Event detection and reconstruction using neural networks in TES Devices: a case study for Athena/X-IFU

Transition Edge Sensors detector devices, like the core of the X-IFU instrument that will be on-board the Athena X-ray Observatory, produce current pulses as a response to the incident X-ray photons. The reconstruction of these pulses has been traditionally performed by means of a triggering algorithm based on the derivative signal overcoming a threshold (detection) followed by an optimal filtering (to retrieve the energy of each event). However, when the arrival of the photons is very close in time, the triggering algorithm is incapable of detecting all the individual pulses which are thus piled-up. In order to improve the efficiency of the detection and energy-retrieval process, we study here an alternative approach based on Machine Learning techniques to process the pulses. For this purpose, we construct and train a series of Neural Networks (NNs) not only for the detection but also for the recovering of the arrival time and the energy of simulated X-ray pulses. The data set used to train the NNs consists of simulations performed with the sixte/xifusim software package, the Athena/X-IFU official simulator. The performance of our NN classification clearly surpasses the detection performance of the classical triggering approach for the full range of photon energy combinations, showing excellent metrics and very competitive computing efficiency. However, the precision obtained for the recovery of the energy of the photons cannot currently compete with the standard optimal filtering algorithm, despite its much better computing efficiency.This paper is supported by European Union’s Horizon 2020 research and innovation program under grant agreement No 871158, project AHEAD2020. JP-G and PG acknowledge the project “Machine Learning for the adaptation and improvement of applications” (MALGAMA) under the CSIC Intramural 20152170 program. The authors gratefully acknowledge the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the Instituto de Física Corpuscular, IFIC (CSIC-UV). The authors also acknowledge the computer resources provided by the Clúster d’Altes Prestacions per Inteligència Artificial at the Instituto de Investigación en Inteligencia Artificial (IIIA-CSIC) and of the Grupo de Astrofisica y Cosmologia computacional at the Universidad Autónoma de Madrid (UAM)