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Oscillating neutron stars are sources of continuous gravitational waves. We study analytically the excitation of stellar oscillations by the mechanical impact on the stellar surface of 'clumps' of stochastically accreted matter. We calculate the waveform and spectrum of the gravitational wave signal emitted by the accretion-driven pulsations. Results are generated for an idealized model of a non-rotating, unmagnetized, one-component star with uniform polytropic index n poly assuming Newtonian gravity and the Cowling approximation. We find that the excited mode amplitudes grow with increasing n poly and mode order n. The gravitational wave signal forms a sequence of amplitude-modulated packets for n poly = 1, lasting ∼10−3 s after each impact. The gravitational wave strain increases with increasing n poly, but decreases with increasing n and increasing multipole order l for n poly = 1. In the observing band of current long-baseline interferometers, g modes emit higher, narrower peaks in the amplitude spectral density than f and p modes, with the highest peaks reaching ∼10−26 Hz−1/2 for modes with damping time τ nl ∼ 108 yr. The root-mean-square strain h rms, calculated by summing over modes with 2 ≤ l ≤ 4 and τ nl ≤ 108 yr, spans the range 10−33 ≤ h rms ≤ 10−32 for 1 ≤ n poly ≤ 2. [ABSTRACT FROM AUTHOR]

The magnetic dipole moment μ of an accretion-powered pulsar in magnetocentrifugal equilibrium cannot be inferred uniquely from time-averaged pulse period and aperiodic X-ray flux data, because the radiative efficiency η 0 of the accretion is unknown, as are the mass, radius, and distance of the star. The degeneracy associated with the radiative efficiency is circumvented if fluctuations of the pulse period and aperiodic X-ray flux are tracked with a Kalman filter, whereupon μ can be measured uniquely up to the uncertainties in the mass, radius, and distance. Here, the Kalman filter analysis is demonstrated successfully in practice for the first time on Rossi X-ray Timing Explorer observations of the X-ray transient SXP 18.3 in the Small Magellanic Cloud (SMC), which is monitored regularly. The analysis yields μ = 8.0 − 1.2 + 1.3 × 10 30 G cm 3 and η 0 = 0.04 − 0.01 + 0.02 , compared to μ = 5.0 − 1.0 + 1.0 × 10 30 G cm 3 as inferred traditionally from time-averaged data assuming η 0 = 1. The analysis also yields time-resolved estimates of two hidden state variables, the mass accretion rate and the Maxwell stress at the disk–magnetosphere boundary. The success of the demonstration confirms that the Kalman filter analysis can be applied in the future to study the magnetic moments and disk–magnetosphere physics of accretion-powered pulsar populations in the SMC and elsewhere. [ABSTRACT FROM AUTHOR]

Stochastic temporal wandering of the spin frequency ν of a rotation-powered pulsar (i.e. the achromatic component of timing noise unrelated to interstellar propagation) affects the accuracy with which the secular braking torque can be measured. Observational studies confirm that pulsars with anomalous braking indices |$\vert n \vert = \vert \nu \ddot{\nu } / \dot{\nu }^2 \vert \gg 1$| exhibit elevated levels of timing noise, where an overdot symbolizes a derivative with respect to time. Here it is shown, through analytic calculations and Monte Carlo simulations involving synthetic data and modern Bayesian timing techniques, that the variance 〈 n 2〉 of the measured n scales with the square of the timing noise amplitude |$\sigma _{\ddot{\nu }}$|. The anomalous regime 〈 n 2〉 ≫ 1 corresponds to |$\sigma _{\ddot{\nu }}^2 \gg 10^{-60} (\gamma _{\ddot{\nu }}/10^{-6} \, {\rm s^{-1}})^2 (\dot{\nu } / 10^{-14} \, {\rm Hz \, s^{-1}})^4 (\nu / 1 \, {\rm Hz})^{-2} (T_{\rm obs} / 10^8 \, {\rm s}) \, {\rm Hz}^2{\rm s}^{-5 }$| , where |$\gamma _{\ddot{\nu }}$| is a stellar damping time-scale, and T obs is the total observing time. When the inequality in the earlier condition is reversed, n is dominated by the secular braking torque, and timing measurements return n ∼ 3, if the secular braking torque is electromagnetic. The variance 〈 n 2〉 is greater, when the stochastic process driving spin fluctuations differs from the red noise model (e.g. power-law spectral density) assumed in the timing solution. [ABSTRACT FROM AUTHOR]

