scholarly journals Consistent radial velocities of classical Cepheids from the cross-correlation technique

2019 ◽  
Vol 631 ◽  
pp. A37 ◽  
Author(s):  
S. Borgniet ◽  
P. Kervella ◽  
N. Nardetto ◽  
A. Gallenne ◽  
A. Mérand ◽  
...  
Keyword(s):  
Time Series ◽  
Line Profile ◽  
Cross Correlation ◽  
Radial Velocities ◽  
Spectral Lines ◽  
Computation Method ◽  
Custom Made ◽  
The Cross ◽  
The Impact ◽  
The Way

Context. Accurate radial velocities (vrad) of Cepheids are mandatory within the context of Cepheid distance measurements using the Baade-Wesselink technique. The most common vrad derivation method consists in cross-correlating the observed stellar spectra with a binary template and measuring a velocity on the resulting mean profile. Nevertheless, for Cepheids and other pulsating stars, the spectral lines selected within the template as well as the way of fitting the cross-correlation function (CCF) have a direct and significant impact on the measured vrad. Aims. Our first aim is to detail the steps to compute consistent CCFs and vrad of Cepheids. Next, this study aims at characterising the impact of Cepheid spectral properties and vrad computation methods on the resulting line profiles and vrad time series. Methods. We collected more than 3900 high-resolution spectra from seven different spectrographs of 64 Classical Milky Way (MW) Cepheids. These spectra were normalised and standardised using a single custom-made process on pre-defined wavelength ranges. We built six tailored correlation templates selecting unblended spectral lines of different depths based on a synthetic Cepheid spectrum, on three different wavelength ranges from 3900 to 8000 Å. Each observed spectrum was cross-correlated with these templates to build the corresponding CCFs, adopted as the proxy for the spectrum mean line profile. We derived a set of line profile observables as well as three different vrad measurements from each CCF and two custom proxies for the CCF quality and amount of signal. Results. This study presents a large catalogue of consistent Cepheid CCFs and vrad time series. It confirms that each step of the process has a significant impact on the deduced vrad: the wavelength, the template line depth and width, and the vrad computation method. The way towards more robust Cepheid vrad time series seems to go through steps that minimise the asymmetry of the line profile and its impact on the vrad. Centroid or first-moment vrad, that exhibit slightly smaller amplitudes but significantly smaller scatter than Gaussian or biGaussian vrad, should therefore be favoured. Stronger or deeper spectral lines also tend to be less asymmetric and lead to more robust vrad than weaker lines.

Time Series Cross Correlation Analysis among Pollution Source and Adjacent Marine Water Environmental Factors

2014 ◽  
Vol 522-524 ◽  
pp. 56-59
Author(s):  
Ming Chang Li ◽  
Ying Jie Zhao
Keyword(s):  
Time Series ◽  
Environmental Factors ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Pollution Source ◽  
Marine Water ◽  
Computation Method ◽  
Correlation Relationship ◽  
Cross Correlation Analysis ◽  
The Cross

The cross correlation analysis of pollution time series among pollution source and adjacent marine water environmental factors is an essential tool for obtaining the relationship in adjacent marine waters and the source of pollution. Meanwhile, the main pollution source should be obtained by this analysis. In this paper, the cross correlation of the dissolved inorganic nitrogen (DIN) in the Caofeidian marine district, the Beidaihe marine district, Tangshan Bay and the whole quantity of pollution in main rivers of Hebei Province is analyzed. The cross correlation coefficient computation method is used for the correlation. The research results show that the stronger correlation relationship exists between the pollution source and the Beidaihe marine district, owing to the influence of the Luanhe river.

scholarly journals Modeling Complex System Correlation Using Detrended Cross-Correlation Coefficient

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
Hong Zhang ◽  
You Gao
Keyword(s):  
Complex System ◽  
Time Series ◽  
Correlation Coefficient ◽  
Cross Correlation ◽  
Heavy Tails ◽  
Correlation Property ◽  
Traffic Time Series ◽  
Cross Correlations ◽  
The Cross ◽  
Active Research

The understanding of complex systems has become an area of active research for physicists because such systems exhibit interesting dynamical properties such as scale invariance, volatility correlation, heavy tails, and fractality. We here focus on traffic dynamic as an example of a complex system. By applying the detrended cross-correlation coefficient method to traffic time series, we find that the traffic fluctuation time series may exhibit cross-correlation characteristic. Further, we show that two traffic speed time series derived from adjacent sections exhibit much stronger cross-correlations than the two speed series derived from adjacent lanes. Similarly, we also demonstrate that the cross-correlation property between the traffic volume variables from two adjacent sections is stronger than the cross-correlation property between the volume variables of adjacent lanes.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

scholarly journals Investigations of Broad Line Region Structure and Kinematics Using Variability

