Lightweight HI source finding for next generation radio surveys

TL;DR

This paper presents LiSA, a modular, lightweight Python toolkit for automated HI source detection and characterization in large 3D spectral datasets, optimized for next-generation radio surveys like SKA.

Contribution

Introduces LiSA, a novel, portable, and modular set of algorithms and neural networks for efficient HI source finding and analysis in large-scale radio survey data.

Findings

LiSA achieves 95% detection rate for sources with SNR > 3.

The algorithms require minimal user parameters, enhancing usability.

LiSA's components perform well on SKA-like data challenges.

Abstract

Future deep HI surveys will be essential for understanding the nature of galaxies and the content of the Universe. However, the large volume of these data will require distributed and automated processing techniques. We introduce LiSA, a set of python modules for the denoising, detection and characterization of HI sources in 3D spectral data. LiSA was developed and tested on the Square Kilometer Array Science Data Challenge 2 dataset, and contains modules and pipelines for easy domain decomposition and parallel execution. LiSA contains algorithms for 2D-1D wavelet denoising using the starlet transform and flexible source finding using null-hypothesis testing. These algorithms are lightweight and portable, needing only a few user-defined parameters reflecting the resolution of the data. LiSA also includes two convolutional neural networks developed to analyse data cubes which separate HI sources from artifacts and predict the HI source properties. All of these components are designed to be as modular as possible, allowing users to mix and match different components to create their ideal pipeline. We demonstrate the performance of the different components of LiSA on the SDC2 dataset, which is able to find 95% of HI sources with SNR > 3 and accurately predict their properties.