Distributed and parallel sparse convex optimization for radio interferometry with PURIFY

Next generation radio interferometric telescopes are entering an era of big data with extremely large data sets. While these telescopes can observe the sky in higher sensitivity and resolution than before, computational challenges in image reconstruction need to be overcome to realize the potential of forthcoming telescopes. New methods in sparse image reconstruction and convex optimization techniques (cf. compressive sensing) have shown to produce higher fidelity reconstructions of simulations and real observations than traditional methods. This article presents distributed and parallel algorithms and implementations to perform sparse image reconstruction, with significant practical considerations that are important for implementing these algorithms for Big Data. We benchmark the algorithms presented, showing that they are considerably faster than their serial equivalents. We then pre-sample gridding kernels to scale the distributed algorithms to larger data sizes, showing application times for 1 Gb to 2.4 Tb data sets over 25 to 100 nodes for up to 50 billion visibilities, and find that the run-times for the distributed algorithms range from 100 milliseconds to 3 minutes per iteration. This work presents an important step in working towards computationally scalable and efficient algorithms and implementations that are needed to image observations of both extended and compact sources from next generation radio interferometers such as the SKA. The algorithms are implemented in the latest versions of the SOPT (https://github.com/astro-informatics/sopt) and PURIFY (https://github.com/astro-informatics/purify) software packages {(Versions 3.1.0)}, which have been released alongside of this article.Comment: 25 pages, 5 figure

muSR and Magnetometry Study of the Type-I Superconductor BeAu

We present muon spin rotation and relaxation (muSR) measurements as well as demagnetising field corrected magnetisation measurements on polycrystalline samples of the noncentrosymmetric superconductor BeAu. From muSR measurements in a transverse field, we determine that BeAu is a type-I superconductor with Hc = 256 Oe, amending the previous understanding of the compound as a type-II superconductor. To account for demagnetising effects in magnetisation measurements, we produce an ellipsoidal sample, for which a demagnetisation factor can be calculated. After correcting for demagnetising effects, our magnetisation results are in agreement with our muSR measurements. Using both types of measurements we construct a phase diagram from T = 30 mK to Tc = 3.25 K. We then study the effect of hydrostatic pressure and find that 450 MPa decreases Tc by 34 mK, comparable to the change seen in type-I elemental superconductors Sn, In and Ta, suggesting BeAu is far from a quantum critical point accessible by the application of pressure.Comment: 10 pages, 8 figure

Wild state secrets: ultra-sensitive measurement of micro-movement can reveal internal processes in animals

Assessment of animal internal "state" - which includes hormonal, disease, nutritional, and emotional states - is normally considered the province of laboratory work, since its determination in animals in the wild is considered more difficult. However, we show that accelerometers attached externally to animals as diverse as elephants, cockroaches, and humans display consistent signal differences in micro-movement that are indicative of internal state. Originally used to elucidate the behavior of wild animals, accelerometers also have great potential for highlighting animal actions, which are considered as responses stemming from the interplay between internal state and external environment. Advances in accelerometry may help wildlife managers understand how internal state is linked to behavior and movement, and thus clarify issues ranging from how animals cope with the presence of newly constructed roads to how diseased animals might change movement patterns and therefore modulate disease spread