PAPER / ARXIV:2609.16973
Vesselin Dimitrov , Alperen Aksoy , Ilja Bekman , Markus Cristinziani , Eric-Teunis de Boone , Qader Dorosti , Chimezie Eguzo , Stefan Heidbrink , Stefan van Waasen , Andre Zambanini
RESUMO
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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