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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
K. P. Harikrishnan; Rinku Jacob; R. Misra; G. Ambika

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The analysis of observed time series from nonlinear systems is usually done by making a time-delay reconstruction to unfold the dynamics on a multi-dimensional state space. An important aspect of the analysis is the choice of the correct embedding dimension. The conventional procedure used for this is either the method of false nearest neighbors or the saturation of some invariant measure, such as, correlation dimension. Here we examine this issue from a complex network perspective and propose...

Topics: Physics, Neurons and Cognition, Data Analysis, Statistics and Probability, Nonlinear Sciences,...

Source: http://arxiv.org/abs/1704.08585

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
I. Eghdami; H. Panahi; S. M. S. Movahed

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Relying on multifractal behavior of pulsar timing residuals ({\it PTR}s), we examine the capability of Multifractal Detrended Fluctuation Analysis (MF-DFA) and Multifractal Detrending Moving Average Analysis (MF-DMA) modified by Singular Value Decomposition (SVD) and Adaptive Detrending (AD), to detect footprint of gravitational waves (GWs) superimposed on {\it PTR}s. Mentioned methods enable us to clarify the type of GWs which is related to the value of Hurst exponent. We introduce three...

Topics: Physics, Astrophysics, Data Analysis, Statistics and Probability, Solar and Stellar Astrophysics,...

Source: http://arxiv.org/abs/1704.08599

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2.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Gianna Vivaldo; Elisa Masi; Cosimo Taiti; Guido Caldarelli; Stefano Mancuso

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Plants emission of volatile organic compounds (VOCs) is involved in a wide class of ecological functions, as VOCs play a crucial role in plants interactions with biotic and abiotic factors. Accordingly, they vary widely across species and underpin differences in ecological strategy. In this paper, VOCs spontaneously emitted by 109 plant species (belonging to 56 different families) have been qualitatively and quantitatively analysed in order to classify plants species. By using bipartite...

Topics: Physics, Quantitative Biology, Data Analysis, Statistics and Probability, Quantitative Methods

Source: http://arxiv.org/abs/1704.08062

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Marc Wiedermann; Jonathan F. Donges; Jürgen Kurths; Reik V. Donner

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Complex networks are usually characterized in terms of their topological, spatial, or information-theoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks, like infrastructure or...

Topics: Physics, Data Analysis, Statistics and Probability, Physics and Society

Source: http://arxiv.org/abs/1704.07599

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2.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Mario Chavez; Fanny Grosselin; Aurore Bussalb; Fabrizio De Vico Fallani; Xavier Navarro-Sune

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Objective: The recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this work, we propose a new data-driven algorithm to effectively...

Topics: Physics, Data Analysis, Statistics and Probability, Medical Physics

Source: http://arxiv.org/abs/1704.07603

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Avik Bhattacharya

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The angle of rotation of any target about the radar line of sight (LOS) is known as the polarization orientation angle. The orientation angle is found to be non-zero for undulating terrains and man-made targets oriented away from the radar LOS. This effect is more pronounced at lower frequencies (eg. L- and P- bands). The orientation angle shift is not only induced by azimuthal slope but also by range slope. This shift increases the cross-polarization (HV) intensity and subsequently, the...

Topics: Physics, Data Analysis, Statistics and Probability, Classical Physics

Source: http://arxiv.org/abs/1704.07372

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Saviz Mowlavi; Themistoklis P. Sapsis

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Stochastic dynamical systems with continuous symmetries arise commonly in nature and often give rise to coherent spatio-temporal patterns. However, because of their random locations, these patterns are not well captured by current order reduction techniques and a large number of modes is typically necessary for an accurate solution. In this work, we introduce a new methodology for efficient order reduction of such systems by combining (i) the method of slices, a symmetry reduction tool, with...

Topics: Physics, Fluid Dynamics, Data Analysis, Statistics and Probability, Dynamical Systems,...

Source: http://arxiv.org/abs/1704.06352

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Lucas Lacasa; Wolfram Just

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Visibility algorithms are a family of geometric and ordering criteria by which a real-valued time series of N data is mapped into a graph of N nodes. This graph has been shown to often inherit in its topology non-trivial properties of the series structure, and can thus be seen as a combinatorial representation of a dynamical system. Here we explore in some detail the relation between visibility graphs and symbolic dynamics. To do that, we consider the degree sequence of horizontal visibility...

