MARS: A Lightweight Morphology-Aware RFI Segmentation Network for Mask-Guided Mitigation in Radio Astronomy
arXiv:2608.05546
Abstract
Next-generation radio telescopes generate filterbank data at rates that make storing all observations for later offline mitigation impractical. Mitigation must therefore operate in real or near-real time within the search pipeline while preserving dispersed astrophysical signals. CPU tools fit GPU-centred search pipelines poorly, while neural alternatives can be computationally heavy. We present MARS, a GPU-based RFI mitigation pipeline centred on a lightweight Morphology-Aware RFI Segmentation Network. The model is a reduced-width, full-resolution U-Net with a bottleneck containing local, horizontal, and vertical filters to capture compact and elongated RFI structures in the frequency-time plane. Normalisation, patch construction, mask reconstruction, replacement, baseline removal, and output rescaling are also implemented on GPU. Training includes an astronomical-signal preservation loss that discourages false flagging of dispersed pulses. In controlled patch-level tests, MARS achieves an RFI-mask F1 score of and a precision of . It retains of the injected dispersed-signal fluence in clean patches and of the non-overlapping signal fluence in patches containing mixed injected RFI. Ablation experiments show that the astronomical-signal preservation loss particularly improves the protection of compact, low-DM, high-S/N pulses. At filterbank level, period-matched PRESTO candidates recovered after MARS mitigation have median significance ratios of -- relative to filtool. Both methods also recover the known pulsars in two real GMRT observations. On an NVIDIA GH200 GPU, MARS achieves a compute-only speedup of -- over the fastest tested multi-threaded filtool configurations on an AMD EPYC 9825 CPU.
31 pages, 17 figures, 11 tables. Submitted to The Astrophysical Journal Supplement Series (ApJS). Code available at https://github.com/Haalandspring/MARS