Methods for characterising microphysical processes in plasmas

Advanced spectral and statistical data analysis techniques have greatly contributed to shaping our understanding of microphysical processes in plasmas. We review some of the main techniques that allow for characterising fluctuation phenomena in geospace and in laboratory plasma observations. Special emphasis is given to the commonalities between different disciplines, which have witnessed the development of similar tools, often with differing terminologies. The review is phrased in terms of few important concepts: self-similarity, deviation from self-similarity (i.e. intermittency and coherent structures), wave-turbulence, and anomalous transport.Comment: Space Science Reviews (2013), in pres

Image Decomposition and Separation Using Sparse Representations: An Overview

This paper gives essential insights into the use of sparsity and morphological diversity in image decomposition and source separation by reviewing our recent work in this field. The idea to morphologically decompose a signal into its building blocks is an important problem in signal processing and has far-reaching applications in science and technology. Starck , proposed a novel decomposition method—morphological component analysis (MCA)—based on sparse representation of signals. MCA assumes that each (monochannel) signal is the linear mixture of several layers, the so-called morphological components, that are morphologically distinct, e.g., sines and bumps. The success of this method relies on two tenets: sparsity and morphological diversity. That is, each morphological component is sparsely represented in a specific transform domain, and the latter is highly inefficient in representing the other content in the mixture. Once such transforms are identified, MCA is an iterative thresholding algorithm that is capable of decoupling the signal content. Sparsity and morphological diversity have also been used as a novel and effective source of diversity for blind source separation (BSS), hence extending the MCA to multichannel data. Building on these ingredients, we will provide an overview the generalized MCA introduced by the authors in and as a fast and efficient BSS method. We will illustrate the application of these algorithms on several real examples. We conclude our tour by briefly describing our software toolboxes made available for download on the Internet for sparse signal and image decomposition and separation

Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders

Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-posed and is further challenged by the variety of time-scales exhibited by sources in time series data. Existing methods typically rely on a preselected window size that limits their capacity to handle multi-scale sources. To address this issue, instead of operating in the time domain, we propose an unsupervised multi-scale clustering and source separation framework by leveraging wavelet scattering covariances that provide a low-dimensional representation of stochastic processes, capable of distinguishing between different non-Gaussian stochastic processes. Nested within this representation space, we develop a factorial Gaussian-mixture variational autoencoder that is trained to (1) probabilistically cluster sources at different time-scales and (2) independently sample scattering covariance representations associated with each cluster. Using samples from each cluster as prior information, we formulate source separation as an optimization problem in the wavelet scattering covariance representation space, resulting in separated sources in the time domain. When applied to seismic data recorded during the NASA InSight mission on Mars, our multi-scale nested approach proves to be a powerful tool for discriminating between sources varying greatly in time-scale, e.g., minute-long transient one-sided pulses (known as ``glitches'') and structured ambient noises resulting from atmospheric activities that typically last for tens of minutes. These results provide an opportunity to conduct further investigations into the isolated sources related to atmospheric-surface interactions, thermal relaxations, and other complex phenomena

Arecibo Pulsar Survey Using ALFA: Probing Radio Pulsar Intermittency and Transients

We present radio transient search algorithms, results, and statistics from the ongoing Arecibo Pulsar ALFA (PALFA) survey of the Galactic plane. We have discovered seven objects through a search for isolated dispersed pulses. All of these objects are Galactic and have measured periods between 0.4 and 4.7 s. One of the new discoveries has a duty cycle of 0.01%, smaller than that of any other radio pulsar. We discuss the impact of selection effects on the detectability and classification of intermittent sources, and compare the efficiencies of periodicity and single-pulse (SP) searches for various pulsar classes. For some cases we find that the apparent intermittency is likely to be caused by off-axis detection or a short time window that selects only a few bright pulses and favors detection with our SP algorithm. In other cases, the intermittency appears to be intrinsic to the source. No transients were found with DMs large enough to require that they originate from sources outside our Galaxy. Accounting for the on-axis gain of the ALFA system, as well as the low gain but large solid-angle coverage of far-out sidelobes, we use the results of the survey so far to place limits on the amplitudes and event rates of transients of arbitrary origin

Hypothesis testing for nonlinear phenomena in the geosciences using synthetic, surrogate data

