HIRAX:A Probe of Dark Energy and Radio Transients

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) is a new 400-800MHz radio interferometer under development for deployment in South Africa. HIRAX will comprise 1024 six meter parabolic dishes on a compact grid and will map most of the southern sky over the course of four years. HIRAX has two primary science goals: to constrain Dark Energy and measure structure at high redshift, and to study radio transients and pulsars. HIRAX will observe unresolved sources of neutral hydrogen via their redshifted 21-cm emission line (`hydrogen intensity mapping'). The resulting maps of large-scale structure at redshifts 0.8-2.5 will be used to measure Baryon Acoustic Oscillations (BAO). HIRAX will improve upon current BAO measurements from galaxy surveys by observing a larger cosmological volume (larger in both survey area and redshift range) and by measuring BAO at higher redshift when the expansion of the universe transitioned to Dark Energy domination. HIRAX will complement CHIME, a hydrogen intensity mapping experiment in the Northern Hemisphere, by completing the sky coverage in the same redshift range. HIRAX's location in the Southern Hemisphere also allows a variety of cross-correlation measurements with large-scale structure surveys at many wavelengths. Daily maps of a few thousand square degrees of the Southern Hemisphere, encompassing much of the Milky Way galaxy, will also open new opportunities for discovering and monitoring radio transients. The HIRAX correlator will have the ability to rapidly and eXperimentciently detect transient events. This new data will shed light on the poorly understood nature of fast radio bursts (FRBs), enable pulsar monitoring to enhance long-wavelength gravitational wave searches, and provide a rich data set for new radio transient phenomena searches. This paper discusses the HIRAX instrument, science goals, and current status.Comment: 11 pages, 5 figure

Graphical modeling of binary data using the LASSO: a simulation study

Background: Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Buhlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models. Methods: We analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index. We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer. Results: Bootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms. Conclusions: Bootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes

Farmers’ perceptions of climate change : identifying types

Ambitious targets to reduce greenhouse gas (GHG) emissions from agriculture have been set by both national governments and their respective livestock sectors. We hypothesize that farmer self-identity influences their assessment of climate change and their willingness to im- plement measures which address the issue. Perceptions of climate change were determined from 286 beef/sheep farmers and evaluated using principal component analysis (PCA). The analysis elicits two components which evaluate identity (productivism and environmental responsibility), and two components which evaluate behavioral capacity to adopt mitigation and adaptation measures (awareness and risk perception). Subsequent Cluster Analyses reveal four farmer types based on the PCA scores. ‘The Productivist’ and ‘The Countryside Steward’ portray low levels of awareness of climate change, but differ in their motivation to adopt pro-environmental behavior. Conversely, both ‘The Environmentalist’ and ‘The Dejected’ score higher in their awareness of the issue. In addition, ‘The Dejected’ holds a high sense of perceived risk; however, their awareness is not conflated with an explicit understanding of agricultural GHG sources. With the exception of ‘The Environmentalist’, there is an evident disconnect between perceptions of agricultural emission sources and their contribution towards GHG emissions amongst all types. If such linkages are not con- ceptualized, it is unlikely that behavioral capacities will be realized. Effective communication channels which encour- age action should target farmers based on the groupings depicted. Therefore, understanding farmer types through the constructs used in this study can facilitate effective and tai- lored policy development and implementation