Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies

TL;DR

ClaRAN is an open-source deep learning tool that automatically classifies radio source morphologies quickly and accurately, addressing the challenge of large-scale radio survey data analysis.

Contribution

It introduces ClaRAN, the first end-to-end open-source radio morphology classifier based on Faster R-CNN, capable of locating and classifying radio sources efficiently.

Findings

Achieves >= 90% accuracy in classification

Classifies images in under 200 milliseconds

Successfully locates and associates radio source components

Abstract

The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible. We present ClaRAN - Classifying Radio sources Automatically with Neural networks - a proof-of-concept radio source morphology classifier based upon the Faster Region-based Convolutional Neutral Networks (Faster R-CNN) method. Specifically, we train and test ClaRAN on the FIRST and WISE images from the Radio Galaxy Zoo Data Release 1 catalogue. ClaRAN provides end users with automated identification of radio source morphology classifications from a simple input of a radio image and a counterpart infrared image of the same region. ClaRAN is the first open-source, end-to-end radio source morphology classifier that is capable of locating and associating discrete and extended components of radio sources in a fast (< 200 milliseconds per image) and accurate (>= 90 %) fashion. Future work will improve ClaRAN's relatively lower success rates in dealing with multi-source fields and will enable ClaRAN to identify sources on much larger fields without loss in classification accuracy.