What question did this study set out to answer?

The aim is to enhance the gamma-ray interaction position determination within AGATA crystals using neural networks.

February 7, 2026

Neural networks for 3D characterisation of AGATA crystals

Key Points

The aim is to enhance the gamma-ray interaction position determination within AGATA crystals using neural networks.
Utilizes Long Short-Term Memory (LSTM) neural networks
Trained on data from IPHC Strasbourg
Employs a custom masked loss function for training
Produces an experimental database from AGATA crystals
The new method outperforms the existing simulated database
Generates a more accurate experimental database than the conventional PSCS algorithm

Abstract

Precise localisation of gamma-ray interactions is crucial for the performance of the Advanced GAmma Tracking Array (AGATA). The Pulse Shape Analysis (PSA) method used for the position estimation of gamma-ray interactions relies on a simulated signal database. The Pulse Shape Comparison Scanning (PSCS) method was used to scan AGATA crystals in order to produce an experimental database of signals. This paper presents a novel approach using Long Short-Term Memory (LSTM) neural networks to determine the 3D interaction position of gamma rays within AGATA crystals, trained on data from IPHC Strasbourg, allowing for the construction of an experimental database. A custom masked loss function is introduced to enable training with incomplete position information. The database generated by this new method outperforms the existing simulated database, and the experimental database obtained from the conventional PSCS algorithm.

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