Hubble Model Research Articles

Addressing redshift controversies through the doppler analog of spectral redshifts caused by light deceleration in dynamic media

This paper discusses some implications of attributing spectral redshifts of astronomical objects to the dynamics of matter in the path of light propagation. Unlike Hubble’s law where the relationship between observed redshifts and distance is a result of observations, the redshift caused by the effect of light speed deceleration with increasing density of matter is naturally proportional to distance. The deviation from linear dependence appreciable for high redshift objects has been characterized using supernovae redshift data for independent distance measurements. The model results in an order of magnitude smaller discrepancy for the distances between quasars and their host galaxies compared to data provided by the Hubble model. The light deceleration model could also contribute in the understanding of dark matter, dark energy, and other attributes of the evolution of the universe.

CCD star images: On the determination of Moffat’s PSF shape parameters

Among the variety of empirical models of optical Point Spread Function used in the astronomical environment, only the Moffat’s (1969) one is able to describe by means of two parameters (in the circular case) both the inner and the outer star image regions. In view of this very important feature, the problem of the simultaneous estimates of Moffat’s PSF shape parameters, off-centring, and the background level in CCD star images has been investigated. The problem does not seem to be rigorously resolvable, but an approximate way to calculate all the parameters except off-centring is shown. It must be stressed that, the Moffat’s PSF model being a softened power law belonging to the family of modified King and Hubble models, the present discussion can be of aid in many other research fields. Also, the integral equation enabling us to convolve a spherical source with Moffat’s PSF is given and applied for comparison to Multi-Gaussian convolution.