Constraining cosmology and ionization history with combined 21 cm power spectrum and global signal measurements

Improvements in current instruments and the advent of next-generation instruments will soon push observational 21 cm cosmology into a new era, with high significance measurements of both the power spectrum and the mean ("global") signal of the 21 cm brightness temperature. In this paper we use the recently commenced Hydrogen Epoch of Reionization Array as a worked example to provide forecasts on astrophysical and cosmological parameter constraints. In doing so we improve upon previous forecasts in a number of ways. First, we provide updated forecasts using the latest best-fit cosmological parameters from the Planck satellite, exploring the impact of different Planck datasets on 21 cm experiments. We also show that despite the exquisite constraints that other probes have placed on cosmological parameters, the remaining uncertainties are still large enough to have a non-negligible impact on upcoming 21 cm data analyses. While this complicates high-precision constraints on reionization models, it provides an avenue for 21 cm reionization measurements to constrain cosmology. We additionally forecast HERA's ability to measure the ionization history using a combination of power spectrum measurements and semi-analytic simulations. Finally, we consider ways in which 21 cm global signal and power spectrum measurements can be combined, and propose a method by which power spectrum results can be used to train a compact parameterization of the global signal. This parameterization reduces the number of parameters needed to describe the global signal, increasing the likelihood of a high significance measurement.Comment: 16 pages, 8 figures. Revised to match accepted MNRAS version: expanded discussion of covariances between astrophysics and cosmology in Section 2.2, including two new figures; short discussion relating to KL modes added to Section 4; final results unchange

Winter Safflower Biodiesel: A Green Biofuel for the Southern High Plains

Combustion of fossil fuels has added tremendous quantities of carbon dioxide to the atmosphere, and the increase will continue over the coming decades considering the increasing global population and standards of living. Biofuel cropping systems are believed to realize GHG emission reductions and the local environmental and societal benefits. However, they must be derived from feedstocks produced with much lower life-cycle GHG emissions than traditional fossil fuels and with little or no competition with food production. Winter safflower is considered a potential feedstock for biodiesel production that can be grown on the Texas High Plains. It requires fewer inputs in terms of irrigation and fertilizer, and could be grown on semi-arid or abandoned land. The purpose of this study is to assess and compare the life-cycle energy and greenhouse gas (GHG) emission impacts associated with winter safflower seed-derived biodiesel, and determine the suitability of safflower biodiesel as an energy crop on the Texas High Plains. In addition, this study identifies the parameters that have the greatest impact on GHG emissions and the likelihood that winter safflower would be adopted by farmers on the High Plains. Finally, in order to analyze farmers’ planting decisions corresponding to different carbon policies, a production function of safflower and GHG emissions are developed, as well as a related profit function to evaluate possible incentives to change behaviors.Winter Safflower, Life-cycle Greenhouse Gas Emission, Biofuel, Suitability, Agricultural and Food Policy, Crop Production/Industries, Environmental Economics and Policy, Production Economics,

Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs

Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions; they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the "Randomized Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, "semi-unstructured" means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation 201

Asian and American graphic design: A Theoretical and cultural comparison

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