A compact morphology-aware U-Net with an astronomy-preservation loss achieves F1 0.978 on synthetic RFI masks, keeps 97.6% of injected dispersed-signal fluence, and runs 6.2-7.0x faster than filtool in compute-only benchmarks.
Development of production-ready GPU data processing pipeline software for AstroAccelerate
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abstract
Upcoming large scale telescope projects such as the Square Kilometre Array (SKA) will see high data rates and large data volumes; requiring tools that can analyse telescope event data quickly and accurately. In modern radio telescopes, analysis software forms a core part of the data read out, and long-term software stability and maintainability are essential. AstroAccelerate is a many core accelerated software package that uses NVIDIA(R) GPUs to perform realtime analysis of radio telescope data, and it has been shown to be substantially faster than realtime at processing simulated SKA-like data. AstroAccelerate contains optimised GPU implementations of signal processing tools used in radio astronomy including dedispersion, Fourier domain acceleration search, single pulse detection, and others. This article describes the transformation of AstroAccelerate from a C-like prototype code to a production-ready software library with a C++ API and a Python interface; while preserving compatibility with legacy software that is implemented in C. The design of the software library interfaces, refactoring aspects, and coding techniques are discussed.
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astro-ph.IM 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
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MARS: A Lightweight Morphology-Aware RFI Segmentation Network for Mask-Guided Mitigation in Radio Astronomy
A compact morphology-aware U-Net with an astronomy-preservation loss achieves F1 0.978 on synthetic RFI masks, keeps 97.6% of injected dispersed-signal fluence, and runs 6.2-7.0x faster than filtool in compute-only benchmarks.