University of California, Santa Cruz

About: University of California, Santa Cruz is a education organization based out in Santa Cruz, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 15541 authors who have published 44120 publications receiving 2759983 citations. The organization is also known as: UCSC & UC, Santa Cruz.

Quantifying the heart of darkness with GHALO – a multibillion particle simulation of a galactic halo

Abstract: We perform a series of simulations of a Galactic mass dark matter halo at different resolutions: our largest uses over 3 billion particles and has a mass resolution of 1000 M⊙. We quantify the structural properties of the inner dark matter distribution and study how they depend on numerical resolution. We can measure the density profile to a distance of 120 pc (0.05 per cent of Rvir), where the logarithmic slope is −0.8 and −1.4 at (0.5 per cent of Rvir). We propose a new two-parameter fitting function that has a linearly varying logarithmic density gradient over the resolved radii which fits the GHALO and VL2 density profiles extremely well. Convergence in the halo shape is achieved at roughly three times the convergence radius for the density profile at which point the halo becomes more spherical due to numerical resolution. The six-dimensional phase-space profile is dominated by the presence of the substructures and does not follow a power law, except in the central few kpc which is devoid of substructure even at this resolution. The quantity, ρ/σ3, which is often used as a proxy for the six-dimensional phase-space density should be used with caution.

Efficiently combining positions and normals for precise 3D geometry

Abstract: Range scanning, manual 3D editing, and other modeling approaches can provide information about the geometry of surfaces in the form of either 3D positions (e.g., triangle meshes or range images) or orientations (normal maps or bump maps). We present an algorithm that combines these two kinds of estimates to produce a new surface that approximates both. Our formulation is linear, allowing it to operate efficiently on complex meshes commonly used in graphics. It also treats high-and low-frequency components separately, allowing it to optimally combine outputs from data sources such as stereo triangulation and photometric stereo, which have different error-vs.-frequency characteristics. We demonstrate the ability of our technique to both recover high-frequency details and avoid low-frequency bias, producing surfaces that are more widely applicable than position or orientation data alone.

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

Abstract: This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.