Open AccessThe H i Parkes All Sky Survey: southern observations, calibration and robust imaging
Author(s) -
Barnes D. G.,
StaveleySmith L.,
De Blok W. J. G.,
Oosterloo T.,
Stewart I. M.,
Wright A. E.,
Banks G. D.,
Bhathal R.,
Boyce P. J.,
Calabretta M. R.,
Disney M. J.,
Drinkwater M. J.,
Ekers R. D.,
Freeman K. C.,
Gibson B. K.,
Green A. J.,
Haynes R. F.,
Te Lintel Hekkert P.,
Henning P. A.,
Jerjen H.,
Juraszek S.,
Kesteven M. J.,
Kilborn V. A.,
Knezek P. M.,
Koribalski B.,
KraanKorteweg R. C.,
Malin D. F.,
Marquarding M.,
Minchin R. F.,
Mould J. R.,
Price R. M.,
Putman M. E.,
Ryder S. D.,
Sadler E. M.,
Schröder A.,
Stootman F.,
Webster R. L.,
Wilson W. E.,
Ye T.
Publication year - 2001
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1046/j.1365-8711.2001.04102.x
Subject(s) - sky , declination , telescope , physics , estimator , calibration , noise (video) , remote sensing , astronomy , root mean square , geography , computer science , statistics , artificial intelligence , image (mathematics) , mathematics , quantum mechanics
The acquisition of H i Parkes All Sky Survey (HIPASS) southern sky data commenced at the Australia Telescope National Facility's Parkes 64‐m telescope in 1997 February, and was completed in 2000 March. HIPASS is the deepest H i survey yet of the sky south of declination +2°, and is sensitive to emission out to 170 h 75 −1 Mpc. The characteristic root mean square noise in the survey images is 13.3 mJy. This paper describes the survey observations, which comprise 23 020 eight‐degree scans of 9‐min duration, and details the techniques used to calibrate and image the data. The processing algorithms are successfully designed to be statistically robust to the presence of interference signals, and are particular to imaging point (or nearly point) sources. Specifically, a major improvement in image quality is obtained by designing a median‐gridding algorithm which uses the median estimator in place of the mean estimator.