©2018. The Authors. Studying nonlinear and potentially chaotic phenomena in geophysics from measured signals is problematic when system noise interferes with the dynamic processes that one is trying to infer. In such circumstances, a framework for statistical hypothesis testing is necessary but the nonlinear nature of the phenomena studied makes the formulation of standard hypothesis tests, such as analysis of variance, problematic as they are based on underlying linear, Gaussian assumptions. One approach to this problem is the method of surrogate data, which is the technique explained in this paper. In particular, we focus on (i) hypothesis testing for nonlinearity by generating linearized surrogates as a null hypothesis, (ii) a variant of this that is perhaps more appropriate for image data where structural nonlinearities are common and should be retained in the surrogates, and (iii) gradual reconstruction where we systematically constrain the surrogates until there is no significant difference between data and surrogates and use this to understand geophysical processes. In addition to time series of sunspot activity, solutions to the Lorenz equations, and spatial maps of enstrophy in a turbulent channel flow, two examples are considered in detail. The first concerns gradual wavelet reconstruction testing of the significance of a specific vortical flow structure from turbulence time series acquired at a point. In the second, the degree of nonlinearity in the spatial profiles of river curvature is shown to be affected by the occurrence of meander cutoff processes but in a more complex fashion than previously envisaged

Arecibo pulsar survey using ALPHA: Probing radio pulsar intermittency and transients

We present radio transient search algorithms, results, and statistics from the ongoing Arecibo Pulsar ALFA (PALFA) survey of the Galactic plane. We have discovered seven objects through a search for isolated dispersed pulses. All of these objects are Galactic and have measured periods between 0.4 and 4.7 s. One of the new discoveries has a duty cycle of 0.01%, smaller than that of any other radio pulsar. We discuss the impact of selection effects on the detectability and classification of intermittent sources, and compare the efficiencies of periodicity and single-pulse (SP) searches for various pulsar classes. For some cases we find that the apparent intermittency is likely to be caused by off-axis detection or a short time window that selects only a few bright pulses and favors detection with our SP algorithm. In other cases, the intermittency appears to be intrinsic to the source. No transients were found with DMs large enough to require that they originate from sources outside our Galaxy. Accounting for the on-axis gain of the ALFA system, as well as the low gain but large solid-angle coverage of far-out sidelobes, we use the results of the survey so far to place limits on the amplitudes and event rates of transients of arbitrary origin. © 2009. The American Astronomical Society. All rights reserved.

Einstein@Home Discovery of 24 Pulsars in the Parkes Multi-Beam Pulsar Survey

We have conducted a new search for radio pulsars in compact binary systems in the Parkes multi-beam pulsar survey (PMPS) data, employing novel methods to remove the Doppler modulation from binary motion. This has yielded unparalleled sensitivity to pulsars in compact binaries. The required computation time of 17, 000 CPU core years was provided by the distributed volunteer computing project Einstein@Home, which has a sustained computing power of about 1 PFlop s–1. We discovered 24 new pulsars in our search, 18 of which were isolated pulsars, and 6 were members of binary systems. Despite the wide filterbank channels and relatively slow sampling time of the PMPS data, we found pulsars with very large ratios of dispersion measure (DM) to spin period. Among those is PSR J1748–3009, the millisecond pulsar with the highest known DM (420 pc cm–3). We also discovered PSR J1840–0643, which is in a binary system with an orbital period of 937 days, the fourth largest known. The new pulsar J1750–2536 likely belongs to the rare class of intermediate-mass binary pulsars. Three of the isolated pulsars show long-term nulling or intermittency in their emission, further increasing this growing family. Our discoveries demonstrate the value of distributed volunteer computing for data-driven astronomy and the importance of applying new analysis methods to extensively searched data

Differential behaviour of distinct motoneuron pools that innervate the triceps surae