1989 ◽  
Vol 134 ◽  
pp. 93-95
Author(s):  
C. Martin Gaskell ◽  
Anuradha P. Koratkar ◽  
Linda S. Sparke
Keyword(s):  
Time Series ◽  
Cross Correlation ◽  
Sampling Interval ◽  
Broad Line ◽  
Line Flux ◽  
Line Region ◽  
Cross Correlations ◽  
The Mean ◽  
The Cross ◽  
The Continuum

Gaskell and Sparke (1986) showed that one can determine the sizes of BLRs more accurately that the mean sampling interval by cross-correlating the continuum flux time series with a line flux time series. The position of the peak in the cross-correlation function (CCF) and its shape give an indication of the BLR size. The technique is explained in detail in Gaskell and Peterson (1987). The widely propagated misunderstanding is that the method involves simply interpolating both time series and cross-correlating them (in which case the CCF is dominated by the cross-correlations of “made-up” data). Actually the method involves cross correlating the observed points in one time series (continuum, say) with the linear interpolations of the other series (line flux). The line flux time series must always be smoother than the continuum time series it is derived from. We have usually employed the method with the interpolation done both ways round and averaged them (to reduce errors due to the interpolation) and we can intercompare the two results (to investigate errors).

Multiscale Multifractal Detrended Cross-Correlation Analysis of High-Frequency Financial Time Series

2019 ◽  
Vol 18 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Jingjing Huang ◽  
Danlei Gu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
High Frequency ◽  
Cross Correlation ◽  
Financial Time Series ◽  
Chinese Market ◽  
Change Of Scale ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross

In order to obtain richer information on the cross-correlation properties between two time series, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). This method is based on the Hurst surface and can be used to study the non-linear relationship between two time series. By sweeping through all the scale ranges of the multifractal structure of the complex system, it can present more information than the multifractal detrended cross-correlation analysis (MF-DCCA). In this paper, we use the MM-DCCA method to study the cross-correlations between two sets of artificial data and two sets of 5[Formula: see text]min high-frequency stock data from home and abroad. They are SZSE and SSEC in the Chinese market, and DJI and NASDAQ in the US market. We use Hurst surface and Hurst exponential distribution histogram to analyze the research objects and find that SSEC, SZSE and DJI, NASDAQ all show multifractal properties and long-range cross-correlations. We find that the fluctuation of the Hurst surface is related to the positive and negative of [Formula: see text], the change of scale range, the difference of national system, and the length of time series. The results show that the MM-DCCA method can give more abundant information and more detailed dynamic processes.

The Impact of Wavelength Step on Radial Velocity Error

2012 ◽  
Vol 239-240 ◽  
pp. 794-797
Author(s):  
Fang Zuo ◽  
A Li Luo
Keyword(s):  
Radial Velocity ◽  
Measurement Method ◽  
Cross Correlation ◽  
Stellar Spectra ◽  
Velocity Error ◽  
Target Spectrum ◽  
Different Types ◽  
The Cross ◽  
The Impact ◽  
Internal Error

Radial velocity (RV) of a star caused by Doppler shift could be easily measured by cross-correlated it’s spectrum with a serial template. Generally, the RV error of a spectrum always is given by the widths of the cross-correlation function of the target spectrum and related template spectrum. RV error comes from many factors, causing from instruments, observation weather, etc.. In this paper, simulation with different types of stellar spectra, which has different temperature, is implemented. The results show that there is an internal error which is not generated by the calculation steps for a given resolution step, for example, 5km/s error exists in the cross-correlation based RV measurement method for resolution about R~2000. The simulation also proves that this error could not be avoided for any type of stars.