Solar flares result from the sudden release of energy deposited by subphotospheric motions into the magnetic field of the corona. The deposited energy accumulates secularly between events. One may interpret the observed event statistics as resulting from a state-dependent Poisson process in which the instantaneous flare rate is a function of the stress in the system and a flare becomes certain as the stress approaches a threshold set by the microphysics of the flare trigger. If the system is driven fast, and if the threshold is static and uniform globally, a cross-correlation is predicted between the size of a flare and the forward waiting time to the next flare. This cross-correlation is broadly absent from the Geostationary Operational Environmental Satellite (GOES) soft X-ray flare database. One also predicts higher cross-correlations in active regions where the shapes of the waiting time and size distributions match. Again, there is no evidence for such an association in the GOES data. The data imply at least one of the following: (i) the threshold at which a flare is triggered varies in time; (ii) the rate at which energy is driven into active regions varies in time; (iii) historical flare catalogs are incomplete; or (iv) the description of solar flares as resulting from a buildup and release of energy, once a threshold is reached, is incomplete. [ABSTRACT FROM AUTHOR]

The radio pulsar GLEAM-X J162759.5–523504.3 has an extremely long spin period (⁠|$P = 1091.17\, \mbox{s}$|⁠), and yet seemingly continues to spin-down rapidly (⁠|$\dot{P} < 1.2 \times 10^{-9}\, \mbox{ss}^{-1}$|⁠). The magnetic field strength that is implied, if the source is a neutron star undergoing magnetic dipole braking, could exceed |$10^{16}\, \mbox{G}$|⁠. This object may therefore be the most magnetized neutron star observed to date. In this paper, a critical analysis of a magnetar interpretation for the source is provided. (i) A minimum polar magnetic field strength of |$B \sim 5 \times 10^{15}\, \mbox{G}$| appears to be necessary for the star to activate as a radio pulsar, based on conventional 'death valley' assumptions. (ii) Back-extrapolation from magnetic braking and Hall–plastic–Ohm decay suggests that a large angularize momentum reservoir was available at birth to support intense field amplification. (iii) The observational absence of X-rays constrains the star's field strength and age, as the competition between heating from field decay and Urca cooling implies a surface luminosity as a function of time. If the object is an isolated, young (⁠|$\sim 10\, \mbox{kyr}$|⁠) magnetar with a present-day field strength of |$B \gtrsim 10^{16}\, \mbox{G}$|⁠ , the upper limit (⁠|$\approx 10^{30}\, \mbox{erg s}^{-1}$|⁠) set on its thermal luminosity suggests it is cooling via a direct Urca mechanism. [ABSTRACT FROM AUTHOR]

Stress accumulation-relaxation meta-models of pulsar glitches make precise, microphysics-agnostic predictions of long-term glitch statistics, which can be falsified by existing and future timing data. Previous meta-models assume that glitches are triggered by an avalanche process, e.g., involving superfluid vortices, and that the probability density function (PDF) of the avalanche sizes is history independent and specified exogenously. Here, a recipe is proposed to generate the avalanche sizes endogenously in a history-dependent manner, by tracking the thresholds of occupied vortex pinning sites as a function of time. Vortices unpin spasmodically from sites with thresholds below a global, time-dependent stress and repin at sites with thresholds above the global stress, imbuing the system with long-term memory. The meta-model predicts PDFs, auto-, and cross-correlations for glitch sizes and waiting times, which are provisionally inconsistent with current observations, unlike some previous meta-models (e.g., state-dependent Poisson process), whose predictions are consistent. The theoretical implications are intriguing, albeit uncertain, because history-dependent avalanches embody faithfully the popular, idealized understanding in the literature of how vortex unpinning operates as a driven, stochastic process. The meta-model predicts aftershocks, which occur with larger than average sizes and longer than average waiting times after the largest, system-resetting glitches. This prediction will be tested, once more data are generated by the next generation of pulsar timing campaigns. [ABSTRACT FROM AUTHOR]

