis, enhancing performance metrics compared to classical quality cut procedures.

Findings

Machine learning improves event classification accuracy.

Including lower quality events increases sensitivity.

Performance metrics like angular and energy resolution are enhanced.

Abstract

The Cherenkov Telescope Array (CTA) will be the next-generation observatory in the field of very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle physics. Classically, data analysis in the field maximizes sensitivity by applying quality cuts on the data acquired. These cuts, optimized using Monte Carlo simulations, select higher quality events from the initial dataset. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs). An alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. In this approach, events are divided into sub-samples based on their reconstruction quality, and a set of IRFs is calculated for each sub-sample. The sub-samples are then combined in a joint analysis, treating them as independent observations. This leads to an improvement in performance parameters such as sensitivity, angular and energy resolution. Data loss is reduced since lower quality events are included in the analysis as well, rather than discarded. In this study, machine learning methods will be used to classify events according to their expected angular reconstruction quality. We will report the impact on CTA high-level performance when applying such an event-type classification, compared to the classical procedure.