The effect of timing noise on targeted and narrow-band coherent searches for continuous gravitational waves from pulsars

Most searches for continuous gravitational-waves from pulsars use Taylor expansions in the phase to model the spin-down of neutron stars. Studies of pulsars demonstrate that their electromagnetic (EM) emissions suffer from \emph{timing noise}, small deviations in the phase from Taylor expansion models. How the mechanism producing EM emission is related to any continuous gravitational-wave (CW) emission is unknown; if they either interact or are locked in phase then the CW will also experience timing noise. Any disparity between the signal and the search template used in matched filtering methods will result in a loss of signal-to-noise ratio (SNR), referred to as `mismatch'. In this work we assume the CW suffers a similar level of timing noise to its EM counterpart. We inject and recover fake CW signals, which include timing noise generated from observational data on the Crab pulsar. Measuring the mismatch over durations of order $\sim 10$ months, the effect is for the most part found to be small. This suggests recent so-called `narrow-band' searches which placed upper limits on the signals from the Crab and Vela pulsars will not be significantly affected. At a fixed observation time, we find the mismatch depends upon the observation epoch. Considering the averaged mismatch as a function of observation time, we find that it increases as a power law with time, and so may become relevant in long baseline searches.Comment: 9 pages, 5 figure

Comparing models of the periodic variations in spin-down and beam-width for PSR B1828-11

We build a framework using tools from Bayesian data analysis to evaluate models explaining the periodic variations in spin-down and beam-width of PSR B1828-11. The available data consists of the time averaged spin-down rate, which displays a distinctive double-peaked modulation, and measurements of the beam-width. Two concepts exist in the literature that are capable of explaining these variations; we formulate predictive models from these and quantitatively compare them. The first concept is phenomenological and stipulates that the magnetosphere undergoes periodic switching between two meta-stable states as first suggested by Lyne et al. The second concept, precession, was first considered as a candidate for the modulation of B1828-11 by Stairs et al.. We quantitatively compare models built from these concepts using a Bayesian odds-ratio. Because the phenomenological switching model itself was informed by this data in the first place, it is difficult to specify appropriate parameter-space priors that can be trusted for an unbiased model comparison. Therefore we first perform a parameter estimation using the spin-down data, and then use the resulting posterior distributions as priors for model comparison on the beam-width data. We find that a precession model with a simple circular Gaussian beam geometry fails to appropriately describe the data, while allowing for a more general beam geometry provides a good fit to the data. The resulting odds between the precession model (with a general beam geometry) and the switching model are estimated as $10^{2.7 \pm 0.5}$ in favour of the precession model.Comment: 20 pages, 15 figures; removed incorrect factor of (2\pi) from equation (15), allowed for arbitrary braking index, and revised prior ranges; overall conclusions unchange

Performance evaluation of MPLS-enabled communications infrastructure for wide area monitoring systems

In order to obtain the transient power system measurement information, Wide Area Monitoring Systems (WAMS) should be able to collect Phasor Measurement Unit (PMU) data in a timely manner. Therefore along with the continual deployment of PMUs in Great Britain (GB) transmission system substations, a high performance communications infrastructure is becoming essential with regard to the establishment of reliable WAMS. This paper focuses mainly on evaluating the performance of the real-time WAMS communication infrastructure when Multi-Protocol Label Switching (MPLS) capability is added to a conventional IP network. Furthermore, PMU communications from geographically distributed substations to a Phasor Data Concentrator (PDC) are investigated over different transport protocols. Using OPNET Modeler, simulations are performed based on the existing WAMS infrastructure as installed on the GB transmission system. The simulation results are analyzed in detail in order to fully determine the different characteristics of communication delays between PMUs and PDC

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

("(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahl's Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment

The mathematics of human contact: Developing a model for social interaction in school children

In this paper, we provide a statistical analysis of high-resolution contact pattern data within primary and secondary schools as collected by the SocioPatterns collaboration. Students are graphically represented as nodes in a temporally evolving network, in which links represent proximity or interaction between students. This article focuses on link- and node-level statistics, such as the on- and off-durations of links as well as the activity potential of nodes and links. Parametric models are fitted to the on- and off-durations of links, inter-event times and node activity potentials and, based on these, we propose a number of theoretical models that are able to reproduce the collected data within varying levels of accuracy. By doing so, we aim to identify the minimal network-level properties that are needed to closely match the real-world data, with the aim of combining this contact pattern model with epidemic models in future work