A quark-magnetic Ginzburg–Landau (qHGL) gradient expansion of the free energy of two-flavor inhomogeneous quark matter in a magnetic field H is derived analytically. It can be applied away from the Lifshitz point, generalizing standard Ginzburg-Landau techniques. The thermodynamic potential is written as a sum of the thermal contribution, the non-thermal lowest Landau level contribution, and the non-thermal qHGL functional, which handles any arbitrary position-dependent periodic modulation of the chiral condensate as an input. The qHGL approximation has two main practical features: (1) it is fast to compute; (2) it applies to non-plane-wave modulations such as solitons even when the amplitude of the condensate and its gradients are large (unlike standard Ginzburg-Landau techniques). It agrees with the output of numerical techniques based on standard regularization schemes and reduces to known results at zero temperature ( T = 0 ) in benchmark studies. It is found that the region of the μ -T plane (where μ is the chemical potential) occupied by the inhomogeneous phase expands, as H increases and T decreases. [ABSTRACT FROM AUTHOR]

In the standard two-component crust-superfluid model of a neutron star, timing noise can arise when the two components are perturbed by stochastic torques. Here it is demonstrated how to analyse fluctuations in radio pulse times of arrival with a Kalman filter to measure physical properties of the two-component model, including the crust-superfluid coupling time-scale and the variances of the crust and superfluid torques. The analysis technique, validated previously on synthetic data, is applied to observations with the Molonglo Observatory Synthesis Telescope of the representative pulsar PSR J1359−6038. It is shown that the two-component model is preferred to a one-component model, with log Bayes factor 6.81 ± 0.02. The coupling time-scale and the torque variances on the crust and superfluid are measured with 90 per cent confidence to be |$10^{7.1^{+0.8}_{-0.5}}$| |$\rm {s}$| and |$10^{-24.0^{+0.4}_{-5.6}}$| |$\rm {rad^2~s^{-3}}$| and |$10^{-21.7^{+3.5}_{-0.9}}$| |$\rm {rad^2~s^{-3}}$|⁠ , respectively. [ABSTRACT FROM AUTHOR]

It is an open challenge to estimate systematically the physical parameters of neutron star interiors from pulsar timing data while separating spin wandering intrinsic to the pulsar (achromatic timing noise) from measurement noise and chromatic timing noise (due to propagation effects). In this paper, we formulate the classic two-component, crust-superfluid model of neutron star interiors as a noise-driven, linear dynamical system and use a state-space-based expectation–maximization method to estimate the system parameters using gravitational-wave and electromagnetic timing data. Monte Carlo simulations show that we can accurately estimate all six parameters of the two-component model provided that electromagnetic measurements of the crust angular velocity and gravitational-wave measurements of the core angular velocity are both available. When only electromagnetic data are available, we can recover the overall relaxation time-scale, the ensemble-averaged spin-down rate, and the strength of the white-noise torque on the crust. However, the estimates of the secular torques on the two components and white-noise torque on the superfluid are biased significantly. [ABSTRACT FROM AUTHOR]

An individual's opinions about media bias derive from their own independent assessment of media outputs combined with peer pressure from networked political allies and opponents. Here we generalize previous idealized, probabilistic models of the perception formation process, based on a network of Bayesian learners inferring the bias of a coin, by introducing obdurate agents (partisans), whose opinions stay fixed. It is found that even one partisan destabilizes an allies-only network, stopping it from achieving asymptotic learning and forcing persuadable agents to vacillate indefinitely (turbulent nonconvergence) between the true coin bias θ 0 and the partisan's belief θ p. The dwell time t d at the partisan's belief increases, as the partisan fraction f increases, and decreases, when multiple partisans disagree amongst themselves. In opponents-only networks, asymptotic learning occurs, whether or not partisans are present. However, the counterintuitive tendency to reach wrong conclusions first, identified in previous work with zero partisans, does not persist in general for θ 0 ≠ θ p in complete networks; it is a property of sparsely connected systems (e.g. Barabási–Albert networks with attachment parameter ≲ 10). In mixed networks containing allies and opponents, partisans drive counterintuitive outcomes, which depend sensitively, on where they reside. A strongly balanced triad exhibits intermittency with a partisan (sudden transitions between long intervals of static beliefs and turbulent nonconvergence) and asymptotic learning without a partisan. Counterintuitively, in an unbalanced triad, one of the persuadable agents achieves asymptotic learning at θ 0 , when the partisan is located favorably, but is driven away from θ 0 , when there is no partisan. The above results are interpreted briefly in terms of the social science theory of structural balance. • Even one partisan destabilizes an allies-only network and drives vacillating beliefs. • Disagreeing partisans heighten network instability and shorten dwell times. • Opponents-only networks achieve asymptotic learning with or without partisans. • Wrong conclusions are reached first in sparsely connected opponents-only networks. • Various ally-opponent networks containing partisans exhibit intermittency. [ABSTRACT FROM AUTHOR]