Topics: Physics, Data Analysis, Statistics and Probability, Nonlinear Sciences, Dynamical Systems, Chaotic...

Source: http://arxiv.org/abs/1704.06467

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
D. Ratha; M. Surendar; A. Bhattacharya

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In this letter, a methodology is proposed to improve the scattering powers obtained from model-based decomposition using Polarimetric Synthetic Aperture Radar (PolSAR) data. The novelty of this approach lies in utilizing the intrinsic information in the off-diagonal elements of the 3$\times$3 coherency matrix $\mathbf{T}$ represented in the form of complex correlation coefficients. Two complex correlation coefficients are computed between co-polarization and cross-polarization components of the...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1704.06054

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4.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Daniel W. Drumm; Andrew D. Greentree

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Finding a fluorescent target in a biological environment is a common and pressing microscopy problem. This task is formally analogous to the canonical search problem. In ideal (noise-free, truthful) search problems, the well-known binary search is optimal. The case of half-lies, where one of two responses to a search query may be deceptive, introduces a richer, R\'enyi-Ulam problem and is particularly relevant to practical microscopy. We analyse microscopy in the contexts of R\'enyi-Ulam games...

Topics: Optics, Physics, Data Analysis, Statistics and Probability, Instrumentation and Detectors

Source: http://arxiv.org/abs/1704.05980

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2.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Maria Grazia Pia; Elisabetta Ronchieri

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We report an investigation of data analysis methods derived from other disciplines, which we applied to physics software systems. They concern the analysis of inequality, trend analysis and the analysis of diversity. The analysis of inequality exploits statistical methods originating from econometrics; trend analysis is typical of economics and environmental sciences; the analysis of diversity is based on concepts derived from ecology and treats software as an ecosystem. To the best of our...

Topics: Physics, Data Analysis, Statistics and Probability, Software Engineering, Computing Research...

Source: http://arxiv.org/abs/1704.05920

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Louis Lyons; Emilien Chapon

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We review the methods to combine several measurements, in the form of parameter values or $p$-values.

Topics: Physics, Data Analysis, Statistics and Probability, High Energy Physics - Experiment

Source: http://arxiv.org/abs/1704.05540

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Thomas D. P. Edwards; Christoph Weniger

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We present a toolbox of new techniques and concepts for the efficient forecasting of experimental sensitivities. These are applicable to a large range of scenarios in (astro-)particle physics, and based on the Fisher information formalism. Fisher information provides an answer to the question what is the maximum extractable information from a given observation?. It is a common tool for the forecasting of experimental sensitivities in many branches of science, but rarely used in astroparticle...

Topics: Physics, High Energy Physics - Phenomenology, Astrophysics, Data Analysis, Statistics and...

Source: http://arxiv.org/abs/1704.05458

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
A. Aliakbari; P. Manshour; M. J. Salehi

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The records statistics in stationary and non-stationary fractal time series is studied extensively. By calculating various concepts in record dynamics, we find some interesting results. In stationary fractional Gaussian noises, we observe a universal behavior for the whole range of Hurst exponents. However, for non-stationary fractional Brownian motions the record dynamics is crucially dependent on the memory, which plays the role of a non-stationarity index, here. Indeed, the deviation from...

Topics: Physics, Data Analysis, Statistics and Probability, Chaotic Dynamics, Nonlinear Sciences

Source: http://arxiv.org/abs/1704.04377

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Edward Higson; Will Handley; Mike Hobson; Anthony Lasenby

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We introduce dynamic nested sampling: a generalisation of the nested sampling algorithm in which the number of "live points" varies to allocate samples more efficiently. In empirical tests the new method increases accuracy by up to a factor of ~8 for parameter estimation and ~3 for evidence calculation compared to standard nested sampling with the same number of samples - equivalent to speeding up the computation by factors of ~64 and ~9 respectively. In addition unlike in standard...

Topics: Physics, Astrophysics, Data Analysis, Statistics and Probability, Computation, Statistics,...