It has been shown that when humans lean in various directions, the central nervous system (CNS) recruits different motoneuron pools for task completion; common units that are active during different leaning directions, and unique units that are active in only one leaning direction. We used high-density surface electromyography (HD-sEMG) to examine if motor unit (MU) firing behaviour was dependent on leaning direction, muscle (medial and lateral gastrocnemius; soleus), limits of stability, or whether a MU is considered common or unique. Fourteen healthy participants stood on a force platform and maintained their center of pressure in five different leaning directions. HD-sEMG recordings were decomposed into MU action potentials and the average firing rate (AFR), coefficient of variation (CoVISI) and firing intermittency were calculated on the MU spike trains. During the 30-90º leaning directions both unique units and common units had higher firing rates (F = 31.31, p \u3c 0.0001). However, the unique units achieved higher firing rates compared to the common units (mean estimate difference = 3.48 Hz, p \u3c 0.0001). The CoVISI increased across directions for the unique units but not for the common units (F = 23.65. p \u3c 0.0001). Finally, intermittent activation of MUs was dependent on the leaning direction (F = 11.15, p \u3c 0.0001), with less intermittent activity occurring during diagonal and forward-leaning directions. These results provide evidence that the CNS can preferentially control separate motoneuron pools within the ankle plantarflexors during voluntary leaning tasks for the maintenance of standing balance

New Discoveries from the Arecibo 327 MHz Drift Pulsar Survey Radio Transient Search

We present Clusterrank, a new algorithm for identifying dispersed astrophysical pulses. Such pulses are commonly detected from Galactic pulsars and rotating radio transients (RRATs), which are neutron stars with sporadic radio emission. More recently, isolated, highly dispersed pulses dubbed fast radio bursts (FRBs) have been identified as the potential signature of an extragalactic cataclysmic radio source distinct from pulsars and RRATs. Clusterrank helped us discover 14 pulsars and 8 RRATs in data from the Arecibo 327 MHz Drift Pulsar Survey (AO327). The new RRATs have DMs in the range $23.5 - 86.6$ pc cm$^{-3}$ and periods in the range $0.172 - 3.901$ s. The new pulsars have DMs in the range $23.6 - 133.3$ pc cm$^{-3}$ and periods in the range $1.249 - 5.012$ s, and include two nullers and a mode-switching object. We estimate an upper limit on the all-sky FRB rate of $10^5$ day$^{-1}$ for bursts with a width of 10 ms and flux density $\gtrsim 83$ mJy. The DMs of all new discoveries are consistent with a Galactic origin. In comparing statistics of the new RRATs with sources from the RRATalog, we find that both sets are drawn from the same period distribution. In contrast, we find that the period distribution of the new pulsars is different from the period distributions of canonical pulsars in the ATNF catalog or pulsars found in AO327 data by a periodicity search. This indicates that Clusterrank is a powerful complement to periodicity searches and uncovers a subset of the pulsar population that has so far been underrepresented in survey results and therefore in Galactic pulsar population models.Comment: 41 pages, 16 figures, 4 tables, accepted by ApJ; added minor corrections to final ApJ proo

Quantum dot clusters as single-molecules: deciphering collective fluorescence and energy transfer signatures

2016 Fall.Includes bibliographical references.Applications of quantum dot nanocrystals span from the individual single-molecule use to large, densely-packed bulk solids. Already, the fluorescence behavior of individual particles is complex and nuanced, particularly involving the blinking phenomenon. When particles are combined into higher-order structures where interaction may occur, a complete description becomes intractable. However, clusters---between two and ten particles---can be effective model systems to explore the local behaviors that occur in larger networks. A benefit of small clusters is the viability of using single-molecule spectroscopic techniques, which are often more informative than bulk measurements. In this work we combine fluorescence microscopy with structure-probing electron microscopy to elucidate the fluorescence dynamics clusters of semiconductor nanocrystals. The spectral characteristics of clusters are explored in the context of an energy transfer model showing low-intensity emission is blue-shifted, corresponding to the weaker emission from donor particles with a larger band gap. Because energy transfer depends intimately on the specific topographical structure of the cluster, the inter-particle spacing, and relative alignment, characterization of specific cluster behavior is better informed by correlated measurements. Next, we present the mapping results from super-resolution microscopy where the spatial distributions of fluorescence in the sub-10 nanometer realm is clearly correlated with scanning electron microscopy imaging of the same clusters. Stochastic blinking events enable such observations. The enhanced blinking associated with energy transfer has practical implications for donor and acceptor roles in clusters. Finally, the dynamic evolution of the emission dipole orientation for single nanocrystals and nanocrystal clusters is measured. The orientation signature suggests coupling strengths and constitutes a first-step towards determining corrections to Förster resonant energy transfer theory involving nanocrystals