We consider optimal Bayesian detection of a slowly varying tone of unknown amplitude in situations characterized by very low signal-to-noise ratio (SNR) and a large number of measurements, as found in certain gravitational wave and passive sonar problems. We use a hidden Markov model (HMM) framework but, unlike typical HMM-based frequency line tracking methods, we develop a true track-before-detect algorithm, which does not threshold the blocked Fourier data and only considers frequency trails that have phase continuity across all HMM steps. We model the frequency and phase evolution as a phase-wrapped Ornstein–Uhlenbeck process. The resulting optimal detector is computationally efficient. The detectability improvement arising from phase continuity is characterized via comparative simulation for a mock, simplified gravitational wave search problem. [ABSTRACT FROM AUTHOR]

One form of long-term behavior revealed by opinion dynamics simulations is intermittency, where an individual cycles between eras of stable, constant beliefs and turbulent, fluctuating beliefs, for example when inferring the political bias of a media organization. We explore this phenomenon by building an idealized network of Bayesian learners, who infer the bias of a coin from observations of coin tosses and peer pressure from political allies and opponents. Numerical simulations reveal that three types of network structure lead to three different types of intermittency, which are caused by agents "locking out" opponents from sure beliefs in specific ways. The probability density functions of the dwell times, over which the learners sustain stable or turbulent beliefs, differ across the three types of intermittency. Hence, one can observe the dwell times of a learner to infer the underlying network structure, at least in principle. • Networked learners can cycle between extended eras of stable and turbulent beliefs. • Intermittency occurs in three types of mixed (allies plus opponents) networks. • Intermittency occurs even in static networks due to conflicting peer pressure. • Observations of belief dwell times can be used to infer network structure. [ABSTRACT FROM AUTHOR]

Enormous progress has been made on observing stellar magnetism in stars from the main sequence (particularly thanks to the MiMeS, MAGORI and BOB surveys) through to compact objects. Recent data have thrown into sharper relief the vexed question of the origin of stellar magnetic fields, which remains one of the main unanswered questions in astrophysics. In this chapter we review recent work in this area of research. In particular, we look at the fossil field hypothesis which links magnetism in compact stars to magnetism in main sequence and pre-main sequence stars and we consider why its feasibility has now been questioned particularly in the context of highly magnetic white dwarfs. We also review the fossil versus dynamo debate in the context of neutron stars and the roles played by key physical processes such as buoyancy, helicity, and superfluid turbulence, in the generation and stability of neutron star fields. Independent information on the internal magnetic field of neutron stars will come from future gravitational wave detections. Coherent searches for the Crab pulsar with the Laser Interferometer Gravitational Wave Observatory (LIGO) have already constrained its gravitational wave luminosity to be ≲2 % of the observed spin-down luminosity, thus placing a limit of ≲10 G on the internal field. Indirect spin-down limits inferred from recycled pulsars also yield interesting gravitational-wave-related constraints. Thus we may be at the dawn of a new era of exciting discoveries in compact star magnetism driven by the opening of a new, non-electromagnetic observational window. We also review recent advances in the theory and computation of magnetohydrodynamic turbulence as it applies to stellar magnetism and dynamo theory. These advances offer insight into the action of stellar dynamos as well as processes which control the diffusive magnetic flux transport in stars. [ABSTRACT FROM AUTHOR]

We report results of a numerical analysis of the memory effects in two-dimensional Abelian sandpiles. It is found that a sandpile forgets its instantaneous configuration in two distinct stages: a fast stage and a slow stage, whose durations roughly scale as N and N 2 respectively, where N is the linear size of the sandpile. We confirm the presence of the longer time-scale by an independent diagnostic based on analysing emission probabilities of a hidden Markov model applied to a time-averaged sequence of avalanche sizes. The application of hidden Markov modelling to the output of sandpiles is novel. It discriminates effectively between a sandpile time series and a shuffled control time series with the same time-averaged event statistics and hence deserves further development as a pattern-recognition tool for Abelian sandpiles. [ABSTRACT FROM AUTHOR]