Source: http://arxiv.org/abs/1704.03459

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Tatiana Barlyaeva; Julien Wojak; Philippe Lamy; Brice Boclet; Imre Toth

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We report on the parallel analysis of the periodic behaviour of coronal mass ejections (CMEs) based on 21 years [1996-2016] of observations with the SOHO/LASCO-C2 coronagraph, solar flares, prominences, and several proxies of solar activity. We consider values of the rates globally and whenever possible, distinguish solar hemispheres and solar cycles 23 and 24. Periodicities are investigated using both frequency (periodogram) and time-frequency (wavelet) analysis. We find that these different...

Topics: Physics, Solar and Stellar Astrophysics, Data Analysis, Statistics and Probability, Astrophysics,...

Source: http://arxiv.org/abs/1704.02336

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Joel Nishimura

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With sufficient time, double edge-swap Markov chain Monte Carlo (MCMC) methods are able to sample uniformly at random from many different and important graph spaces. For instance, for a fixed degree sequence, MCMC methods can sample any graph from: simple graphs; multigraphs (which may have multiedges); and pseudographs (which may have multiedges and/or multiple self-loops). In this note we extend these MCMC methods to `multiloop-graphs', which allow multiple self-loops but not multiedges and...

Topics: Physics, Data Analysis, Statistics and Probability, Combinatorics, Mathematics

Source: http://arxiv.org/abs/1704.01951

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Yingying Xu; Erik Aurell; Jukka Corander; Yoshiyuki Kabashima

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We consider the statistical properties of interaction parameter estimates obtained by the direct coupling analysis (DCA) approach to learning interactions from large data sets. Assuming that the data are generated from a random background distribution, we determine the distribution of inferred interactions. Two inference methods are considered: the L2 regularized naive mean-field inference procedure (regularized least squares, RLS), and the pseudo-likelihood maximization (plmDCA). For RLS we...

Topics: Physics, Data Analysis, Statistics and Probability, Disordered Systems and Neural Networks,...

Source: http://arxiv.org/abs/1704.01459

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Felipe Olivares; Luciano Zunino; Damián Gulich; Darío G. Pérez; Osvaldo A. Rosso

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We have experimentally quantified the temporal structural diversity from the coordinate fluctuations of a laser beam propagating through isotropic optical turbulence. The main focus here is on the characterization of the long-range correlations in the wandering of a thin Gaussian laser beam over a screen after propagating through a turbulent medium. To fulfill this goal, a laboratory-controlled experiment was conducted in which coordinate fluctuations of the laser beam were recorded at a...

Topics: Fluid Dynamics, Physics, Statistical Mechanics, Condensed Matter, Data Analysis, Statistics and...

Source: http://arxiv.org/abs/1704.01556

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2.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Joseph Lemley; Filip Jagodzinski; Razvan Andonie

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We present the first algorithm for finding holes in high dimensional data that runs in polynomial time with respect to the number of dimensions. Previous algorithms are exponential. Finding large empty rectangles or boxes in a set of points in 2D and 3D space has been well studied. Efficient algorithms exist to identify the empty regions in these low-dimensional spaces. Unfortunately such efficiency is lacking in higher dimensions where the problem has been shown to be NP-complete when the...

Topics: Computational Geometry, Physics, Data Analysis, Statistics and Probability, Computing Research...

Source: http://arxiv.org/abs/1704.00683

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Okpeafoh S. Agimelen; Vaclav Svoboda; Bilal Ahmed; Javier Cardona; Jerzy Dziewierz; Cameron J. Brown; Thomas McGlone; Alison Cleary; Christos Tachtatzis; Craig Michie; Alastair J. Florence; Ivan Andonovic; Anthony J. Mulholland; Jan Sefcik

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The success of the various secondary operations involved in the production of particulate products depends on the production of particles with a desired size and shape from a previous primary operation such as crystallisation. This is because these properties of size and shape affect the behaviour of the particles in the secondary processes. The size and the shape of the particles are very sensitive to the conditions of the crystallisation processes, and so control of these processes is...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.09186

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Shahriar Etemadi Tajbakhsh; Justin P. Coon; David E. Simmons

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In a wide range of complex networks, the links between the nodes are temporal and may sporadically appear and disappear. This temporality is fundamental to analyze the formation of paths within such networks. Moreover, the presence of the links between the nodes is a random process induced by nature in many real-world networks. In this paper, we study random temporal networks at a microscopic level and formulate the \emph{probability of accessibility} from a node \emph{i} to a node \emph{j}...

