The Lofar Central Processing Facility Architecture

Reconfiguration is a key feature characteristic of the LOFAR telescope. Software platforms are utilised to program out the required data transformations in the generation of scientific end-products. Reconfigurable resources nowadays often replace the hard-wired processing systems from the past. This paper describes how this paradigm is implemented in a purely general-purpose telescope back-end. Experiences from high performance computing, stream processing and software engineering have been combined, leading to a state-of-the-art processing platform. The processing platform offers a total processing power of 35 TFlops, which is used to process a sustained input data- stream of 320 Gbps. The architecture of this platform is optimised for streaming data processing and offers appropriate processing resources for each step in the data processing chains. Typical data processing chains include Fourier transformations and correlation tasks along with controlling tasks such as fringe rotation correction. These tasks are defined in a high level programming language and mapped onto the available resources at run time. A scheduling system is used to control a collection of concurrently executing observations, providing each associated application with the appropriate resources to meet its timing constraint and give the integrated system the correct on-line and off-line look and feel.     

High-eccentricity planets from the Anglo-Australian Planet Search

We report Doppler measurements of the stars HD 187085 and HD 20782 which indicate two high eccentricity low-mass companions to the stars. We find HD 187085 has a Jupiter-mass companion with a ∼1000-d orbit. Our formal 'best-fitting' solution suggests an eccentricity of 0.47, however, it does not sample the periastron passage of the companion and we find that orbital solutions with eccentricities between 0.1 and 0.8 give only slightly poorer fits (based on rms and  χ²_ν  ) and are thus plausible. Observations made during periastron passage in 2007 June should allow for the reliable determination of the orbital eccentricity for the companion to HD 187085. Our data set for HD 20782 does sample periastron and so the orbit for its companion can be more reliably determined. We find the companion to HD 20782 has   M sin   i = 1.77 ± 0.22  M _Jup  , an orbital period of 595.86 ± 0.03 d and an orbit with an eccentricity of 0.92 ± 0.03. The detection of such high-eccentricity (and relatively low-velocity amplitude) exoplanets appears to be facilitated by the long-term precision of the Anglo-Australian Planet Search. Looking at exoplanet detections as a whole, we find that those with higher eccentricity seem to have relatively higher velocity amplitudes indicating higher mass planets and/or an observational bias against the detection of high-eccentricity systems.     

Satellite selection with an end-to-end deep learning network

Benefiting from multi-constellation Global Navigation Satellite Systems (GNSS), more and more visible satellites can be used to improve user positioning performance. However, due to limited tracking receiver channels and power consumption, and other issues, it may be not possible, or desirable, to use all satellites in view for positioning. The optimal subset is generally selected from all possible satellite combinations to minimize either Geometric Dilution of Precision (GDOP) or weighted GDOP (WGDOP). However, this brute force approach is difficult to implement in real-time applications due to the time- and power-consuming calculation of the DOP values. As an alternative to a brute force satellite selection procedure, the authors propose an end-to-end deep learning network for satellite selection based on the PointNet and VoxelNet networks. The satellite selection is converted to a satellite segmentation problem, with specified input channel for each satellite and two class labels, one for selected satellites and the other for those not selected. The aim of the satellite segmentation is that a fixed number of satellites with the minimum GDOP/WGDOP value can be segmented from any feeding order of input satellites. To validate the proposed satellite segmentation network, training and test data from 220 IGS stations tracking GPS and GLONASS satellites were used. The segmentation performance using different architectures and representations of input channels, including receiver-to-satellite unit vector and elevation and azimuth, were compared. It was found that the input channel with elevation and azimuth can achieve better performance than using the receiver-to-satellite unit vector, and an architecture with stacked feature encoding (FE) layers has better satellite segmentation performance than one without stacked FE layers. In addition, the models with GDOP and WGDOP criteria for selecting 9 and 12 satellites were trained. It was demonstrated that the satellite segmentation network was about 90 times faster than using the brute force approach. Furthermore, all the trained models can effectively select the satellites making the most contribution to the desired GDOP/WGDOP value. Approximately 99% of the tests had GDOP and WGDOP value differences smaller than 0.03 and 0.2, respectively, between the predicted subset and the optimal subset.     

Petrogenesis of peralkaline granite dykes of the Straumsvola complex,western Dronning Maud Land,Antarctica

Peralkaline syenite and granite dykes cut the Straumsvola nepheline syenite pluton in Western Dronning Maud Land, Antarctica. The average peralkalinity index (PI?=?molecular Al/[Na?+?K]) of the dykes is 1.20 (n?=?29) and manifests itself in the presence of the Zr silicates eudialyte, dalyite and vlasovite, and the Na–Ti silicate, narsarsukite. The dykes appear to have intruded during slow cooling of the nepheline syenite pluton, and the petrogenetic relationship of the dykes and the pluton cannot be related to closed-system processes at low pressure, given the thermal divide that exists between silica-undersaturated and oversaturated magmas. Major and trace element variations in the dykes are consistent with a combination of fractional crystallization of parental peralkaline magma of quartz trachyte composition, and internal mineral segregation prior to final solidification. The distribution of accessory minerals is consistent with late-stage crystallization of isolated melt pockets. The dykes give an Rb–Sr isochron age of 171?±?4.4 Ma, with variable initial ⁸⁷Sr/⁸⁶Sr ratio (0.7075?±?0.0032), and have an average ε _Nd of ? 12.0. Quartz phenocrysts have δ¹⁸O values of 8.4–9.2‰, which are generally in O-isotope equilibrium with bulk rock. Differences in the δ¹⁸O values of quartz and aegirine (average Δ_{quartz?aegirine} = 3.5‰) suggest aegirine formation temperatures around 500 °C, lower than expected for a felsic magma, but consistent with poikilitic aegirine that indicates subsolidus growth. The negative ε _Nd (< ? 10) and magma δ¹⁸O values averaging 8.6‰ (assuming Δ_quartz?magma = 0.6‰) are inconsistent with a magma produced by closed-system fractional crystallization of a mantle-derived magma. By contrast, the nepheline syenite magma had mantle-like δ¹⁸O values and much less negative ε _Nd (average ??3.1, n?=?3). The country rock has similar δ¹⁸O values to the granite dykes (average 8.0‰, n?=?108); this means that models for the petrogenesis of the granites by assimilation are unfeasible, unless an unexposed high-δ¹⁸O contaminant is invoked. Instead, it is proposed that the peralkaline syenite and granite dykes formed by partial melting of alkali-metasomatised gneiss that surrounds the nepheline syenite, followed by fractional crystallization.