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The analog signals generated in the read-out electronics of radiationdetectors are shaped prior to the digitization in order to improve the signalto noise ratio (SNR). The real amplitude of the analog signal is then obtainedusing digital filters, which provides information about the energy deposited inthe detector. The classical digital filters have a good performance in idealsituations with Gaussian electronic noise and no pulse shape distortion.However, high-energy particle colliders, such as the Large Hadron Collider(LHC) at CERN, can produce multiple simultaneous radiation events, whichproduce signal pileup. The performance of classical digital filtersdeteriorates in these conditions since the signal pulse shape gets distorted.In addition, this type of experiments produces a high rate of collisions, whichrequires high throughput data acquisitions systems. In order to cope with theseharsh requirements, new read-out electronics systems are based onhigh-performance FPGAs, which permit the utilization of more advanced real-timesignal reconstruction algorithms. In this paper, a deep learning method isproposed for real-time signal reconstruction in high pileup radiationdetectors. The performance of the new method has been studied using simulateddata and the results are compared with a classical FIR filter method. Inparticular, the signals and FIR filter used in the ATLAS Tile Calorimeter areused as benchmark. The implementation, resources usage and performance of theproposed Neural Network algorithm in FPGA are also presented.

FPGA implementation of a deep learning algorithm for real-time signal reconstruction in radiation detectors under high pile-up conditions