Proposal of an FPGA Neural Network Trigger for Recognizing the Chemical Composition of Ultra-High-Energy Cosmic Rays in the Pierre Auger Surface Detector

The standard first-level trigger in the Pierre Auger Observatory surface detectors (data analysis in FPGAs immediately after digitization in ADCs) was developed when FPGAs were relatively simple and expensive. Thus, the algorithms developed in the 1990s are relatively simple. Substantial progress in electronics now allows the implementation of very sophisticated mathematical algorithms in very efficient systems and relatively inexpensive FPGAs. A neural network was recently developed as an alternative trigger for recognizing neutrino-induced showers, providing relatively high efficiency and allowing signal profiles from Auger photomultiplier tubes of water-Cherenkov detectors originating from atmospheric showers induced by high background neutrinos to be distinguished from other showers. The chemical composition of ultra-high-energy cosmic rays (UHECR) is complex and still not fully known. Additional tools for online, real-time analysis of potential chemical composition could help address this problem. We simulated a large dataset using the CORSIKA package (for simulating the development of extensive air showers in the atmosphere) and OffLine (for generating Cherenkov radiation in surface detectors and digitizing photomultiplier signals in an analog-to-digital converter). These data served as input to a neural network (using MATLAB tools) that attempted to identify the type of initiating particle. Ultimately, the neural network was implemented on an Arria 10 FPGA to generate real-time neural network triggers directly on the pampas in the surface detector. Both simulations and measurements on the Arria 10 development kit confirmed a high degree of reliability.