Source finding, parametrization, and classification for the extragalactic Effelsberg-Bonn H i Survey

¹ Argelander-Institut für Astronomie, Universität Bonn, Auf dem Hügel 71, 53121 Bonn, Germany
e-mail: lfloeer@astro.uni-bonn.de
² Max-Planck-Institut fur Radioastronomie, Auf dem Hügel 69, 53121 Bonn, Germany

Received: 17 June 2014
Accepted: 25 July 2014

Abstract

Context. Source extraction for large-scale H i surveys currently involves large amounts of manual labor. For data volumes expected from future H i surveys with upcoming facilities, this approach is not feasible any longer.

Aims. We describe the implementation of a fully automated source finding, parametrization, and classification pipeline for the Effelsberg-Bonn H i Survey (EBHIS). With future radio astronomical facilities in mind, we want to explore the feasibility of a completely automated approach to source extraction for large-scale H i surveys.

Methods. Source finding is implemented using wavelet denoising methods, which previous studies show to be a powerful tool, especially in the presence of data defects. For parametrization, we automate baseline fitting, mask optimization, and other tasks based on well-established algorithms, currently used interactively. For the classification of candidates, we implement an artificial neural network, which is trained on a candidate set comprised of false positives from real data and simulated sources. Using simulated data, we perform a thorough analysis of the algorithms implemented.

Results. We compare the results from our simulations to the parametrization accuracy of the H i Parkes All-Sky Survey (HIPASS) survey. Even though HIPASS is more sensitive than EBHIS in its current state, the parametrization accuracy and classification reliability match or surpass the manual approach used for HIPASS data.