pulseflow 0.1.12

High-performance radio astronomy single pulse search tools

AstroFlow

A GPU-accelerated pipeline for radio single-pulse and fast radio burst searches

AstroFlow is an end-to-end search pipeline for radio single pulses and fast radio bursts (FRBs). It combines CUDA-accelerated dedispersion, RFI mitigation, AI-assisted candidate detection, and publication-ready candidate visualization in a YAML-configured command-line workflow. The pipeline supports FILTERBANK (.fil) and PSRFITS (.fits) observations and is designed for high-throughput offline or near-real-time searches.

Documentation: https://lintian233.github.io/astroflow/

Preview

Installation

Python 3.10 to 3.12 is recommended.

pip install pulseflow

Read the full documentation: https://lintian233.github.io/astroflow/

Source Build

Source builds require CUDA Toolkit 12.6 or newer, GCC/G++ 11.4 or compatible, CMake 3.22 or newer, Conan 2.x, Ninja, and Python 3.10 to 3.12.

git clone https://github.com/lintian233/astroflow
cd astroflow
bash configure.sh
conda activate dev-astroflow-ml
bash build.sh
pip install -e .

Development builds are also published to TestPyPI:

pip install --pre -i https://test.pypi.org/simple/ pulseflow --extra-index-url https://pypi.org/simple

Citation

If AstroFlow is useful in your research, please cite:

ASTROFLOW: A Real-Time End-to-End Pipeline for Radio Single-Pulse Searches
https://doi.org/10.3847/1538-4365/ae4a26

Limitations

• For best throughput, keep individual observations below roughly 300 seconds and below 5 GB per input file. Larger files may become I/O-bound and increase memory pressure.
• When a file produces more than roughly 50 candidates, Python-side candidate object construction and plotting can become the dominant runtime cost.

Roadmap

• Add PSRDADA ingestion and zero-copy memory mapping for high-throughput streaming.
• Reuse dedispersed data products across multi-parameter searches.
• Complete the injection-pulse and model fine-tuning workflow.
• Expand automated test coverage.

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