We present the HIPASS Bright Galaxy Catalog (BGC), which contains the 1000 H i brightest galaxies in the southern sky as obtained from the H i Parkes All-Sky Survey (HIPASS). The selection of the brightest sources is based on their H i peak flux density (S peak k116 mJy) as measured from the spatially integrated HIPASS spectrum. The derived H i masses range from $10 7 to 4 ; 10 10 M . While the BGC (z < 0:03) is complete in S peak , only a subset of $500 sources can be considered complete in integrated H i flux density (F H i k 25 Jy km s À1 ). The HIPASS BGC contains a total of 158 new redshifts. These belong to 91 new sources for which no optical or infrared counterparts have previously been cataloged, an additional 51 galaxies for which no redshifts were previously known, and 16 galaxies for which the cataloged optical velocities disagree. Of the 91 newly cataloged BGC sources, only four are definite H i clouds: while three are likely Magellanic debris with velocities around 400 km s À1 , one is a tidal cloud associated with the NGC 2442 galaxy group. The remaining 87 new BGC sources, the majority of which lie in the zone of avoidance, appear to be galaxies. We identified optical counterparts to all but one of the 30 new galaxies at Galactic latitudes jbj > 10 . Therefore, the BGC yields no evidence for a population of ''free-floating'' intergalactic H i clouds without associated optical counterparts. HIPASS provides a clear view of the local large-scale structure. The dominant features in the sky distribution of the BGC are the Supergalactic Plane and the Local Void. In addition, one can clearly see the Centaurus Wall, which connects via the Hydra and Antlia Clusters to the Puppis Filament. Some previously hardly noticable galaxy groups stand out quite distinctly in the H i sky distribution. Several new structures, including some not behind the Milky Way, are seen for the first time.

We present a new accurate measurement of the H I mass function of galaxies from the HIPASS Bright Galaxy Catalog, a sample of 1000 galaxies with the highest H I peak flux densities in the southern (δ < 0 • ) hemisphere (Koribalski et al. 2003). This sample spans nearly four orders of magnitude in H I mass (from log(M HI ⁄ M ⊙ ) + 2 log h 75 = 6.8 to 10.6) and is the largest sample of H I selected galaxies to date. We develop a bivariate maximum likelihood technique to measure the space density of galaxies, and show that this is a robust method, insensitive to the effects of large scale structure. The resulting H I mass function can be fitted satisfactorily with a Schechter function with faint-end slope α = −1.30. This slope is found to be dependent on morphological type, with later type galaxies giving steeper slopes. We extensively test various effects that potentially bias the determination of the H I mass function, including peculiar motions of galaxies, large scale structure, selection bias, and inclination effects, and quantify these biases. The large sample of galaxies enables an accurate measurement of the cosmological mass density of neutral gas: Ω HI = (3.8 ± 0.6) × 10 −4 h −1 75 . Low surface brightness galaxies contribute only ∼ 15% to this value, consistent with previous findings.

A catalog of Southern anomalous-velocity HI clouds at Decl. < +2 • is presented. This catalog is based on data from the HI Parkes All-Sky Survey (HIPASS) reprocessed with the minmed5 procedure (Putman et al. 2002;Putman 2000), and searched with the -2high-velocity cloud finding algorithm described by de Heij et al. (2001). The improved sensitivity (5σ: ∆T B = 0.04 K), resolution (15. ′ 5), and velocity range (−500 < V LSR < +500 km s −1 ) of the HIPASS data, results in a substantial increase in the number of individual clouds (1956, as well as 41 galaxies) compared to what was known from earlier Southern data. The method of cataloging the anomalous-velocity objects is described, and a catalog of key cloud parameters, including velocity, angular size, peak column density, total flux, position angle, and degree of isolation, is presented. The data are characterized into several classes of anomalous-velocity HI emission. Most high-velocity emission features, HVCs, have a filamentary morphology and are loosely organized into large complexes extending over tens of degrees. In addition, 179 compact and isolated anomalous-velocity objects, CHVCs, are identified based on their size and degree of isolation. 25% of the CHVCs originally classified by Braun & Burton (1999) are reclassified based on the HIPASS data. The properties of all the highvelocity emission features and only the CHVCs are investigated, and distinct similarities and differences are found. Both populations have typical HI masses of ∼ 4.5 D 2 kpc M ⊙ and have similar slopes for their column density and flux distributions. On the other hand, the CHVCs appear to be clustered and the population can be broken up into three spatially distinct groups, while the entire population of clouds is more uniformly distributed with a significant percentage aligned with the the Magellanic Stream. The median velocities are V GSR = −38 km s −1 for the CHVCs and −30 km s −1 for all of the anomalous-velocity clouds. Based on the catalog sizes, high-velocity features cover 19% of the southern sky, and CHVCs cover 1%.

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 h75−1 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.

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