Topics: Physics, Data Analysis, Statistics and Probability, Computing Research Repository, Physics and...

Source: http://arxiv.org/abs/1703.09036

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Masamichi J. Miyama; Koji Hukushima

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A sparse modeling approach is proposed for analyzing scanning tunneling microscopy topography data, which contains numerous peaks corresponding to surface atoms. The method, based on the relevance vector machine with $\mathrm{L}_1$ regularization and $k$-means clustering, enables separation of the peaks and atomic center positioning with accuracy beyond the resolution of the measurement grid. The validity and efficiency of the proposed method are demonstrated using synthetic data in comparison...

Topics: Materials Science, Physics, Data Analysis, Statistics and Probability, Condensed Matter

Source: http://arxiv.org/abs/1703.08643

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Ramon Ferrer-i-Cancho; Carlos Gomez-Rodriguez; J. L. Esteban

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The syntactic structure of a sentence can be modelled as a tree, where vertices correspond to words and edges indicate syntactic dependencies. It has been claimed recurrently that the number of edge crossings in real sentences is small. However, a baseline or null hypothesis has been lacking. Here we quantify the amount of crossings of real sentences and compare it to the predictions of a series of baselines. We conclude that crossings are really scarce in real sentences. Their scarcity is...

Topics: Physics, Condensed Matter, Physics and Society, Data Analysis, Statistics and Probability,...

Source: http://arxiv.org/abs/1703.08324

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Iu. Skorodumina; G. V. Fedotov; V. D. Burkert; E. Golovach; R. W. Gothe; V. Mokeev

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The new event generator TWOPEG for the channel $e p \rightarrow e' p' \pi^{+} \pi^{-}$ has been developed. It uses an advanced method of event generation with weights and employs the five-fold differential structure functions from the recent versions of the JM model fit to all results on charged double pion photo- and electroproduction cross sections from CLAS (both published and preliminary). In the areas covered by measured CLAS data, TWOPEG successfully reproduces the available integrated...

Topics: Physics, Data Analysis, Statistics and Probability, Nuclear Experiment

Source: http://arxiv.org/abs/1703.08081

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
M. Andrecut

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The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the least-squares support vector machine classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that...

Topics: Physics, Learning, Data Analysis, Statistics and Probability, Computing Research Repository,...

Source: http://arxiv.org/abs/1703.07830

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3.0

Jun 30, 2018
06/18

Jun 30, 2018
by
James P. Bagrow

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The most critical time for information to spread is in the aftermath of a serious emergency, crisis, or disaster. Individuals affected by such situations can now turn to an array of communication channels, from mobile phone calls and text messages to social media posts, when alerting social ties. These channels drastically improve the speed of information in a time-sensitive event, and provide extant records of human dynamics during and afterward the event. Retrospective analysis of such...

Topics: Physics, Physics and Society, Data Analysis, Statistics and Probability, Computers and Society,...

Source: http://arxiv.org/abs/1703.07362

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2.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Mason A. Porter; Sam D. Howison

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Many problems in industry --- and in the social, natural, information, and medical sciences --- involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this article,...

Topics: Physics, Physics and Society, Adaptation and Self-Organizing Systems, Data Analysis, Statistics and...

Source: http://arxiv.org/abs/1703.06843

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Sophie A. Murray; Suzy Bingham; Michael Sharpe; David R. Jackson

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The Met Office Space Weather Operations Centre produces 24/7/365 space weather guidance, alerts, and forecasts to a wide range of government and commercial end users across the United Kingdom. Solar flare forecasts are one of its products, which are issued multiple times a day in two forms; forecasts for each active region on the solar disk over the next 24 hours, and full-disk forecasts for the next four days. Here the forecasting process is described in detail, as well as first verification...

Topics: Physics, Solar and Stellar Astrophysics, Data Analysis, Statistics and Probability, Astrophysics,...

