Feature Detection in Radio Astronomy using the Circle Hough Transform

TL;DR

This paper investigates the effectiveness of the Circle Hough Transform for automatically detecting extended, low-surface brightness, and arc-like features in radio astronomical images, addressing the challenge posed by large data volumes from next-generation telescopes.

Contribution

It demonstrates how the Circle Hough Transform responds to various extended astronomical objects and noise, proposing a potential method for automatic detection of diffuse features in radio astronomy.

Findings

Circle Hough Transform effectively detects circular and arc-like structures.

The method distinguishes between noise, point sources, and extended features.

Potential for automating the analysis of large radio astronomy datasets.

Abstract

While automatic detection of point sources in astronomical images has experienced a great degree of success, less effort has been directed towards the detection of extended and low-surface brightness features. At present, existing telescopes still rely on human expertise to reduce the raw data to usable images and then to analyse the images for non-pointlike objects. However, the next generation of radio telescopes will generate unprecedented volumes of data making manual data reduction and object extraction infeasible. Without developing new methods of automatic detection for extended and diffuse objects such as supernova remnants, bent-tailed galaxies, radio relics and halos, a wealth of scientifically important results will not be uncovered. In this paper we explore the response of the Circle Hough Transform to a representative sample of different extended circular or arc-like astronomical objects. We also examine the response of the Circle Hough Transform to input images containing noise alone and inputs including point sources.