scholarly journals Searching for Stochastic Background of Ultra-Light Fields with Atomic Sensors

Universe ◽  
2018 ◽  
Vol 4 (10) ◽  
pp. 99
Author(s):  
Tigran Kalaydzhyan ◽  
Nan Yu
Keyword(s):  
Cross Correlation ◽  
Correlation Method ◽  
Light Fields ◽  
Observation Data ◽  
Main Motivation ◽  
Cross Spectrum ◽  
Space Network ◽  
Stochastic Background ◽  
The Cross ◽  
Atomic Sensors

We propose a cross-correlation method for the searches of ultra-light fields, in particular, with a space network of atomic sensors. The main motivation of the approach is cancellation of uncorrelated noises in the observation data and unique pattern the fields leave on the cross-spectrum, depending on their nature (i.e., scalar, vector or tensor). In particular, we analytically derive a dependence of the cross-spectrum on the angle between two pairs of detectors. We then confirm obtained angular curves with a numerical simulation. We apply the method to the detection of dark matter and gravitational waves.

The Cross Correlation Method: A Useful Tool for Peak Shift Determination in XSE

1996 ◽  
Vol 228-231 ◽  
pp. 73-76
Author(s):  
V.R. Vosberg ◽  
W. Fischer ◽  
W.Joe Quadakkers
Keyword(s):  
Cross Correlation ◽  
Correlation Method ◽  
Peak Shift ◽  
The Cross

scholarly journals Implementation of a Cross-Spectrum FFT Analyzer for a Phase-Noise Test System in a Low-Cost FPGA

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Patrick Fleischmann ◽  
Heinz Mathis ◽  
Jakub Kucera ◽  
Stefan Dahinden
Keyword(s):  
Phase Noise ◽  
Cross Correlation ◽  
Dynamic Range ◽  
Low Cost ◽  
Test System ◽  
Low Noise ◽  
Noise Levels ◽  
High Quality ◽  
Cross Spectrum ◽  
The Cross

The cross-correlation method allows phase-noise measurements of high-quality devices with very low noise levels, using reference sources with higher noise levels than the device under test. To implement this method, a phase-noise analyzer needs to compute the cross-spectral density, that is, the Fourier transform of the cross-correlation, of two time series over a wide frequency range, from fractions of Hz to tens of MHz. Furthermore, the analyzer requires a high dynamic range to accommodate the phase noise of high-quality oscillators that may fall off by more than 100 dB from close-in noise to the noise floor at large frequency offsets. This paper describes the efficient implementation of a cross-spectrum analyzer in a low-cost FPGA, as part of a modern phase-noise analyzer with very fast measurement time.

Comparative Study of Established DPIV Algorithms for Planar Velocity Measurements

2002 ◽  
Author(s):  
Claude Abiven ◽  
Pavlos P. Vlachos ◽  
George Papadopoulos
Keyword(s):  
Cross Correlation ◽  
Correlation Method ◽  
Planar Velocity ◽  
Evaluation Scheme ◽  
Estimation Scheme ◽  
Centroid Detection ◽  
The Cross ◽  
Multi Phase ◽  
Processing Techniques ◽  
Gaussian Estimation

This paper represents a continuation of our effort to develop a velocity evaluation scheme optimized to resolve multiphase flows. An improved adaptive hybrid scheme that integrates the dynamically adaptive cross-correlation method with a particle tracking velocimetry algorithm is developed, presented and evaluated in this paper. A detailed description of the methodology, error analysis using Monte-Carlo simulations and elaborate comparisons with established schemes and robust commercial packages are presented. Improvements were guided towards increased accuracy for resolving vortical and poly-dispersed multi-phase flows. We introduce a novel iterative scheme that localizes the cross-correlation. We incorporate state of the art elaborate image processing techniques that allow increased particle densities. A new particle pairing method based on an adaptive cross-correlation masking is introduced. Finally, a refined gaussian estimation scheme that involves only four non-saturated pixels for the particle centroid detection is proposed. Overall, the dynamically adaptive hybrid velocity evaluation scheme presented here allows superior resolution of high velocity gradients, minimizes the loss of the rotational motion of the particles, and eliminates the spatial averaging effects inherent from the cross-correlation.

scholarly journals Fluctuation Characteristics of Water Level and Water Temperature of Huize Well Based on MF-DCCA

2021 ◽  
Vol 39 (3) ◽  
pp. 825-832
Author(s):  
Jian Yu ◽  
Lili Sui ◽  
Yirong Xu ◽  
Baoming Chi
Keyword(s):  
Time Series ◽  
Water Temperature ◽  
Water Level ◽  
Cross Correlation ◽  
Correlation Method ◽  
Multifractal Spectrum ◽  
Earthquake Precursor ◽  
Observation Data ◽  
Cross Correlation Analysis ◽  
Abnormal Increase

In recent decades, the network of seismic subsurface fluid observatories is developing constantly, the observation data of subsurface fluids are enriched accordingly, which provides a favorable condition for the research on the formation, occurrence, and development of earthquakes. In the observation data of subsurface fluids, water level and water temperature changes are very important observation indicators, and their fluctuation sequences are quite complicated. Therefore, this paper employed a non-linear cross-correlation method to study the relationship between the water level and water temperature of Huize Well from 2004 to 2006, and found that there’s a significant cross-correlation between the time series of water level and water temperature; then, this study adopted DCCA (detrended cross-correlation analysis) to calculate the cross-correlation coefficient under different scales and explore the continuous changes of water level and water temperature; at last, this paper used the MF-DCCA (Multifractal-DCCA) method to prove that there’s multifractal cross-correlation between the time series of water level and water temperature. Before the M5.1 earthquake in Huize area, there’s an abnormal increase in the width of the multifractal spectrum of the water level and water temperature drawn with a sliding window of 500-hour, and this is a possible earthquake precursor.

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