Tables of electrostatic structure of near wakes behind space probes at the mesothermal speeds

Tabular data for electrostatic structure of disturbed region behind moving bodies in collisionless plasma for sphere and long circular cylinde

An automated method for comparing motion artifacts in cine four-dimensional computed tomography images.

The aim of this study is to develop an automated method to objectively compare motion artifacts in two four-dimensional computed tomography (4D CT) image sets, and identify the one that would appear to human observers with fewer or smaller artifacts. Our proposed method is based on the difference of the normalized correlation coefficients between edge slices at couch transitions, which we hypothesize may be a suitable metric to identify motion artifacts. We evaluated our method using ten pairs of 4D CT image sets that showed subtle differences in artifacts between images in a pair, which were identifiable by human observers. One set of 4D CT images was sorted using breathing traces in which our clinically implemented 4D CT sorting software miscalculated the respiratory phase, which expectedly led to artifacts in the images. The other set of images consisted of the same images; however, these were sorted using the same breathing traces but with corrected phases. Next we calculated the normalized correlation coefficients between edge slices at all couch transitions for all respiratory phases in both image sets to evaluate for motion artifacts. For nine image set pairs, our method identified the 4D CT sets sorted using the breathing traces with the corrected respiratory phase to result in images with fewer or smaller artifacts, whereas for one image pair, no difference was noted. Two observers independently assessed the accuracy of our method. Both observers identified 9 image sets that were sorted using the breathing traces with corrected respiratory phase as having fewer or smaller artifacts. In summary, using the 4D CT data of ten pairs of 4D CT image sets, we have demonstrated proof of principle that our method is able to replicate the results of two human observers in identifying the image set with fewer or smaller artifacts

Hierarchical Bayesian CMB Component Separation with the No-U-Turn Sampler

Key to any cosmic microwave background (CMB) analysis is the separation of the CMB from foreground contaminants. In this paper we present a novel implementation of Bayesian CMB component separation. We sample from the full posterior distribution using the No-U-Turn Sampler (NUTS), a gradient based sampling algorithm. Alongside this, we introduce new foreground modelling approaches. We use the mean-shift algorithm to define regions on the sky, clustering according to naively estimated foreground spectral parameters. Over these regions we adopt a complete pooling model, where we assume constant spectral parameters, and a hierarchical model, where we model individual spectral parameters as being drawn from underlying hyper-distributions. We validate the algorithm against simulations of the LiteBIRD and C-BASS experiments, with an input tensor-to-scalar ratio of $r=5\times 10^{-3}$. Considering multipoles $32\leq\ell\leq 121$, we are able to recover estimates for $r$. With LiteBIRD only observations, and using the complete pooling model, we recover $r=(10\pm 0.6)\times 10^{-3}$. For C-BASS and LiteBIRD observations we find $r=(7.0\pm 0.6)\times 10^{-3}$ using the complete pooling model, and $r=(5.0\pm 0.4)\times 10^{-3}$ using the hierarchical model. By adopting the hierarchical model we are able to eliminate biases in our cosmological parameter estimation, and obtain lower uncertainties due to the smaller Galactic emission mask that can be adopted for power spectrum estimation. Measured by the rate of effective sample generation, NUTS offers performance improvements of $\sim10^3$ over using Metropolis-Hastings to fit the complete pooling model. The efficiency of NUTS allows us to fit the more sophisticated hierarchical foreground model, that would likely be intractable with non-gradient based sampling algorithms.Comment: 19 pages, 9 figure

The C-Band All-Sky Survey (C-BASS): Constraining diffuse Galactic radio emission in the North Celestial Pole region