The sensitivity of pulsar timing arrays to gravitational waves is, at some level, limited by timing noise. Red timing noise - the stochastic wandering of pulse arrival times with a red spectrum - is prevalent in slow-spinning pulsars and has been identified in many millisecond pulsars. Phenomenological models of timing noise, such as from superfluid turbulence, suggest that the timing noise spectrum plateaus below some critical frequency, fc, potentially aiding the hunt for gravitational waves. We examine this effect for individual pulsars by calculating minimum observation times, Tmin(fc), over which the gravitational wave signal becomes larger than the timing noise plateau. We do this in two ways: (1) in a model-independent manner, and (2) by using the superfluid turbulence model for timing noise as an example to illustrate how neutron star parameters can be constrained. We show that the superfluid turbulence model can reproduce the data qualitatively from a number of pulsars observed as part of the Parkes Pulsar Timing Array. We further show how a value of fc, derived either through observations or theory, can be related to Tmin. This provides a diagnostic whereby the usefulness of timing array pulsars for gravitational-wave detection can be quantified. [ABSTRACT FROM AUTHOR]

Radio pulsars provide us with some of the most stable clocks in the universe. Nevertheless several pulsars exhibit sudden spin-up events, known as glitches. More than forty years after their first discovery, the exact origin of these phenomena is still open to debate. It is generally thought that they are observational manifestation of a superfluid component in the stellar interior and provide an insight into the dynamics of matter at extreme densities. In recent years, there have been several advances on both the theoretical and observational side, that have provided significant steps forward in our understanding of neutron star interior dynamics and possible glitch mechanisms. In this article we review the main glitch models that have been proposed and discuss our understanding, in the light of current observations. [ABSTRACT FROM AUTHOR]

Perceptions of political bias in the media are formed directly, through the independent consumption of the published outputs of a media organization, and indirectly, through observing the collective responses of political allies and opponents to the same published outputs. A network of Bayesian learners is constructed to model this system, in which the bias perceived by each agent obeys a probability density function, which is updated according to Bayes's theorem given data about the published outputs and the beliefs of the agent's political allies and opponents. The Bayesian framework allows for uncertain beliefs, multimodal probability distribution functions, and antagonistic interactions with opponents, not just cooperation with allies. Numerical simulations are performed to test the idealized example of inferring the bias of a coin. It is found that some agents converge on the wrong conclusion faster than others converge on the right conclusion under a surprisingly broad range of conditions, when antagonistic interactions are present which "lock out" some agents from the truth, e.g. in Barabási–Albert networks. It is also found that structurally unbalanced networks routinely experience turbulent nonconvergence, where some agents fail to achieve a steady-state belief, e.g. when they are allies of two agents who are opponents themselves. The subtle phenomenon of long-term intermittency is also explored. • Opponents-only networks encourage the wrong conclusion to be reached first. • Ally-opponent networks prevent a steady-state conclusion from being reached. • Some agents in ally-opponent networks vacillate between certainty and doubt. • Discernment of media bias by Bayesian learners fits structural balance theory. [ABSTRACT FROM AUTHOR]

Abstract: We analyze a simple asset transfer model in which the transfer amount is a fixed fraction of the giver’s wealth. The model is analyzed in a new way by Laplace transforming the master equation, solving it analytically and numerically for the steady-state distribution, and exploring the solutions for various values of . The Laplace transform analysis is superior to agent-based simulations as it does not depend on the number of agents, enabling us to study entropy and inequality in regimes that are costly to address with simulations. We demonstrate that Boltzmann entropy is not a suitable (e.g. non-monotonic) measure of disorder in a multiplicative asset transfer system and suggest an asymmetric stochastic process that is equivalent to the asset transfer model. [Copyright &y& Elsevier]

A population of supermassive black hole (SMBH) binaries is expected to generate a stochastic gravitational wave background (SGWB) in the pulsar timing array (PTA) frequency range of 10−9 to |$10^{-7}\, {\rm Hz}$|⁠. Detection of this signal is a current observational goal and so predictions of its characteristics are of significant interest. In this work, we use SMBH binary mergers from the MassiveBlackII simulation to estimate the characteristic strain of the stochastic background. We examine both a gravitational wave (GW) driven model of binary evolution and a model which also includes the effects of stellar scattering and a circumbinary gas disc. Results are consistent with PTA upper limits and similar to estimates in the literature. The characteristic strain at a reference frequency of |$1\, {\rm yr}^{-1}$| is found to be |$A_{\rm {yr}^{-1}}= 6.9 \times 10^{-16}$| and |$A_{\rm {yr}^{-1}}= 6.4 \times 10^{-16}$| in the GW-driven and stellar scattering/gas disc cases, respectively. Using the latter approach, our models show that the SGWB is mildly suppressed compared to the purely GW-driven model as frequency decreases inside the PTA frequency band. [ABSTRACT FROM AUTHOR]

