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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.

Event Detection and Reconstruction Using Neural Networks in TES Devices: a Case Study for Athena/X-IFU