Direct detection of WIMPs : Implications of a self-consistent truncated isothermal model of the Milky Way's dark matter halo (1006.5588v2)

Abstract: Direct detection of Weakly Interacting Massive Particle (WIMP) candidates of Dark Matter (DM) is studied within the context of a self-consistent truncated isothermal model of the finite-size dark halo of the Galaxy based on the "King model" of the phase space distribution function of collisionless DM particles. Our halo model takes into account the modifications of the phase-space structure of the halo due to the gravitational influence of the observed visible matter in a self-consistent manner. The parameters of the halo model are determined by a fit to a recently determined circular rotation curve of the Galaxy that extends up to $\sim$ 60 kpc. Unlike in the Standard Halo Model (SHM) customarily used in the analysis of the results of WIMP direct detection experiments, the velocity distribution of the WIMPs in our model is non-Maxwellian with a cut-off at a maximum velocity that is self-consistently determined by the model itself. For our halo model that provides the best fit to the rotation curve data, the 90% C.L. upper limit on the WIMP-nucleon spin-independent cross section from the recent results of the CDMS-II experiment, for example, is $\sim 5.3\times10^{-8}\pb$ at a WIMP mass of $\sim$ 71 GeV. We also find, using the original 2-bin annual modulation amplitude data of the DAMA experiment, that there exists a range of small WIMP masses, typically $\sim$ 2 -- 16 GeV, within which DAMA collaboration's claimed annual modulation signal purportedly due to WIMPs is compatible with the null results of other experiments. These results strengthen the possibility of low-mass ($\lsim 10\gev$) WIMPs as a candidate for dark matter as indicated by several earlier studies performed within the context of the SHM. A more rigorous analysis using DAMA bins over smaller intervals should be able to better constrain the "DAMA regions" in the WIMP parameter space within the context of our model.