Source: http://arxiv.org/abs/1703.06754

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Luca Perotti; Daniel Vrinceanu; Daniel Bessis

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We present a new method to locate the starting points in time of an arbitrary number of (damped) delayed signals. For a finite data sequence, the method permits to first locate the starting point of the component with the longest delay, and then --by iteration-- all the preceding ones. Numerical examples are given and noise sensitivity is tested for weak noise.

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.07001

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Javier M. Buldú; Mason A. Porter

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We explore how to study dynamical interactions between brain regions using functional multilayer networks whose layers represent the different frequency bands at which a brain operates. Specifically, we investigate the consequences of considering the brain as a multilayer network in which all brain regions can interact with each other at different frequency bands, instead of as a multiplex network, in which interactions between different frequency bands are only allowed within each brain region...

Topics: Physics, Neurons and Cognition, Data Analysis, Statistics and Probability, Combinatorics,...

Source: http://arxiv.org/abs/1703.06091

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Jakub Ślęzak; Aleksander Weron

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Modelling physical data with linear discrete time series, namely Fractionally Integrated Autoregressive Moving Average (ARFIMA), is a technique which achieved attention in recent years. However, these models are used mainly as a statistical tool only, with weak emphasis on physical background of the model. The main reason for this lack of attention is that ARFIMA model describes discrete-time measurements, whereas physical models are formulated using continuous-time parameter. In order to...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.06018

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Debajyoti Saha; Pankaj Kumar Shaw; Sabuj Ghosh; M. S. Janaki; A. N. Sekar Iyengar

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We have carried out a detailed study of scaling region using detrended fractal analysis test by applying different forcing likewise noise, sinusoidal, square on the floating potential fluctuations acquired under different pressures in a DC glow discharge plasma. The transition in the dynamics is observed through recurrence plot techniques which is an efficient method to observe the critical regime transitions in dynamics. The complexity of the nonlinear fluctuation has been revealed with the...

Topics: Plasma Physics, Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.06017

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Wagner S. de Lima; Emerson L. de Santa Helena

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q-Gaussian distribution appear in many science areas where we can find systems that could be described within a nonextensive framework. Usually, a way to assert that these systems belongs to nonextensive framework is by means of numerical data analysis. To this end, we implement random number generator for q-Gaussian distribution, while we present how to computing its probability density function, cumulative density function and quantile function besides a tail weight measurement using robust...

Topics: Physics, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.06172

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Andrew T. Sornborger; James D. Lauderdale

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Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, $C(\tau)$, as opposed to standard methods that decompose the time series, $\mathbf{X}(t)$, using only information at zero-lag. In both simulated and...

Topics: Physics, Quantitative Biology, Neurons and Cognition, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.05414

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Vladimir Klinshov; Serhiy Yanchuk; Artur Stephan; Vladimir Nekorkin

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Phase response curve (PRC) is an extremely useful tool for studying the response of oscillatory systems, e.g. neurons, to sparse or weak stimulation. Here we develop a framework for studying the response to a series of pulses which are frequent or/and strong so that the standard PRC fails. We show that in this case, the phase shift caused by each pulse depends on the history of several previous pulses. We call the corresponding function which measures this shift the phase response function...

Topics: Physics, Data Analysis, Statistics and Probability, Chaotic Dynamics, Nonlinear Sciences,...

Source: http://arxiv.org/abs/1703.05611

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
G. Inglese; R. Olmi; S. Priori

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Let $\Omega_\epsilon$ be a metallic plate whose top inaccessible surface has been damaged by some chemical or mechanical agent. We heat the opposite side and collect a sequence of temperature maps $u^\epsilon$. Here, we construct a formal explicit approximation of the damage $\epsilon\theta$ by solving a nonlinear inverse problem for the heat equation in three steps: (i) smoothing of temperature maps, (ii) domain derivative of the temperature, (iii) thin plate approximation of the model and...

