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).
@article{arxiv.1808.07781,
title = {Identifying transient and variable sources in radio images},
author = {Antonia Rowlinson and Adam J. Stewart and Jess W. Broderick and John D. Swinbank and Ralph A. M. J. Wijers and Dario Carbone and Yvette Cendes and Rob Fender and Alexander van der Horst and Gijs Molenaar and Bart Scheers and Tim Staley and Sean Farrell and Jean-Mathias Grießmeier and Martin Bell and Jochen Eislöffel and Casey J. Law and Joeri van Leeuwen and Philippe Zarka},
journal= {arXiv preprint arXiv:1808.07781},
year = {2019}
}