Context. Current and future blind surveys for HI generate large catalogs of spectral lines for which automated characterization would be convenient. Aims. A 6-parameter mathematical model for HI galactic spectral lines is described. The aim of the
paper is to show that this model is indeed a useful way to characterize such lines. Methods. The model is fitted to spectral lines extracted for the 34 spiral galaxies of the recent high-definition THINGS survey. Three scenarios with different instrumental characteristics are compared. Quantities obtained from the model fits, most importantly line width and total flux, are compared with analog quantities measured in more standard, non-parametric ways. Results. The model is shown to be a good fit to nearly all the THINGS profiles. When extra noise is added to the test spectra, the fits remain consistent; the model-fitting approach is also shown to return superior estimates of linewidth and flux under such conditions.
The CLEAN algorithm, widely used in radio interferometry for the deconvolution of radio images, performs well only if the raw radio image (dirty image) is, to good approximation, a simple convolution between the instrumental point-spread function (di
rty beam) and the true distribution of emission across the sky. An important case in which this approximation breaks down is during frequency synthesis if the observing bandwidth is wide enough for variations in the spectrum of the sky to become significant. The convolution assumption also breaks down, in any situation but snapshot observations, if sources in the field vary significantly in flux density over the duration of the observation. Such time-variation can even be instrumental in nature, for example due to jitter or rotation of the primary beam pattern on the sky during an observation. An algorithm already exists for dealing with the spectral variation encountered in wide-band frequency synthesis interferometry. This algorithm is an extension of CLEAN in which, at each iteration, a set of N `dirty beams are fitted and subtracted in parallel, instead of just a single dirty beam as in standard CLEAN. In the wide-band algorithm the beams are obtained by expanding a nominal source spectrum in a Taylor series, each term of the series generating one of the beams. In the present paper this algorithm is extended to images which contain sources which vary over both frequency and time. Different expansion schemes (or bases) on the time and frequency axes are compared, and issues such as Gibbs ringing and non-orthogonality are discussed. It is shown that practical considerations make it often desirable to orthogonalize the set of beams before commencing the cleaning. This is easily accomplished via a Gram-Schmidt technique.