The C-Band All-Sky Survey C-BASS is a high-sensitivity all-sky radio survey at an angular resolution of 45 arcmin and a frequency of 4.7 GHz. We present a total intensity 4.7 GHz map of the North Celestial Pole (NCP) region of sky, above declination +80 deg, which is limited by source confusion at a level of ~0.6 mK rms. We apply the template-fitting (cross-correlation) technique to WMAP and Planck data, using the C-BASS map as the synchrotron template, to investigate the contribution of diffuse foreground emission at frequencies ~20-40 GHz. We quantify the anomalous microwave emission (AME) that is correlated with far-infrared dust emission. The AME amplitude does not change significantly (<10%) when using the higher frequency C-BASS 4.7 GHz template instead of the traditional Haslam 408 MHz map as a tracer of synchrotron radiation. We measure template coefficients of $9.93\pm0.35$ and $9.52\pm0.34$ K per unit $\tau_{353}$ when using the Haslam and C-BASS synchrotron templates, respectively. The AME contributes $55\pm2\,\mu$K rms at 22.8 GHz and accounts for ~60% of the total foreground emission. Our results suggest that a harder (flatter spectrum) component of synchrotron emission is not dominant at frequencies >5 GHz; the best-fitting synchrotron temperature spectral index is $\beta=-2.91\pm0.04$ from 4.7 to 22.8 GHz and $\beta=-2.85\pm0.14$ from 22.8 to 44.1 GHz. Free-free emission is weak, contributing ~$7\,\mu$K rms (~7%) at 22.8 GHz. The best explanation for the AME is still electric dipole emission from small spinning dust grains.Comment: 18 pages, 6 figures, version matches version accepted by MNRA

The role of quantitative cross-case analysis in understanding tropical smallholder farmers’ adaptive capacity to climate shocks

Climate shocks are predicted to increase in magnitude and frequency as the climate changes, notably impacting poor and vulnerable communities across the Tropics. The urgency to better understand and improve communities' resilience is reflected in international agreements such as the Paris Agreement and the multiplication of adaptation research and action programs. In turn, the need for collecting and communicating evidence on the climate resilience of communities has increasingly drawn questions concerning how to assess resilience. While empirical case studies are often used to delve into the context-specific nature of resilience, synthesizing results is essential to produce generalizable findings at the scale at which policies are designed. Yet datasets, methods and modalities that enable cross-case analyses that draw from individual local studies are still rare in climate resilience literature. We use empirical case studies on the impacts of El Niño on smallholder households from five countries to test the application of quantitative data aggregation for policy recommendation. We standardized data into an aggregated dataset to explore how key demographic factors affected the impact of climate shocks, modeled as crop loss. We find that while cross-study results partially align with the findings from the individual projects and with theory, several challenges associated with quantitative aggregation remain when examining complex, contextual and multi-dimensional concepts such as resilience. We conclude that future exercises synthesizing cross-site empirical evidence in climate resilience could accelerate research to policy impact by using mixed methods, focusing on specific landscapes or regional scales, and facilitating research through the use of shared frameworks and learning exercises

C-Band All-Sky Survey (C-BASS): Simulated parametric fitting in single pixels in total intensity and polarization

The cosmic microwave background (CMB) B-mode signal is potentially weaker than the diffuse Galactic foregrounds over most of the sky at any frequency. A common method of separating the CMB from these foregrounds is via pixel-based parametric-model fitting. There are not currently enough all-sky maps to fit anything more than the most simple models of the sky. By simulating the emission in seven representative pixels, we demonstrate that the inclusion of a 5 GHz data point allows for more complex models of low-frequency foregrounds to be fitted than at present. It is shown that the inclusion of the C-BASS data will significantly reduce the uncertainties in a number of key parameters in the modelling of both the galactic foregrounds and the CMB. The extra data allow estimates of the synchrotron spectral index to be constrained much more strongly than is presently possible, with corresponding improvements in the accuracy of the recovery of the CMB amplitude. However, we show that to place good limits on models of the synchrotron spectral curvature will require additional low-frequency data

The C-Band All-Sky Survey (C-BASS): Simulated parametric fitting in single pixels in total intensity and polarization

The cosmic microwave background (CMB) B-mode signal is potentially weaker than the diffuse Galactic foregrounds over most of the sky at any frequency. A common method of separating the CMB from these foregrounds is via pixel-based parametric-model fitting. There are not currently enough all-sky maps to fit anything more than the most simple models of the sky. By simulating the emission in seven representative pixels, we demonstrate that the inclusion of a 5 GHz data point allows for more complex models of low-frequency foregrounds to be fitted than at present. It is shown that the inclusion of the C-BASS data will significantly reduce the uncertainties in a number of key parameters in the modelling of both the galactic foregrounds and the CMB. The extra data allow estimates of the synchrotron spectral index to be constrained much more strongly than is presently possible, with corresponding improvements in the accuracy of the recovery of the CMB amplitude. However, we show that to place good limits on models of the synchrotron spectral curvature will require additional low-frequency data