M. Boyce, A. Hopkins, S. Riggi, L. Rudnick, M. Ramsay, C. Hale, J. Marvil, M. Whiting, P. Venkataraman, C. O’Dea, S. Baum, Y. Gordon, A. Vantyghem, M. Dionyssiou, H. Andernach, J. Collier, J. English, B. Koribalski, D. Leahy, M. Michałowski, S. Safi-Harb, M. Vaccari, Elaine L. Alexander, M. Cowley, A. Kapinska, A. Robotham, H. Tang
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Hydra, which currently includes the SFs Aegean, Caesar, ProFound, PyBDSF, and Selavy, provides for the addition of new SFs through containerisation and configuration files. The SF input RMS noise and island parameters are optimised to a 90% ‘percentage real detections’ threshold (calculated from the difference between detections in the real and inverted images), to enable comparison between SFs. Hydra provides completeness and reliability diagnostics through observed-deep ( \n$\\mathcal{D}$\n ) and generated-shallow ( \n$\\mathcal{S}$\n ) images, as well as other statistics. In addition, it has a visual inspection tool for comparing residual images through various selection filters, such as S/N bins in completeness or reliability. The tool allows the user to easily compare and evaluate different SFs in order to choose their desired SF, or a combination thereof. This paper is part one of a two part series. 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Hydra I: An extensible multi-source-finder comparison and cataloguing tool
Abstract The latest generation of radio surveys are now producing sky survey images containing many millions of radio sources. In this context it is highly desirable to understand the performance of radio image source finder (SF) software and to identify an approach that optimises source detection capabilities. We have created Hydra to be an extensible multi-SF and cataloguing tool that can be used to compare and evaluate different SFs. Hydra, which currently includes the SFs Aegean, Caesar, ProFound, PyBDSF, and Selavy, provides for the addition of new SFs through containerisation and configuration files. The SF input RMS noise and island parameters are optimised to a 90% ‘percentage real detections’ threshold (calculated from the difference between detections in the real and inverted images), to enable comparison between SFs. Hydra provides completeness and reliability diagnostics through observed-deep (
$\mathcal{D}$
) and generated-shallow (
$\mathcal{S}$
) images, as well as other statistics. In addition, it has a visual inspection tool for comparing residual images through various selection filters, such as S/N bins in completeness or reliability. The tool allows the user to easily compare and evaluate different SFs in order to choose their desired SF, or a combination thereof. This paper is part one of a two part series. In this paper we introduce the Hydra software suite and validate its
$\mathcal{D/S}$
metrics using simulated data. The companion paper demonstrates the utility of Hydra by comparing the performance of SFs using both simulated and real images.
期刊介绍:
ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.