The classic, two-component, crust-superfluid model of a neutron star can be formulated as a noise-driven, linear dynamical system, in which the angular velocities of the crust and superfluid are tracked using a Kalman filter applied to electromagnetic pulse timing data and gravitational-wave data, when available. Here it is shown how to combine the marginal likelihood of the Kalman filter and nested sampling to estimate full posterior distributions of the six model parameters, extending previous analyses based on a maximum-likelihood approach. The method is tested across an astrophysically plausible parameter domain using Monte Carlo simulations. It recovers the injected parameters to ≲10 per cent for time series containing ∼103 samples, typical of long-term pulsar timing campaigns. It runs efficiently in |$\mathcal {O}(1)$| CPU-hr for data sets of the above size. In a present-day observational scenario, when electromagnetic data are available only, the method accurately estimates three parameters: the relaxation time, the ensemble-averaged spin-down of the system, and the amplitude of the stochastic torques applied to the crust. In a future observational scenario, where gravitational-wave data are also available, the method also estimates the ratio between the moments of inertia of the crust and the superfluid, the amplitude of the stochastic torque applied to the superfluid, and the crust-superfluid lag. These empirical results are consistent with a formal identifiability analysis of the linear dynamical system. [ABSTRACT FROM AUTHOR]

The general relativistic formulation of the problem of magnetically confined mountains on neutron stars is presented, and the resulting equations are solved numerically, generalizing previous Newtonian calculations. The hydromagnetic structure of the accreted matter and the subsequent magnetic burial of the star's magnetic dipole moment are computed. Overall, it is observed that relativistic corrections reduce the hydromagnetic deformation associated with the mountain. The magnetic field lines are curved more gently than in previous calculations, and the screening of the dipole moment is reduced. Quantitatively, it is found that the dimensionless dipole moment (m d) depends on the accreted mass (M a) as m d = −3.2 × 103 M a/ M ⊙ + 1.0, implying approximately three times less screening compared to the Newtonian theory. Additionally, the characteristic scale height of the mountain, governing the gradients of quantities like pressure, density, and magnetic field strength, reduces by approximately 40  per cent for an isothermal equation of state. [ABSTRACT FROM AUTHOR]

Detection of continuous gravitational waves from rapidly spinning neutron stars opens up the possibility of examining their internal physics. We develop a framework that leverages a future continuous gravitational wave detection to infer a neutron star's moment of inertia, equatorial ellipticity, and the component of the magnetic dipole moment perpendicular to its rotation axis. We assume that the neutron star loses rotational kinetic energy through both gravitational wave and electromagnetic radiation, and that the distance to the neutron star can be measured, but do not assume electromagnetic pulsations are observable or a particular neutron star equation of state. We use the Fisher information matrix and Monte Carlo simulations to estimate errors in the inferred parameters, assuming a population of gravitational-wave-emitting neutron stars consistent with the typical parameter domains of continuous gravitational wave searches. After an observation time of 1 yr, the inferred errors for many neutron stars are limited chiefly by the error in the distance to the star. The techniques developed here will be useful if continuous gravitational waves are detected from a radio, X-ray, or gamma-ray pulsar, or else from a compact object with known distance, such as a supernova remnant. [ABSTRACT FROM AUTHOR]

Inference and hypothesis testing are typically constructed on the basis that a specific model holds for the data. To determine the veracity of conclusions drawn from such data analyses, one must be able to identify the presence of the assumed structure within the data. In this paper, a model verification test is developed for the presence of a random walk-like structure in the variations in the frequency of complex-valued sinusoidal signals measured in additive Gaussian noise. This test evaluates the joint inference of the random walk hypothesis tests found in economics literature that seek random walk behaviours in time series data, with an additional test to account for how the random walk behaves in frequency space. [ABSTRACT FROM AUTHOR]

