Arcfinder: An algorithm for the automatic detection of gravitational arcs

Abstract

We present an efficient algorithm designed for and capable of detecting elongated, thin features such as lines and curves in astronomical images, and its application to the automatic detection of gravitational arcs. The algorithm is sufficiently robust to detect such features even if their surface brightness is near the pixel noise in the image, yet the amount of spurious detections is low. The algorithm subdivides the image into a grid of overlapping cells which are iteratively shifted towards a local centre of brightness in their immediate neighbourhood. It then computes the ellipticity for each cell, and combines cells with correlated ellipticities into objects. These are combined to graphs in a next step, which are then further processed to determine properties of the detected objects. We demonstrate the operation and the efficiency of the algorithm applying it to HST images of galaxy clusters known to contain gravitational arcs. The algorithm completes the analysis of an image with 3000x3000 pixels in about 4 seconds on an ordinary desktop PC. We discuss further applications, the method's remaining problems and possible approaches to their solution.

12 pages, 12 figures

References in corpus (2)

• The Lensed Arc Production Efficiency of Galaxy Clusters: A Comparison of Matched Observed and Simulated Samples
• Automatic detection of arcs and arclets formed by gravitational lensing

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