Topics: Physics, Data Analysis, Statistics and Probability, Instrumentation and Detectors

Source: http://arxiv.org/abs/1703.04551

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0.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Nima Dehmamy; Neda Rohani; Aggelos Katsaggelos

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We show that in a deep neural network trained with ReLU, the low-lying layers should be replaceable with truncated linearly activated layers. We derive the gradient descent equations in this truncated linear model and demonstrate that --if the distribution of the training data is stationary during training-- the optimal choice for weights in these low-lying layers is the eigenvectors of the covariance matrix of the data. If the training data is random and uniform enough, these eigenvectors can...

Topics: Physics, Learning, Data Analysis, Statistics and Probability, Computing Research Repository,...

Source: http://arxiv.org/abs/1703.04757

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Chase Shimmin; Peter Sadowski; Pierre Baldi; Edison Weik; Daniel Whiteson; Edward Goul; Andreas Søgaard

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We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved discovery significance relative to existing taggers. The network is trained using an adversarial strategy, resulting in a tagger that learns to balance classification accuracy with...

Topics: Physics, High Energy Physics - Experiment, Data Analysis, Statistics and Probability, Machine...

Source: http://arxiv.org/abs/1703.03507

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1.0

Jun 30, 2018
06/18

Jun 30, 2018
by
Liang Sun; Rafael Aoude; Alberto Correa Dos Reis; Michael David Sokoloff

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The functionality of GooFit, a GPU-friendly framework for doing maximum-likelihood fits, has been extended to extract model-independent S-wave amplitudes in three-body decays such as $D^+ \to h^+h^+h^-$. A full amplitude analysis is done where the magnitudes and phases of the S-wave amplitudes are anchored at a finite number of $m^2(h^+h^-)$ control points, and a cubic spline is used to interpolate between these points. The amplitudes for P-wave and D-wave intermediate states are modeled as...

Topics: Physics, Data Analysis, Statistics and Probability, High Energy Physics - Experiment

Source: http://arxiv.org/abs/1703.03284

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Jun 30, 2018
06/18

Jun 30, 2018
by
Corentin Herbert; Freddy Bouchet

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Transitions between multiple stable states of nonlinear systems are ubiquitous in physics, chemistry, and beyond. Two types of behaviors are usually seen as mutually exclusive: unpredictable noise-induced transitions and predictable bifurcations of the underlying vector field. Here, we report a new situation, corresponding to a fluctuating system approaching a bifurcation, where both effects collaborate. We show that the problem can be reduced to a single control parameter governing the...

Topics: Physics, Statistical Mechanics, Condensed Matter, Data Analysis, Statistics and Probability,...

Source: http://arxiv.org/abs/1703.01450

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Jun 30, 2018
06/18

Jun 30, 2018
by
Mateusz Monterial; Peter Marleau; Shaun Clarke; Sara Pozzi

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A Bayesian approach is proposed for pulse shape discrimination of photons and neutrons in liquid organic scinitillators. Instead of drawing a decision boundary, each pulse is assigned a photon or neutron confidence probability. This allows for photon and neutron classification on an event-by-event basis. The sum of those confidence probabilities is used to estimate the number of photon and neutron instances in the data. An iterative scheme, similar to an expectation-maximization algorithm for...

Topics: Physics, Data Analysis, Statistics and Probability, Instrumentation and Detectors

Source: http://arxiv.org/abs/1703.00973

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Jun 30, 2018
06/18

Jun 30, 2018
by
Xin Xia; Teng Li; Xing-Tao Huang; Xue-Yao Zhang

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A fast physics analysis framework has been developed based on SNiPER to process the increasingly large data sample collected by BESIII. In this framework, a reconstructed event data model with SmartRef is designed to improve the speed of Input/Output operations, and necessary physics analysis tools are migrated from BOSS to SNiPER. A real physics analysis $e^{+}e^{-} \rightarrow \pi^{+}\pi^{-}J/\psi$ is used to test the new framework, and achieves a factor of 10.3 improvement in Input/Output...

Topics: Physics, High Energy Physics - Experiment, Data Analysis, Statistics and Probability

Source: http://arxiv.org/abs/1703.00131

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Jun 30, 2018
06/18

Jun 30, 2018
by
Florian Dufey

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A concise derivation of the "Joel equations", which allow for the determination of the axis angle 2V from measurements of extinction directions on a spindle stage, is provided starting from the wave-equation. Only analytic methods and no geometric arguments referring to stereographic projections are invoked. For error free data, the resulting equations allow for a closed form solution. If angle data with measurement error are to be used, a maximum likelihood estimation methodology is...

