Context. Source extraction for large-scale HI surveys currently involves
large amounts of manual labor. For data volumes expected from future HI 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 HI 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 HI 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 HI 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.Comment: 13 Pages, 13 Figures, 1 Table, accepted for publication in A&
