Constraining H-0 in general dark energy models from Sunyaev-Zeldovich/X-ray technique and complementary probes

In accelerating dark energy models, the estimates of the Hubble constant, Ho, from Sunyaev-Zerdovich effect (SZE) and X-ray surface brightness of galaxy clusters may depend on the matter content (Omega(M)), the curvature (Omega(K)) and the equation of state parameter GO. In this article, by using a sample of 25 angular diameter distances of galaxy clusters described by the elliptical beta model obtained through the SZE/X-ray technique, we constrain Ho in the framework of a general ACDM model (arbitrary curvature) and a flat XCDM model with a constant equation of state parameter omega = p(x)/rho(x). In order to avoid the use of priors in the cosmological parameters, we apply a joint analysis involving the baryon acoustic oscillations (BA()) and the (MB Shift Parameter signature. By taking into account the statistical and systematic errors of the SZE/X-ray technique we obtain for nonflat ACDM model H-0 = 74(-4.0)(+5.0) km s(-1) Mpc(-1) (1 sigma) whereas for a fiat universe with constant equation of state parameter we find H-0 = 72(-4.0)(+5.5) km s(-1) Mpc(-1)(1 sigma). By assuming that galaxy clusters are described by a spherical beta model these results change to H-0 = 6(-7.0)(+8.0) and H-0 = 59(-6.0)(+9.0) km s(-1) Mpc(-1)(1 sigma), respectively. The results from elliptical description are in good agreement with independent studies from the Hubble Space Telescope key project and recent estimates based on the Wilkinson Microwave Anisotropy Probe, thereby suggesting that the combination of these three independent phenomena provides an interesting method to constrain the Bubble constant. As an extra bonus, the adoption of the elliptical description is revealed to be a quite realistic assumption. Finally, by comparing these results with a recent determination for a, flat ACDM model using only the SZE/X-ray technique and BAO, we see that the geometry has a very weak influence on H-0 estimates for this combination of data