scholarly journals Search for the gravitational wave memory effect with the Parkes Pulsar Timing Array

2012 ◽  
Vol 8 (S291) ◽  
pp. 543-545
Author(s):  
Jingbo Wang ◽  
G. Hobbs ◽  
Na Wang

AbstractGravitational wave bursts produced by supermassive binary black hole mergers will leave a persistent imprint on the space-time metric. Such gravitational wave memory signals are detectable by pulsar timing arrays as a glitch event that would seem to occur simultaneously for all pulsars. In this paper, we describe an initial algorithm which can be used to search for gravitational wave memory signals. We apply this algorithm to the Parkes Pulsar Timing Array data set. No significant gravitational wave memory signal is founded in the data set.

2012 ◽  
Vol 425 (2) ◽  
pp. 1597-1597 ◽  
Author(s):  
R. van Haasteren ◽  
Y. Levin ◽  
G. H. Janssen ◽  
K. Lazaridis ◽  
M. Kramer ◽  
...  

2018 ◽  
Vol 477 (4) ◽  
pp. 5447-5459 ◽  
Author(s):  
Janna M Goldstein ◽  
John Veitch ◽  
Alberto Sesana ◽  
Alberto Vecchio

2014 ◽  
Vol 444 (4) ◽  
pp. 3709-3720 ◽  
Author(s):  
X.-J. Zhu ◽  
G. Hobbs ◽  
L. Wen ◽  
W. A. Coles ◽  
J.-B. Wang ◽  
...  

2019 ◽  
Vol 487 (3) ◽  
pp. 3644-3649 ◽  
Author(s):  
Haochen Wang ◽  
Stephen R Taylor ◽  
Michele Vallisneri

ABSTRACT Gravitational-wave data analysis demands sophisticated statistical noise models in a bid to extract highly obscured signals from data. In Bayesian model comparison, we choose among a landscape of models by comparing their marginal likelihoods. However, this computation is numerically fraught and can be sensitive to arbitrary choices in the specification of parameter priors. In Bayesian cross validation, we characterize the fit and predictive power of a model by computing the Bayesian posterior of its parameters in a training data set, and then use that posterior to compute the averaged likelihood of a different testing data set. The resulting cross-validation scores are straightforward to compute; they are insensitive to prior tuning; and they penalize unnecessarily complex models that overfit the training data at the expense of predictive performance. In this article, we discuss cross validation in the context of pulsar-timing-array data analysis, and we exemplify its application to simulated pulsar data (where it successfully selects the correct spectral index of a stochastic gravitational-wave background), and to a pulsar data set from the NANOGrav 11-yr release (where it convincingly favours a model that represents a transient feature in the interstellar medium). We argue that cross validation offers a promising alternative to Bayesian model comparison, and we discuss its use for gravitational-wave detection, by selecting or refuting models that include a gravitational-wave component.


2011 ◽  
Vol 414 (4) ◽  
pp. 3117-3128 ◽  
Author(s):  
R. van Haasteren ◽  
Y. Levin ◽  
G. H. Janssen ◽  
K. Lazaridis ◽  
M. Kramer ◽  
...  

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