What question did this study set out to answer?

This work aims to enhance the Cherenkov Telescope Array Observatory's data analysis by introducing an event-type-based approach to optimize reconstruction quality.

July 12, 2026Open Access

Enhancing the Cherenkov Telescope Array Observatory high-level performance through an event-type-based analysis

JBJ. BerneteCentro de Investigaciones Energéticas, Medioambientales y Tecnológicas SGS. García-SotoCentro de Investigaciones Energéticas, Medioambientales y Tecnológicas THT. HassanCentro de Investigaciones Energéticas, Medioambientales y Tecnológicas

Key Points

This work aims to enhance the Cherenkov Telescope Array Observatory's data analysis by introducing an event-type-based approach to optimize reconstruction quality.
Implemented an event-type-based analysis using simulated data for the Cherenkov Telescope Array Observatory.
Trained a multi-layer perceptron neural network to predict direction reconstruction error for events.
Computed independent instrument response functions for various event types and analyzed them using high-level analysis tools.
Achieved a 25% to 50% improvement in spatial resolving power compared to the standard analysis.
Gained approximately 25% increase in sensitivity over the traditional methods.
Demonstrated that using event-type analysis has strong implications for analyzing crowded regions and searching for Dark Matter signatures.

Abstract

The analysis traditionally employed by Imaging Atmospheric Cherenkov Telescopes involves optimizing quality cuts to select a sub-sample of high-quality events. These events are used for the scientific interpretation of the data, employing a single set of Instrument Response Functions (IRFs). All selected events are treated equally and assumed to be well represented by these IRFs, while the rest are discarded. An alternative approach, successfully applied in experiments such as Fermi -LAT, is an event-type-based analysis. This method divides datasets into subsamples, each containing events of a given expected reconstruction quality. IRFs are computed for each subsample independently, improving the accuracy with which IRFs represent the reconstruction quality of each event. The high-level analysis of these subsamples is performed treating them as independent observations, each with their own set of IRFs, and analyzed jointly. In this work we present a proof-of-concept implementation of an event-type-based analysis for the future Cherenkov Telescope Array Observatory (CTAO) using simulated data. A neural network (specifically a multi-layer perceptron) is trained to predict the direction reconstruction error of each event, and the simulated dataset is divided into event types based on this predicted variable. We compute IRFs for each event type and compare them with those from the standard analysis (without event types). Finally, we simulate observations using these event-type-wise IRFs and analyze them with high-level analysis tools to test the performance of both approaches. This implementation demonstrates notable improvements: 25% to 50% boost in spatial resolving power and ∼ 25% in sensitivity. This boost in performance will have strong implications in the scientific exploitation of the CTAO data, especially in crowded regions such as the Galactic Plane or searching for spectral signatures like Dark Matter annihilation lines.

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