scholarly journals Comparison of Terrestrial and Lunar Time Scales by Giant Pulsar Impulses

2021 ◽  
Vol 65 (11) ◽  
pp. 1136-1144
Author(s):  
A. E. Rodin ◽  
V. V. Oreshko ◽  
V. A. Fedorova
Keyword(s):  
Time Scales ◽  
Cross Correlation ◽  
Arrival Time ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
The Moon ◽  
Recording Equipment ◽  
Giant Pulses ◽  
Cross Correlation Analysis ◽  
Pulse Arrival

Abstract We have developed a model for the time delay of pulse arrival between stations on the Moon and Earth. Comparison of the lunar and terrestrial time scales is proposed to be carried out by comparing the arrival time moments of giant pulses from pulsars. A method for such a comparison has been developed based on the cross-correlation analysis of the received pulses. Using the example of giant pulses from the pulsar PSR 0531+21, we showed that the error of comparing scales in the case of a high signal-to-noise ratio reaches a sub-discrete level and, thus, is determined by the reception band of the recording equipment.

scholarly journals Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

2019 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Natália Costa ◽  
César Silva ◽  
Paulo Ferreira
Keyword(s):  
Time Scales ◽  
Cross Correlation ◽  
Data Availability ◽  
Fluctuation Analysis ◽  
Financial Assets ◽  
Sliding Windows ◽  
Cross Correlation Analysis ◽  
Short Time ◽  
Detrended Fluctuation ◽  
Great Development

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

Long-range hydroacoustic observations of the Monowai Volcanic Centre as a proxy for seasonal variations in sound propagation

2020 ◽  
Author(s):  
Pieter Smets ◽  
Kees Weemstra ◽  
Läslo Evers
Keyword(s):  
Volcanic Activity ◽  
Seasonal Variations ◽  
Cross Correlation ◽  
Sound Propagation ◽  
Signal To Noise Ratio ◽  
Seismic Station ◽  
Volcanic Centre ◽  
Processing Scheme ◽  
Cook Island ◽  
Cross Correlation Analysis

<p>Hydroacoustic activity of the submarine Monowai Volcanic Centre (MVC) is repeatedly observed at two distant triplet hydrophone stations, south of Juan Fernandez Islands (H03S, 9,159km) and north of Ascension Island (H10N, 15,823km). <em>T</em>-phase converted energy recorded at the broadband seismic station Rarotonga on Cook Island (RAR, 1,845km) is used as a reference for the cross-correlation analysis. A detailed processing scheme for the calculation of the daily cross-correlation functions (CCF) of the hydroacoustic and seismic data is provided. Preprocessing is essential to account for the non-identical measurements and sensitivities as well as the different sample rates.<span> </span>Further postprocessing by systematic data selection has to be applied before stacking CCFs in order to account for the non-continuous activity of the MVC source.<span> </span>Daily volcanic activity is determined for the period from 2006 until 2018 using the signal-to-noise ratio of the CCFs assuming sound propagation in the SOFAR channel. Monthly stacked CCFs with clear volcanic activity are used to study seasonal variations in sound propagation between the MVC and the hydrophone stations.<span> </span>In winter, however, a faster than expected signal is observed at H10N which is hypothesized to (partial) propagation through the formed sea ice along the path near Antarctica.</p>

scholarly journals A review and appraisal of arrival-time picking methods for downhole microseismic data

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. KS71-KS91 ◽  
Author(s):  
Jubran Akram ◽  
David W. Eaton
Keyword(s):  
Arrival Time ◽  
Hybrid Methods ◽  
Optimal Parameter ◽  
Signal To Noise Ratio ◽  
Information Criterion ◽  
High Signal ◽  
Data Set ◽  
Single Level ◽  
Microseismic Data ◽  
Arrival Time Picking

We have evaluated arrival-time picking algorithms for downhole microseismic data. The picking algorithms that we considered may be classified as window-based single-level methods (e.g., energy-ratio [ER] methods), nonwindow-based single-level methods (e.g., Akaike information criterion), multilevel- or array-based methods (e.g., crosscorrelation approaches), and hybrid methods that combine a number of single-level methods (e.g., Akazawa’s method). We have determined the key parameters for each algorithm and developed recommendations for optimal parameter selection based on our analysis and experience. We evaluated the performance of these algorithms with the use of field examples from a downhole microseismic data set recorded in western Canada as well as with pseudo-synthetic microseismic data generated by adding 100 realizations of Gaussian noise to high signal-to-noise ratio microseismic waveforms. ER-based algorithms were found to be more efficient in terms of computational speed and were therefore recommended for real-time microseismic data processing. Based on the performance on pseudo-synthetic and field data sets, we found statistical, hybrid, and multilevel crosscorrelation methods to be more efficient in terms of accuracy and precision. Pick errors for S-waves are reduced significantly when data are preconditioned by applying a transformation into ray-centered coordinates.

