Unveiling the rarest morphologies of the LOFAR Two-metre Sky Survey radio source population with self-organised maps

TL;DR

This paper employs self-organising maps to classify and identify rare radio source morphologies in the LOFAR Two-metre Sky Survey, demonstrating the method's effectiveness in large astronomical datasets.

Contribution

It introduces a novel application of self-organising maps with rotation and flipping invariance for morphological classification of radio sources in LOFAR data.

Findings

Successfully identified rare radio source morphologies

Demonstrated the method's scalability to large sky areas

Provided an interactive visualization tool for data exploration

Abstract

The Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) is a low-frequency radio continuum survey of the Northern sky at an unparalleled resolution and sensitivity. In order to fully exploit this huge dataset and those produced by the Square Kilometre Array in the next decade, automated methods in machine learning and data-mining will be increasingly essential both for morphological classifications and for identifying optical counterparts to the radio sources. Using self-organising maps (SOMs), a form of unsupervised machine learning, we created a dimensionality reduction of the radio morphologies for the $\sim$25k extended radio continuum sources in the LoTSS first data release, which is only $\sim$2 percent of the final LoTSS survey. We made use of \textsc{PINK}, a code which extends the SOM algorithm with rotation and flipping invariance, increasing its suitability and effectiveness for training on astronomical sources. After training, the SOMs can be used for a wide range of science exploitation and we present an illustration of their potential by finding an arbitrary number of morphologically rare sources in our training data (424 square degrees) and subsequently in an area of the sky ($\sim$5300 square degrees) outside the training data. Objects found in this way span a wide range of morphological and physical categories: extended jets of radio active galactic nuclei, diffuse cluster haloes and relics, and nearby spiral galaxies. Finally, to enable accessible, interactive, and intuitive data exploration, we showcase the LOFAR-PyBDSF Visualisation Tool, which allows users to explore the LoTSS dataset through the trained SOMs.