Efficient Neutrino Oscillation Parameter Inference with Gaussian Process

Many experiments have been set-up to measure the parameters governing the neutrino oscillation probabilities accurately, with implications for the fundamental structure of the universe. Very often, this involves inferences from tiny samples of data which have complicated dependencies on multiple oscillation parameters simultaneously. This is typically carried out using the unified approach of Feldman and Cousins which is very computationally expensive, on the order of tens of millions of CPU hours. In this work, we propose an iterative method using Gaussian Process to efficiently find a confidence contour for the oscillation parameters and show that it produces the same results at a fraction of the computation cost.

Nano-Precision Polishing of Oxygen-Free Copper Using MCF (Magnetic Compound Fluid) Slurry

Oxygen-free copper (OFC) is a popular material used for molds/dies in injection molding of plastic lens because of its high ductility, strong impact strength and good thermal conductivity. nanoprecision polishing is essential as the final process in its fabrication. For this purpose, a novel polishing method using magnetic compound fluid (MCF) slurry was proposed. In this article, the construction of an experimental rig to realize the proposed method was described at first. Then the effects of process parameters including MCF slurry composition, workpiece oscillation parameter f/Ap-p and clearance Δ between workpiece and MCF carrier on work-surface roughness and material removal were experimentally investigated. As a result, nanoprecision surface finish of OFC was successfully attained by polishing with MCF slurry and the optimum process parameters (f/Ap-p=30 Hz/4mm, Δ=0.6 mm with an MCF slurry (45wt.% of CIP, 12 wt.% of Al2O3 grain, 3 wt.% of α-cellulose, 40 wt.% of MF) for obtaining the smoothest work-surface were determined.