scholarly journals Li abundances and chromospheric activity of BY Dra type stars

2009 ◽  
Vol 5 (S268) ◽  
pp. 343-344
Author(s):  
Tamara V. Mishenina ◽  
Caroline Soubiran ◽  
Valery V. Kovtyukh ◽  
Stanislav I. Belik
Keyword(s):  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Large Fraction ◽  
Ratio Method ◽  
Clear Correlation ◽  
High Signal ◽  
Echelle Spectrograph ◽  
Active Stars ◽  
Ionization Balance ◽  
Line Depth

AbstractAtmospheric parameters and Li abundances have been determined for 162 stars observed at high resolution, high signal to noise ratio with the ELODIE echelle spectrograph (OHP, France). Among them, about 70 stars are active stars with a large fraction of BY Dra type stars. For all stars, rotational velocities were obtained with a calibration of the cross-correlation function, effective temperatures by the line depth ratio method, surface gravities by the parallaxe method and by the ionization balance of iron. The frequency of stars with observed lithium is significantly higher in active stars than in non active stars. Among active stars, no clear correlation has been found between different indicators of activity for our sample stars, but some correlation of an index R′H K and vsini is observed.

scholarly journals Theoretical characterisation of strand cross-correlation in ChIP-seq

2020 ◽  
Author(s):  
Hayato Anzawa ◽  
Hitoshi Yamagata ◽  
Kengo Kinoshita
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Cross Correlation ◽  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Peak Calling ◽  
The Public ◽  
Cross Correlation Analysis ◽  
Maximum Coefficient ◽  
High Consistency

Abstract Background: Strand cross-correlation profiles are used for both peak calling pre-analysis and quality control (QC) in chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis. Despite its potential for robust and accurate assessments of signal-to-noise ratio (S/N) because of its peak calling independence, it remains unclear what aspects of quality such strand cross-correlation profiles actually measure. Results: We introduced a simple model to simulate the mapped read-density of ChIP-seq and then derived the theoretical maximum and minimum of cross-correlation coefficients between strands. The results suggest that the maximum coefficient of typical ChIP-seq samples is directly proportional to the number of total mapped reads and the square of the ratio of signal reads, and inversely proportional to the number of peaks and the length of read-enriched regions. Simulation analysis supported our results and evaluation using 790 ChIP-seq data obtained from the public database demonstrated high consistency between calculated cross-correlation coefficients and estimated coefficients based on the theoretical relations and peak calling results. In addition, we found that the mappability-bias-correction improved sensitivity, enabling differentiation of maximum coefficients from the noise level. Based on these insights, we proposed virtual S/N (VSN), a novel peak call-free metric for S/N assessment. We also developed PyMaSC, a tool to calculate strand cross-correlation and VSN efficiently. VSN achieved most consistent S/N estimation for various ChIP targets and sequencing read depths. Furthermore, we demonstrated that a combination of VSN and pre-existing peak calling results enable the estimation of the numbers of detectable peaks for posterior experiments and assess peak calling results. Conclusions: We present the first theoretical insights into the strand cross-correlation, and the results reveal the potential and the limitations of strand cross-correlation analysis. Our quality assessment framework using VSN provides peak call-independent QC and will help in the evaluation of peak call analysis in ChIP-seq experiments.

scholarly journals Theoretical characterisation of strand cross-correlation in ChIP-seq

2019 ◽  
Author(s):  
Hayato Anzawa ◽  
Hitoshi Yamagata ◽  
Kengo Kinoshita
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Cross Correlation ◽  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Peak Calling ◽  
The Public ◽  
Cross Correlation Analysis ◽  
Maximum Coefficient ◽  
High Consistency

Abstract Background: Strand cross-correlation profiles are used for both peak calling pre-analysis and quality control in chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis. Despite its potential for robust and accurate assessments of signal-to-noise ratio (S/N) of ChIP-seq samples, it remains unclear what aspects of quality such strand cross-correlation profiles actually measure. Results: We introduced a simple model to simulate the mapped read-density of ChIP-seq and then derived the theoretical maximum and minimum of cross-correlation coefficients between strands. The results suggest that the maximum coefficient of typical ChIP-seq samples is directly proportional to the number of total mapped reads and the square of the ratio of signal reads, and inversely proportional to the number of peaks and the length of read-enriched regions. We also developed PyMaSC to efficiently generate strand cross-correlation profiles. Simulation analysis supported our results and evaluation using 790 ChIP-seq data obtained from the public database demonstrated high consistency between calculated cross-correlation coefficients and estimated coefficients based on the theoretical relations and peak calling results. In addition, we found that the mappability-bias-correction improved sensitivity, enabling differentiation of maximum coefficients from the noise level. Conclusions: We present the first theoretical insights into the strand cross-correlation and the results reveal the potential and the limitations of strand cross-correlation analysis. Our work will help in the establishment of better QC metrics using strand cross-correlation.

scholarly journals Theoretical characterisation of strand cross-correlation in ChIP-seq

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Hayato Anzawa ◽  
Hitoshi Yamagata ◽  
Kengo Kinoshita
Keyword(s):  
Chromatin Immunoprecipitation ◽  
Cross Correlation ◽  
Simulation Analysis ◽  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Peak Calling ◽  
The Public ◽  
Cross Correlation Analysis ◽  
Maximum Coefficient ◽  
High Consistency