GPS Cross Correlation Mitigation Technique in Multipath Environment

2010 ◽  
Vol 439-440 ◽  
pp. 715-720
Author(s):  
Jun Yao Gao ◽  
Jing Shu Yang ◽  
Jia Zhao
Keyword(s):  
Interference Cancellation ◽  
Cross Correlation ◽  
Parallel Interference Cancellation ◽  
Multipath Environment ◽  
Simulation Results ◽  
The Cross ◽  
The Impact ◽  
Mitigation Technique

The paper investigates the cross correlation mitigation (CCM) technique in the multipath propagating environment. It analyses the impact of cross correlation firstly, then expounds the universal technique in CCM, at last presents an improved DPIC (Delayed Parallel Interference Cancellation) method based on MEDLL. The algorithm estimates the parameters of multipath by using MEDLL, reconstructs the strong signals IF utilizing these parameters, and mitigates the cross correlation with the aid of DPIC method. At last, simulation results prove the validity of this method.

scholarly journals Adaptive seismic noise reduction using Wiener filter

2019 ◽  
Vol 8 (1) ◽  
pp. 12-20
Author(s):  
Sesar Prabu Dwi Sriyanto
Keyword(s):  
Seismic Noise ◽  
Cross Correlation ◽  
Seismic Station ◽  
Wiener Filter ◽  
Dominant Frequency ◽  
Observation System ◽  
Before And After ◽  
Filter Process ◽  
The Cross ◽  
The Impact

Seismic noise disrupts the earthquake observation system due to the frequency and amplitude of seismic noise similar to the earthquake signal. The filter process is one of the methods that can be used to reduce seismic noise. In this study, the Wiener filter algorithm was designed with the Decision-Directed method for Apriori SNR estimation. This filter was chosen because it is adaptive, so it can adjust to environmental conditions without requiring manual parameter settings. The data used are earthquake signals that occur in the Palu area, Central Sulawesi, which are recorded on PKA29 temporary seismic station from February 3 to April 28, 2015. After each signal data has been filtered, then it is evaluated by calculating SNR differences before and after filtering, the signal's dominant frequency, and the cross-correlation of the signal before and after filtering. As a result, the Wiener filter is able to reduce the noise content in earthquake signals according to noisy frequencies before earthquake signals. The impact is that SNR has increased with an average of 8.056 dB. In addition, this filter is also able to maintain the shape of earthquake signals. This is indicated by the normalization value of the cross-correlation between signals before and after the filter which ranges from 0.703 to 1.00.

scholarly journals Synchronization in Monkey Motor Cortex During a Precision Grip Task. I. Task-Dependent Modulation in Single-Unit Synchrony

2001 ◽  
Vol 85 (2) ◽  
pp. 869-885 ◽  
Author(s):  
S. N. Baker ◽  
R. Spinks ◽  
A. Jackson ◽  
R. N. Lemon
Keyword(s):  
Motor Cortex ◽  
Firing Rate ◽  
Cross Correlation ◽  
Primary Motor Cortex ◽  
Precision Grip ◽  
Average Width ◽  
Population Synchrony ◽  
Hold Period ◽  
The Cross ◽  
The Impact

Neural synchronization in the cortex, and its potential role in information coding, has attracted much recent attention. In this study, we have recorded long spike trains (mean, 33,000 spikes) simultaneously from multiple single neurons in the primary motor cortex (M1) of two conscious macaque monkeys performing a precision grip task. The task required the monkey to use its index finger and thumb to move two spring-loaded levers into a target, hold them there for 1 s, and release for a food reward. Synchrony was analyzed using a time-resolved cross-correlation method, normalized using an estimate of the instantaneous firing rate of the cell. This was shown to be more reliable than methods using trial-averaged firing rate. A total of 375 neurons was recorded from the M1 hand area; 235 were identified as pyramidal tract neurons. Synchrony was weak [mean k′ = 1.05 ± 0.04 (SD)] but widespread among pairs of M1 neurons (218/1359 pairs with above-chance synchrony), including output neurons. Synchrony usually took the form of a broad central peak [average width, 18.7 ± 8.7 (SD) ms]. There were marked changes during different phases of the task. As a population, synchrony was greatest during the steady hold period in striking contrast to the averaged cell firing rate, which was maximal when the animal was moving the levers into target. However, the modulation of synchrony during task performance showed considerable variation across individual cell pairs. Two types of synchrony were identified: oscillatory (with periodic side lobes in the cross-correlation) and nonoscillatory. Their relative contributions were quantified by filtering the cross-correlations to exclude either frequencies from 18 to 37 Hz or all higher and lower frequencies. At the peak of population synchrony during the hold period, about half (51.7% in one monkey, 56.2% in the other) of the synchronization was within this oscillatory bandwidth. This study provides strong support for assemblies of neurons being synchronized during specific phases of a complex task with potentially important consequences for both information processing within M1 and for the impact of M1 commands on target motoneurons.

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