Hardware-efficient neural networks for FPGA-based radio triggering of extensive air showers

Vesselin Dimitrov; Alperen Aksoy; Ilja Bekman; Markus Cristinziani; Eric-Teunis de Boone; Qader Dorosti; Chimezie Eguzo; Stefan Heidbrink; Stefan van Waasen; Andre Zambanini

doi:10.22323/1.538.0034

arXiv · 2609.16973

Hardware-efficient neural networks for FPGA-based radio triggering of extensive air showers

Vesselin Dimitrov·Alperen Aksoy·Ilja Bekman·Markus Cristinziani·Eric-Teunis de Boone·Qader Dorosti·Chimezie Eguzo·Stefan Heidbrink·Stefan van Waasen·Andre Zambanini

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Abstract

We present a hardware-efficient hybrid trigger for FPGA-based radio detection of extensive air showers. The hybrid design consists of a lightweight denoiser that cleans raw ADC traces and a compact classifier that operates on the denoised output, enabling robust near-threshold pulse detection in high-interference environments. Both neural networks are trained quantization-aware. Signals are generated from detector-folded CoREAS/CORSIKA simulations and embedded into measured noise to form a realistic benchmark. The trigger reaches an AUC of 0.992 while fitting comfortably within the resource budget of a Zynq-7000 Z-7020, with microsecond-scale latency and sub-watt power consumption. RTL validation confirms agreement between the fixed-point hardware and the quantized software model, demonstrating that neural denoising combined with classification provides reliable, low-cost radio triggering in noisy environments.

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Vesselin Dimitrov, Alperen Aksoy, Ilja Bekman, Markus Cristinziani, Eric-Teunis de Boone, Qader Dorosti, Chimezie Eguzo, Stefan Heidbrink, Stefan van Waasen, Andre Zambanini. 2026-09-15. Hardware-efficient neural networks for FPGA-based radio triggering of extensive air showers. https://doi.org/10.22323/1.538.0034

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