Topics: Physics, Data Analysis, Statistics and Probability, Computational Physics, Chemical Physics

Source: http://arxiv.org/abs/1703.00070

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Jun 30, 2018
06/18

Jun 30, 2018
by
D. M. Mateos; L. Riveaud; P. W. Lamberti

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Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide-band power spectrum, a delta-like autocorrelation function, and share other features as well. In general signals are presented as continuous records and require to be discretized for being analyzed. In this work we present...

Topics: Physics, Data Analysis, Statistics and Probability, Chaotic Dynamics, Nonlinear Sciences

Source: http://arxiv.org/abs/1702.08276

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Jun 30, 2018
06/18

Jun 30, 2018
by
E. B. Postnikov; A. A. Grinyuk; L. A. Kuzmichev; L. G. Sveshnikova

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This work is a methodical study of another option of the hybrid method originally aimed at gamma/hadron separation in the TAIGA experiment. In the present paper this technique was performed to distinguish between different mass groups of cosmic rays in the energy range 200 TeV - 500 TeV. The study was based on simulation data of TAIGA prototype and included analysis of geometrical form of images produced by different nuclei in the IACT simulation as well as shower core parameters reconstructed...

Topics: High Energy Astrophysical Phenomena, Physics, Data Analysis, Statistics and Probability,...

Source: http://arxiv.org/abs/1702.08302

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Jun 30, 2018
06/18

Jun 30, 2018
by
Caroline Chaux; Laurent Duval; Jean-Christophe Pesquet

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We propose a 2D generalization to the $M$-band case of the dual-tree decomposition structure (initially proposed by N. Kingsbury and further investigated by I. Selesnick) based on a Hilbert pair of wavelets. We particularly address (\textit{i}) the construction of the dual basis and (\textit{ii}) the resulting directional analysis. We also revisit the necessary pre-processing stage in the $M$-band case. While several reconstructions are possible because of the redundancy of the representation,...

Topics: Physics, Computer Vision and Pattern Recognition, Data Analysis, Statistics and Probability,...

Source: http://arxiv.org/abs/1702.08534

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Jun 30, 2018
06/18

Jun 30, 2018
by
Camille Couprie; Laurent Duval; Maxime Moreaud; Sophie Hénon; Mélinda Tebib; Vincent Souchon

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Comprehensive Two dimensional gas chromatography (GCxGC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templates of blobs are defined and superimposed on a reference chromatogram. The templates then need to be modified when different chromatograms are recorded. In this study, we present...

Topics: Physics, Data Analysis, Statistics and Probability, Computing Research Repository, Computer Vision...

Source: http://arxiv.org/abs/1702.07942

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Jun 30, 2018
06/18

Jun 30, 2018
by
A. Sh. M. Elshoukrofy; E. B. Postnikov; L. G. Sveshnikova

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A 'knee-like' approximation of Cherenkov light Lateral Distribution Functions, which we developed earlier, now is used for the actual tasks of background rejection methods for high energy (tens and hundreds of TeV) gamma-ray astronomy. In this work we implement this technique to the HiSCORE wide angle timing array consisting of Cherenkov light detectors with spacing of 100 m covering 0.2 km$^2$ presently and up to 5 km$^2$ in future. However, it can be applied to other similar arrays. We also...

Topics: High Energy Astrophysical Phenomena, Physics, Data Analysis, Statistics and Probability,...

Source: http://arxiv.org/abs/1702.07756

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Jun 30, 2018
06/18

Jun 30, 2018
by
Charlotte Revel; Yannick Deville; Véronique Achard; Xavier Briottet

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Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers) are the same in all the image for each class of materials. In the framework of remote sensing, such an assumption is no more valid in the presence of intra-class variabilities due to illumination conditions, weathering, slight variations of the pure materials, etc... In this paper, we first describe the results of...

Topics: Physics, Computer Vision and Pattern Recognition, Data Analysis, Statistics and Probability,...

Source: http://arxiv.org/abs/1702.07630