CNNCat: Categorizing high-energy photons in a Compton/Pair Telescope with Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based classification algorithm for high-energy photon events in a Compton/Pair telescope, improving onboard event classification and data reconstruction accuracy.

Contribution

The paper presents a novel CNN architecture for classifying electromagnetic interactions using low-level detector data, tailored for high-energy astrophysics telescopes.

Findings

High classification accuracy achieved with CNN model

Effective discrimination of interaction types in detector data

Potential for improved onboard data handling in telescopes

Abstract

A Compton/Pair telescope, designed to provide spectral resolved images of cosmic photons from sub-MeV to GeV energies, records a wealth of data in a combination of tracking detector and calorimeter. Onboard event classification can be required to decide on which data to downlink with priority, given limited data-transfer bandwidth. Event classification is also the first and one of the most crucial steps in reconstructing data. Its outcome determines the further handling of the event, i.e., the type of reconstruction (Compton, pair) or, possibly, the decision to discard it. Errors at this stage result in misreconstruction and loss of source information. We present a classification algorithm driven by a Convolutional Neural Network. It provides classification of the type of electromagnetic interaction, based solely on low-level detector data. We introduce the task, describe the architecture and the dataset used, and present the performance of this method in the context of the proposed (e-)ASTROGAM and similar telescopes.