Mass formulae and strange quark matter

We have derived the popularly used parametrization formulae for quark masses at low densities and modified them at high densities within the mass-density-dependent model. The results are applied to investigate the lowest density for the possible existence of strange quark matter at zero temperature.Comment: 9 pages, LATeX with ELSART style, one table, no figures. Improvement on the derivation of qark mass formula

r-Process Nucleosynthesis in Shocked Surface Layers of O-Ne-Mg Cores

We demonstrate that rapid expansion of the shocked surface layers of an O-Ne-Mg core following its collapse can result in r-process nucleosynthesis. As the supernova shock accelerates through these layers, it makes them expand so rapidly that free nucleons remain in disequilibrium with alpha-particles throughout most of the expansion. This allows heavy r-process isotopes including the actinides to form in spite of the very low initial neutron excess of the matter. We estimate that yields of heavy r-process nuclei from this site may be sufficient to explain the Galactic inventory of these isotopes.Comment: 11 pages, 1 figure, to appear in the Astrophysical Journal Letter

(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior

Advanced diffusion magnetic resonance imaging (dMRI) techniques, like diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly longer. To accelerate DSI and HARDI, recent methods from compressed sensing (CS) exploit a sparse underlying representation of the data in the spatial and angular domains to undersample in the respective k- and q-spaces. State-of-the-art frameworks, however, impose sparsity in the spatial and angular domains separately and involve the sum of the corresponding sparse regularizers. In contrast, we propose a unified (k,q)-CS formulation which imposes sparsity jointly in the spatial-angular domain to further increase sparsity of dMRI signals and reduce the required subsampling rate. To efficiently solve this large-scale global reconstruction problem, we introduce a novel adaptation of the FISTA algorithm that exploits dictionary separability. We show on phantom and real HARDI data that our approach achieves significantly more accurate signal reconstructions than the state of the art while sampling only 2-4% of the (k,q)-space, allowing for the potential of new levels of dMRI acceleration.Comment: To be published in the 2017 Computational Diffusion MRI Workshop of MICCA