A robust and coherent network statistic for detecting gravitational waves from inspiralling compact binaries in non-Gaussian noise

2002 ◽  
Vol 19 (7) ◽  
pp. 1437-1442 ◽  
Author(s):  
Sukanta Bose
Keyword(s):  
Gravitational Waves ◽  
Gaussian Noise ◽  
Compact Binaries ◽  
Non Gaussian

scholarly journals Robust statistics for deterministic and stochastic gravitational waves in non-Gaussian noise: Frequentist analyses

2002 ◽  
Vol 65 (12) ◽  
Author(s):  
Bruce Allen ◽  
Jolien D. E. Creighton ◽  
Éanna É. Flanagan ◽  
Joseph D. Romano
Keyword(s):  
Gravitational Waves ◽  
Gaussian Noise ◽  
Robust Statistics ◽  
Non Gaussian

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 Robust statistics for deterministic and stochastic gravitational waves in non-Gaussian noise. II. Bayesian analyses

2003 ◽  
Vol 67 (12) ◽  
Author(s):  
Bruce Allen ◽  
Jolien D. E. Creighton ◽  
Éanna É. Flanagan ◽  
Joseph D. Romano
Keyword(s):  
Gravitational Waves ◽  
Gaussian Noise ◽  
Robust Statistics ◽  
Bayesian Analyses ◽  
Non Gaussian

scholarly journals Detecting transient gravitational waves in non-Gaussian noise with partially redundant analysis methods

2012 ◽  
Vol 85 (12) ◽  
Author(s):  
Rahul Biswas ◽  
Patrick R. Brady ◽  
Jordi Burguet-Castell ◽  
Kipp Cannon ◽  
Jessica Clayton ◽  
...  
Keyword(s):  
Gravitational Waves ◽  
Gaussian Noise ◽  
Analysis Methods ◽  
Non Gaussian

scholarly journals Detecting gravitational waves in data with non-stationary and non-Gaussian noise

2021 ◽  
Vol 104 (6) ◽  
Author(s):  
Barak Zackay ◽  
Tejaswi Venumadhav ◽  
Javier Roulet ◽  
Liang Dai ◽  
Matias Zaldarriaga
Keyword(s):  
Gravitational Waves ◽  
Gaussian Noise ◽  
Non Gaussian

SEARCHING FOR GRAVITATIONAL WAVES FROM INSPIRALING COMPACT BINARIES USING TAMA300 DATA

2000 ◽  
Vol 09 (03) ◽  
pp. 319-323 ◽  
Author(s):  
◽  
HIDEYUKI TAGOSHI ◽  
TAKAHIRO TANAKA ◽  
NOBUYUKI KANDA ◽  
DAISUKE TATSUMI ◽  
...  
Keyword(s):  
Gravitational Waves ◽  
Preliminary Analysis ◽  
Search Strategy ◽  
Computation Time ◽  
Continuous Operation ◽  
Matched Filtering ◽  
Compact Binaries ◽  
Hierarchical Search ◽  
Non Gaussian

We analyzed the 7 hours of continuous data from TAMA300 by using the matched filtering, searching for the gravitational waves from inspiraling compact binaries. For future long-term continuous operation, it is necessary to reduce the computation time. For this purpose, we introduced hierarchical search strategy. We also introduced a χ2-test to avoid the loss of signal due to the non-Gaussian property of noise. In this paper, we report the results from our preliminary analysis.

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