arXiv subjects

Qader Dorosti

Publications and source records attributed to Qader Dorosti.

5 recordsLinked to original sources

arXiv2026

A dual-polarization whitened-template trigger for real-time radio detection of extensive air showers

Autonomous radio stations require a first-stage trigger that rejects measured background while preserving weak extensive-air-shower pulses under tight hardware constraints. We present a dual-polarization trigger based on a 16-sample whitened pulse template evaluated continuously on the orthogonal north-south and east-west channels of an Auger Engineering Radio Array station. The template is derived from detector-folded air-shower pulses and whitened using the covariance of measured background. Responses from both polarizations are combined within a fixed timing radius and compared with a calibrated threshold. On an independent test set, the trigger reaches an efficiency of 0.97, with a 95 percent confidence interval of 0.96 to 0.98, at a frame-equivalent candidate rate of 3.39 kHz. At the same operating point, an unwhitened dual-polarization template reaches 0.41 efficiency and an amplitude trigger reaches 0.16. Efficiency remains at least 0.94 in every populated signal-to-noise bin. Replay of 159.29 ms of continuous measured background yields a score-cluster rate of 6.64 kHz, below the 71.4 kHz capacity of the downstream module. The FPGA implementation accepts one dual-polarization sample per clock, closes timing at 200 MHz, and uses 2377 lookup tables, 5330 registers, and 32 digital signal-processing blocks. Register-transfer-level simulation agrees bit for bit with the reference implementation for 400 traces. These results show that background covariance, pulse morphology, and dual-polarization consistency can be combined in a compact real-time radio trigger.

astro-ph.IM↗

arXiv2026

Hybrid neural denoising for resource-efficient near- and sub-threshold radio triggering of extensive air showers

Autonomous radio self-triggering for extensive air showers must reject variable radio-frequency interference while preserving sensitivity to weak pulses and remaining compatible with station-level edge hardware. This work presents a hybrid neural trigger in which waveform recovery and signal classification are treated as a single deployment-constrained problem. A compact convolutional denoiser maps a noisy single-channel trace to a cleaned estimate of the air-shower pulse, which is then evaluated by a compact classifier. The method is tested with measured high-interference background traces and detector-folded air-shower pulses from the Pierre Auger Offline simulation chain, with signals concentrated in the near- and sub-threshold regime. Model selection and deployment are linked through hyperparameter optimisation, quantisation-aware training, fixed-point quantisation, hls4ml firmware export, high-level synthesis, and register-transfer-level validation. The denoiser alone turns a simple peak-envelope decision into an efficient weak-pulse trigger, showing that the cleaned waveform carries trigger-relevant information beyond a final classifier score. In the full denoiser-classifier chain, the hybrid trigger improves signal-background separation and efficiency at fixed false-positive rates: at a false-positive rate of 10^-4, it retains about 41% of held-out signal traces in the weak-signal benchmark, while the classical peak-envelope trigger retains none. The cleaned waveform preserves timing and peak-amplitude structure for station-level diagnostics, feature extraction, and selective readout. The firmware meets timing on representative FPGA targets with microsecond-scale latency and compact arithmetic-resource demand. These results establish hybrid neural denoising as a practical route toward radio-only triggering for weak and inclined air-shower signals in noisy environments.

astro-ph.IM↗

arXiv2025

Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications

This paper examines the use of Quantized Neural Networks (QNNs) for two resource-constrained scientific applications: automated calibration of semi- conductor quantum bits (qubits) and scientific particle detectors. We evaluate the trade-offs between Post-Training Quantization (PTQ), Quantization-Aware Train- ing (QAT), and ultra-low-bit Binary Neural Networks (BNNs) with respect to la- tency and resource usage. Our results demonstrate that PTQ and QAT are easily implementable solutions achieving a four-fold reduction in memory usage for U- shaped CNN (U-Net) architectures, whereas BNNs are better suitable for use cases with challenging latency requirements. For the training of non-differentiable custom BNNs , we propose a novel, hardware-constrained learning approach using Genetic Algorithms (GAs). We showcase a Look-Up Table (LUT)-based BNN architecture suitable for direct conversion to Very High-Speed Integrated Circuit Hardware De- scription Language (VHDL) via the HCL4BNN framework. This method achieves nanosecond-scale inference latencies at 10 ns to 15 ns without requiring specialized Digital Signal Processor (DSP) or Block RAM (BRAM) resources.

cs.NE↗

arXiv2025

Subatomic Heroes

Sharing the amazing achievements of the (particle) physics world with the general public is at the heart of the mission of the Subatomic Heroes, based at the University of Siegen, Germany. Originally this started out as an endeavor of theoretical particle physics, now we are steadily spreading out to cover and include more branches of physics and science. Our activities range from merging art with public physics lectures via marvelous artistic performances at the local theater, over dedicated events for high-school students, to our Subatomic Heroes channel on Instagram and TikTok where you may also find out when and where our famous "hadronic ice-cream" will be served next! So follow us on https://www.instagram.com/subatomic_heroes and https://www.tiktok.com/@subatomic_heroes.

physics.ed-ph↗

arXiv2025

AI-Enhanced Self-Triggering for Extensive Air Showers: Performance and FPGA Feasibility

Autonomous self-triggering for radio detection of extensive air showers remains a long-standing challenge, particularly in environments dominated by strong and variable radio-frequency interference. Current radio arrays usually rely on external particle-detector triggers: while this lowers thresholds for vertical showers, it excludes very inclined events, where radio detection is uniquely powerful because the particle cascade is absorbed while the radio pulse remains measurable. In this work, I present a proof-of-principle study showing that deep-learning-based triggering can overcome these limitations, operating robustly under realistic high-interference conditions and within the strict latency constraints of large-scale observatories. Using measured noise traces combined with simulated cosmic-ray pulses, a fully convolutional network was trained and optimised for MHz-scale trigger-trial rates, and its performance was compared to a simple threshold-based trigger, which proved markedly less efficient at the same false-positive rate. The trained model was then quantised with HLS4ML and synthesised with Vitis HLS for multiple FPGA targets. The quantised implementation preserves the floating-point model performance (AUC_float = 0.997, AUC_quant = 0.996) while achieving microsecond-scale inference latencies.These results show that modern FPGA-based AI inference can provide low-latency, radio-only triggering for next-generation cosmic-ray observatories, particularly in regions with strong and variable radio backgrounds. This capability unlocks access to weak and highly inclined air-shower signals--even in noisy environments--thereby broadening the energy range, extending sky coverage, and opening the path toward ultra-high-energy neutrino detection with ground-based radio arrays.

astro-ph.IM↗