Cosmology on a Mesh

An adaptive multi grid approach to simulating the formation of structure from collisionless dark matter is described. MLAPM (Multi-Level Adaptive Particle Mesh) is one of the most efficient serial codes available on the cosmological 'market' today. As part of Swinburne University's role in the development of the Square Kilometer Array, we are implementing hydrodynamics, feedback, and radiative transfer within the MLAPM adaptive mesh, in order to simulate baryonic processes relevant to the interstellar and intergalactic media at high redshift. We will outline our progress to date in applying the existing MLAPM to a study of the decay of satellite galaxies within massive host potentials.Comment: 3 pages, 2 figures, to appear in the proceedings of "The IGM/Galaxy Connection - The Distribution of Baryons at z=0", ed. M. Putman & J. Rosenber

Half-life of the electron-capture decay of 97Ru: Precision measurement shows no temperature dependence

We have measured the half-life of the electron-capture (ec) decay of 97Ru in a metallic environment, both at low temperature (19K), and also at room temperature. We find the half-lives at both temperatures to be the same within 0.1%. This demonstrates that a recent claim that the ec decay half-life for 7Be changes by $0.9% +/- 0.2% under similar circumstances certainly cannot be generalized to other ec decays. Our results for the half-life of 97Ru, 2.8370(14)d at room temperature and 2.8382(14)d at 19K, are consistent with, but much more precise than, previous room-temperature measurements. In addition, we have also measured the half-lives of the beta-emitters 103Ru and 105Rh at both temperatures, and found them also to be unchanged.Comment: 6 pages, 6 figure

Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing

There is an urgent societal need to assess whether autonomous vehicles (AVs) are safe enough. From published quantitative safety and reliability assessments of AVs, we know that, given the goal of predicting very low rates of accidents, road testing alone requires infeasible numbers of miles to be driven. However, previous analyses do not consider any knowledge prior to road testing – knowledge which could bring substantial advantages if the AV design allows strong expectations of safety before road testing. We present the advantages of a new variant of Conservative Bayesian Inference (CBI), which uses prior knowledge while avoiding optimistic biases. We then study the trend of disengagements (take-overs by human drivers) by applying Software Reliability Growth Models (SRGMs) to data from Waymo’s public road testing over 51 months, in view of the practice of software updates during this testing. Our approach is to not trust any specific SRGM, but to assess forecast accuracy and then improve forecasts. We show that, coupled with accuracy assessment and recalibration techniques, SRGMs could be a valuable test planning aid