Astronomy and Astrophysics – Astronomy
Scientific paper
Dec 2003
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003aas...20313802k&link_type=abstract
American Astronomical Society Meeting 203, #138.02; Bulletin of the American Astronomical Society, Vol. 36, p.585
Astronomy and Astrophysics
Astronomy
Scientific paper
Using data from the Southern Galactic Plane Survey(SGPS) we present a routine to search for cold HI clouds in the Galaxy, based on their HI self-absorption(HISA) signature. The data was obtained using the Australia Telescope Compact Array and the Parkes Radio Telescope. The SGPS, because of its good angular and velocity resolution, is excellent for searching for HISA clouds. We have already analyzed a few of the more prominent HISA features, finding spin temperatures, Ts ˜ 20K, column densities, NHI ˜ 2 x 1020}cm{-2, and optical depths of ˜ 1. The next step is to search the entire SGPS. A search by eye is possible, but is biased towards the most pronounced features. To better understand the role HISA plays in the ISM, an automated search technique is required. Our routine takes the first and second derivatives of the HI emission brightness temperature with respect to velocity. Due to the sharp drop in the emission profile through a HISA cloud, the derivative profiles show characteristic positive and/or negative peaks. These peaks represent a population of clouds separate from random HI emission fluctuations. By setting thresholds on the derivative maps and defining HISA only if it passes both derivative tests, we can build an unbiased catalog of HISA candidates in the Galaxy. The number distribution can be used to put constraints on the parameters used to find the spin temperature and optical depth, allowing us to more accurately determine the temperature, column density, and optical depth distribution of HISA clouds. We also compare HISA with 12CO emission. In the Inner Galaxy from l=313 deg to l=338 deg we find 30-50% of HISA is associated with 12CO at a brightness temperature of at least 1K. This work was supported by NSF grant AST 97-32695 to the University of Minnesota.
Dickey J. D.
Gaensler Bryan M.
Green Andrew J.
Kavars Dain William
McClure-Griffiths Naomi M.
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