scholarly journals Effect of non-Gaussian noise sources in a noise-induced transition

2004 ◽  
Vol 193 (1-4) ◽  
pp. 161-168 ◽  
Author(s):  
Horacio S. Wio ◽  
Raúl Toral
Keyword(s):  
Gaussian Noise ◽  
Noise Sources ◽  
Non Gaussian

scholarly journals Characterizing transient noise in the LIGO detectors

2018 ◽  
Vol 376 (2120) ◽  
pp. 20170286 ◽  
Author(s):  
L. K. Nuttall
Keyword(s):  
Data Quality ◽  
Gravitational Wave ◽  
Environmental Conditions ◽  
Gaussian Noise ◽  
Noise Sources ◽  
Diverse Range ◽  
Non Gaussian ◽  
True Signal

Data from the LIGO detectors typically contain many non-Gaussian noise transients which arise due to instrumental and environmental conditions. These non-Gaussian transients can be an issue for the modelled and unmodelled transient gravitational-wave searches, as they can mask or mimic a true signal. Data quality can change quite rapidly, making it imperative to track and find new sources of transient noise so that data are minimally contaminated. Several examples of transient noise and the tools used to track them are presented. These instances serve to highlight the diverse range of noise sources present at the LIGO detectors during their second observing run. This article is part of a discussion meeting issue ‘The promises of gravitational-wave astronomy’.

THE SIGNAL TRACK SEARCH ALGORITHM

2000 ◽  
Vol 09 (03) ◽  
pp. 315-318 ◽  
Author(s):  
R. BALASUBRAMANIAN
Keyword(s):  
Data Analysis ◽  
Gravitational Waves ◽  
Gaussian Noise ◽  
Search Algorithm ◽  
Noise Sources ◽  
Time Frequency ◽  
Basic Strategy ◽  
Curvilinear Structures ◽  
Detector Output ◽  
Non Gaussian

In order to detect gravitational waves from partially modelled/unmodelled sources we will need to have in place a group of robust data analysis algorithms. We discuss here, a particular time-frequency strategy, namely the Signal Track Search (STS) strategy, which is both robust and sensitive for a certain class of signals buried in Gaussian noise. We assume that the signal is a a broadband burst containing several cycles. Our basic strategy involves the construction of time-frequency maps of the detector output and subsequently a search for curvilinear structures in the maps. Though this strategy has been found to work extremely well when the noise is Gaussian, it remains to be seen whether the strategy will be effective when the detector output is further contaminated by non Gaussian noise sources. In this article we will discuss various unresolved issues pertaining to the STS algorithm.

scholarly journals Non-Gaussian noise spectroscopy with a superconducting qubit sensor

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Youngkyu Sung ◽  
Félix Beaudoin ◽  
Leigh M. Norris ◽  
Fei Yan ◽  
David K. Kim ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Degrees Of Freedom ◽  
Quantum Control ◽  
Gaussian Approximation ◽  
Spectral Estimation ◽  
Superconducting Qubit ◽  
Major Step ◽  
Noise Sources ◽  
Noise Spectroscopy ◽  
Non Gaussian

Abstract Accurate characterization of the noise influencing a quantum system of interest has far-reaching implications across quantum science, ranging from microscopic modeling of decoherence dynamics to noise-optimized quantum control. While the assumption that noise obeys Gaussian statistics is commonly employed, noise is generically non-Gaussian in nature. In particular, the Gaussian approximation breaks down whenever a qubit is strongly coupled to discrete noise sources or has a non-linear response to the environmental degrees of freedom. Thus, in order to both scrutinize the applicability of the Gaussian assumption and capture distinctive non-Gaussian signatures, a tool for characterizing non-Gaussian noise is essential. Here, we experimentally validate a quantum control protocol which, in addition to the spectrum, reconstructs the leading higher-order spectrum of engineered non-Gaussian dephasing noise using a superconducting qubit as a sensor. This first experimental demonstration of non-Gaussian noise spectroscopy represents a major step toward demonstrating a complete spectral estimation toolbox for quantum devices.

STATEMENT OF THE PROBLEM ON SYNTHESIS OF OPTIMAL EVALUATION OF SIGNAL PARAMETERS IN NON-GAUSSIAN NOISE

2012 ◽  
Vol 71 (17) ◽  
pp. 1541-1555
Author(s):  
V. A. Baranov ◽  
S. V. Baranov ◽  
A. V. Nozdrachev ◽  
A. A. Rogov
Keyword(s):  
Gaussian Noise ◽  
Signal Parameters ◽  
Non Gaussian ◽  
Optimal Evaluation

NONPARAMETRIC DECODING OF BLOCK CODES IN CHANNELS WITH NON-GAUSSIAN NOISE

2013 ◽  
Vol 72 (11) ◽  
pp. 1029-1038
Author(s):  
M. Yu. Konyshev ◽  
S. V. Shinakov ◽  
A. V. Pankratov ◽  
S. V. Baranov
Keyword(s):  
Gaussian Noise ◽  
Block Codes ◽  
Non Gaussian

Wavelet threshold algorithm analysis under non-Gaussian noise background

2013 ◽  
Vol 32 (9) ◽  
pp. 2445-2447
Author(s):  
Qing-hua LI ◽  
Dalabaev Senbai ◽  
Xin-jian QIU ◽  
Chang LIAO ◽  
Quan-fu SUN
Keyword(s):  
Gaussian Noise ◽  
Algorithm Analysis ◽  
Noise Background ◽  
Threshold Algorithm ◽  
Non Gaussian
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