arXiv subjects

Zhaocheng Gong

Publications and source records attributed to Zhaocheng Gong.

3 recordsLinked to original sources

arXiv2026

LeoNet: A Machine Learning Method for Binary Pulsar Classification

Binary pulsars provide valuable laboratories for testing theories of gravity, but orbital Doppler shifts complicate their detection. Fourier-domain acceleration and jerk searches address this challenge via matched filtering, but at substantial computational cost. We present LeoNet, a convolutional neural network that uses ten learnable filters to extract features of signals affected by Doppler shifts. The resulting ten-channel feature map provides a compact, lower-dimensional alternative to an explicitly sampled acceleration-jerk response grid and is analysed by a convolutional classifier to identify candidate signals. For simulated observations lasting 500 s, LeoNet achieves a mean relative reduction in false negative rate of 55.5% across five sampling intervals compared with the evaluated PRESTO acceleration-search configuration. TensorRT-optimised LeoNet processes each 500 s observation in 3.44-4.37 ms in FP32 on an NVIDIA H100 PCIe GPU across eight sampling intervals, including preprocessing, inference, and postprocessing. At a sampling interval of 128 microseconds, its mean processing time is 3.54 ms, compared with 1.767 s for PRESTO FDAS on an AMD EPYC 9825 CPU with search-frequency limits of 96-1000 Hz, corresponding to an approximately 499-fold speedup in the measured processing time. These results suggest that LeoNet has the potential to improve detection performance, while its millisecond-scale processing time supports its use as a candidate-identification stage in real-time binary pulsar search pipelines.

astro-ph.IM↗

arXiv2026

MARS: A Lightweight Morphology-Aware RFI Segmentation Network for Mask-Guided Mitigation in Radio Astronomy

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 $0.978$ and a precision of $0.995$. It retains $97.6\%$ of the injected dispersed-signal fluence in clean patches and $96.4\%$ 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 $0.90$--$0.99$ 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 $6.2\times$--$7.0\times$ over the fastest tested multi-threaded filtool configurations on an AMD EPYC 9825 CPU.

astro-ph.IM↗

arXiv2026

The SPOTLIGHT Pulsar Search Pipeline: A GPU-Accelerated FFT Approach

We present the pulsar search component of SPOTLIGHT (Survey for sPoradic radiO bursTs via a commensaL multI-beam Gpu-powered Hpc at the gmrT), a GPU-accelerated commensal backend operating at the upgraded Giant Metrewave Radio Telescope (uGMRT). While SPOTLIGHT is primarily designed for real-time detection and localisation of fast radio bursts (FRBs), it simultaneously records a subset of beamformed data products for periodicity searches without requiring dedicated telescope time. To process the large data volumes generated by the survey, we have developed a scalable FFT-based pulsar search pipeline that combines radio-frequency interference mitigation, GPU-accelerated dedispersion and periodicity searches, multi-beam candidate sifting, efficient folding and machine-learning classification. Using population synthesis and archival uGMRT observations, we estimate that a fully operational SPOTLIGHT survey with 160 PC and one IA beam could discover $\sim$ 450 new pulsars, probing both high-sky coverage and faint pulsars over three and a half years of commensal observations. The pipeline has been validated on GMRT Cycle 48 and 49 observations (i.e. April 2025 to Mar 2026), successfully re-detecting numerous known pulsars with a wide range of Period, DM and flux densities, and is currently operational for SPOTLIGHT commensal data processing. We describe the SPOTLIGHT observing system, pulsar survey design, search parameter space, candidate-selection strategy, current status, and future developments. SPOTLIGHT demonstrates the scientific potential of commensal pulsar surveys and serves as a pathfinder for real-time, large-scale pulsar and transient searches in the SKA era.

astro-ph.HE↗