Abstract Background Strand cross-correlation profiles are used for both peak calling pre-analysis and quality control (QC) in chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis. Despite its potential for robust and accurate assessments of signal-to-noise ratio (S/N) because of its peak calling independence, it remains unclear what aspects of quality such strand cross-correlation profiles actually measure. Results We introduced a simple model to simulate the mapped read-density of ChIP-seq and then derived the theoretical maximum and minimum of cross-correlation coefficients between strands. The results suggest that the maximum coefficient of typical ChIP-seq samples is directly proportional to the number of total mapped reads and the square of the ratio of signal reads, and inversely proportional to the number of peaks and the length of read-enriched regions. Simulation analysis supported our results and evaluation using 790 ChIP-seq data obtained from the public database demonstrated high consistency between calculated cross-correlation coefficients and estimated coefficients based on the theoretical relations and peak calling results. In addition, we found that the mappability-bias-correction improved sensitivity, enabling differentiation of maximum coefficients from the noise level. Based on these insights, we proposed virtual S/N (VSN), a novel peak call-free metric for S/N assessment. We also developed PyMaSC, a tool to calculate strand cross-correlation and VSN efficiently. VSN achieved most consistent S/N estimation for various ChIP targets and sequencing read depths. Furthermore, we demonstrated that a combination of VSN and pre-existing peak calling results enable the estimation of the numbers of detectable peaks for posterior experiments and assess peak calling results. Conclusions We present the first theoretical insights into the strand cross-correlation, and the results reveal the potential and the limitations of strand cross-correlation analysis. Our quality assessment framework using VSN provides peak call-independent QC and will help in the evaluation of peak call analysis in ChIP-seq experiments.

Arrival-time picking uncertainty: Theoretical estimations and their application to microseismic data

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. U65-U76
Author(s):  
Ivan Abakumov ◽  
Aurelian Roeser ◽  
Serge A. Shapiro
Keyword(s):  
Seismic Waves ◽  
Arrival Time ◽  
Signal To Noise Ratio ◽  
Estimation Error ◽  
Additive White Gaussian Noise ◽  
Accurate Determination ◽  
Microseismic Monitoring ◽  
High Signal ◽  
Arrival Times ◽  
Velocity Models

Traveltime-based methods depend on the accurate determination of the arrival times of seismic waves. They further benefit from information on the uncertainty with which the arrival times are determined. Among other applications, arrival-time uncertainties are used to weight data in inversion algorithms and to define the resolution of reconstructed velocity models. The most physically meaningful approaches for the estimation of arrival-time uncertainties are based on probabilistic formulations. The two approaches for the assessment of the lower bound of arrival-time uncertainties, the Cramér–Rao Bound (CRB) and the Ziv–Zakai Bound (ZZB), have been reviewed. The CRB determines the minimum-achievable estimation error under the assumption of a high signal-to-noise ratio (S/N) but underestimates said error for small S/N. The ZZB provides a better result for noisy data because it utilizes a priori information. The CRB and ZZB require knowledge of the spectral variance of the signal, which often is hard to determine in seismic experiments. Furthermore, both bounds assume additive white Gaussian noise (AWGN), which does not hold for seismic data. To overcome these problems, alternative expressions have been proposed, which yield comparable estimates as CRB and ZZB but are solely based on the S/N and the dominant period in the data. Moreover, a recipe to correct the S/N and account for the difference between the seismic noise and AWGN has been provided. For a case study of downhole microseismic monitoring, it is determined that the new expressions provide station-dependent arrival-time uncertainties, which are used as weights to improve source location uncertainties.

scholarly journals Multiple stellar systems

1986 ◽  
Vol 118 ◽  
pp. 441-442
Author(s):  
A. Duquennoy ◽  
M. Mayor
Keyword(s):  
Radial Velocity ◽  
Small Amplitude ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Correlation Technique ◽  
Multiple Systems ◽  
Triple Systems ◽  
The Cross

A spectroscopic survey of visual binaries with known orbital elements has been carried out with the radial velocity scanner CORAVEL at the Haute-Provence Observatory, since 1977, (Baranne, Mayor, Poncet, 1979). This survey of more than 100 visual systems, selected from Dommanget's catalogue (1967) (see also a new edition 1982) was first devoted to the determination of stellar masses. Several multiple systems were detected and have permitted also a study of the structure of triple systems. We have detected and measured in particular a class of triple systems with radial velocity variations of small amplitude. Taking advantage of the high resolution and high signal-to-noise ratio accessible with the cross-correlation technique, such small amplitude radial velocity curves are sometimes derived only through the change of width and shape of the cross-correlation function. Let us recall that the cc-function of a SB2 (or SB3) system is only the weighted sum of the individual cc-functions (Mayor, 1985). This property of the cross-correlation combined with the linearity of the detector allow a very simple analysis of blended dips. The full width at half depth of the cross-correlation dip is about FWHD = 16 km/s (in absence of noticeable rotation). Analysis of blended systems allows a good determination of the two individual velocities if the difference |vr1 -Vr2| is equal or larger than about 0.15 *FWHD (about 2 km/s).

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