Magnetars are neutron stars in which a strong magnetic field is the main energy source. About two dozens of magnetars, plus several candidates, are currently known in our Galaxy and in the Magellanic Clouds. They appear as highly variable X-ray sources and, in some cases, also as radio and/or optical pulsars. Their spin periods (2-12 s) and spin-down rates (∼10-10 s s) indicate external dipole fields of ∼10 G, and there is evidence that even stronger magnetic fields are present inside the star and in non-dipolar magnetospheric components. Here we review the observed properties of the persistent emission from magnetars, discuss the main models proposed to explain the origin of their magnetic field and present recent developments in the study of their evolution and connection with other classes of neutron stars. [ABSTRACT FROM AUTHOR]

Gravitational-wave observatories around the world are searching for continuous waves: persistent signals from sources, such as spinning neutron stars. These searches use sophisticated statistical techniques to look for weak signals in noisy data. In this paper, we demonstrate these techniques using a table-top model gravitational-wave detector: a Michelson interferometer where sound is used as an analog for gravitational waves. Using signal processing techniques from continuous-wave searches, we demonstrate the recovery of tones with constant and wandering frequencies. We also explore the use of the interferometer as a teaching tool for educators in physics and electrical engineering by using it as an "optical microphone" to capture music and speech. A range of filtering techniques used to recover signals from noisy data are detailed in the supplementary material of this article. Here, we present the highlights of our results using a combined notch plus Wiener filter and the statistical log minimum mean-square error (logMMSE) estimator. Using these techniques, we easily recover recordings of simple chords and drums, but complex music and speech are more challenging. This demonstration can be used by educators in undergraduate laboratories and can be adapted for communicating gravitational-wave and signal-processing topics to nonspecialist audiences. [ABSTRACT FROM AUTHOR]

Abstract: High-value transactions between banks in Australia are settled in the Reserve Bank Information and Transfer System (RITS) administered by the Reserve Bank of Australia. RITS operates on a real-time gross settlement (RTGS) basis and settles payments and transfers sourced from the SWIFT payment delivery system, the Austraclear securities settlement system, and the interbank transactions entered directly into RITS. In this paper, we analyse a dataset received from the Reserve Bank of Australia that includes all interbank transactions settled in RITS on an RTGS basis during five consecutive weekdays from 19 February 2007 inclusive, a week of relatively quiescent market conditions. The source, destination, and value of each transaction are known, which allows us to separate overnight loans from other transactions (nonloans) and reconstruct monetary flows between banks for every day in our sample. We conduct a novel analysis of the flow stability and examine the connection between loan and nonloan flows. Our aim is to understand the underlying causal mechanism connecting loan and nonloan flows. We find that the imbalances in the banks’ exchange settlement funds resulting from the daily flows of nonloan transactions are almost exactly counterbalanced by the flows of overnight loans. The correlation coefficient between loan and nonloan imbalances is about −0.9 on most days. Some flows that persist over two consecutive days can be highly variable, but overall the flows are moderately stable in value. The nonloan network is characterised by a large fraction of persistent flows, whereas only half of the flows persist over any two consecutive days in the loan network. Moreover, we observe an unusual degree of coherence between persistent loan flow values on Tuesday and Wednesday. We probe static topological properties of the Australian interbank network and find them consistent with those observed in other countries. [Copyright &y& Elsevier]

Self-organization is a property of dissipative nonlinear processes that are governed by a global driving force and a local positive feedback mechanism, which creates regular geometric and/or temporal patterns, and decreases the entropy locally, in contrast to random processes. Here we investigate for the first time a comprehensive number of (17) self-organization processes that operate in planetary physics, solar physics, stellar physics, galactic physics, and cosmology. Self-organizing systems create spontaneous "order out of randomness", during the evolution from an initially disordered system to an ordered quasi-stationary system, mostly by quasi-periodic limit-cycle dynamics, but also by harmonic (mechanical or gyromagnetic) resonances. The global driving force can be due to gravity, electromagnetic forces, mechanical forces (e.g., rotation or differential rotation), thermal pressure, or acceleration of nonthermal particles, while the positive feedback mechanism is often an instability, such as the magneto-rotational (Balbus-Hawley) instability, the convective (Rayleigh-Bénard) instability, turbulence, vortex attraction, magnetic reconnection, plasma condensation, or a loss-cone instability. Physical models of astrophysical self-organization processes require hydrodynamic, magneto-hydrodynamic (MHD), plasma, or N-body simulations. Analytical formulations of self-organizing systems generally involve coupled differential equations with limit-cycle solutions of the Lotka-Volterra or Hopf-bifurcation type. [ABSTRACT FROM AUTHOR]