Identifying transient and variable sources in radio images

TL;DR

This paper explores machine learning methods to optimize automated pipelines for detecting and analyzing transient and variable radio sources in large-scale survey data, enhancing efficiency and adaptability.

Contribution

It introduces versatile machine learning strategies and Python tools for training parameters in automated radio transient detection pipelines, applicable across various datasets.

Findings

Demonstrated effective machine learning strategies with LOFAR data

Developed publicly available Python tools for pipeline parameter training

Applicable to diverse radio transient datasets and pipelines

Abstract

With the arrival of a number of wide-field snapshot image-plane radio transient surveys, there will be a huge influx of images in the coming years making it impossible to manually analyse the datasets. Automated pipelines to process the information stored in the images are being developed, such as the LOFAR Transients Pipeline, outputting light curves and various transient parameters. These pipelines have a number of tuneable parameters that require training to meet the survey requirements. This paper utilises both observed and simulated datasets to demonstrate different machine learning strategies that can be used to train these parameters. The datasets used are from LOFAR observations and we process the data using the LOFAR Transients Pipeline; however the strategies developed are applicable to any light curve datasets at different frequencies and can be adapted to different automated pipelines. These machine learning strategies are publicly available as Python tools that can be downloaded and adapted to different datasets (https://github.com/AntoniaR/TraP_ML_tools).