scholarly journals New advances in the Gaussian-process approach to pulsar-timing data analysis

2014 ◽  
Vol 90 (10) ◽  
Author(s):  
Rutger van Haasteren ◽  
Michele Vallisneri
Keyword(s):  
Data Analysis ◽  
Gaussian Process ◽  
Process Approach ◽  
Pulsar Timing ◽  
Timing Data

scholarly journals Stochastic and continuous gravitational wave analysis pipelines for pulsar timing array data

2012 ◽  
Vol 8 (S291) ◽  
pp. 178-178
Author(s):  
Justin Ellis ◽  
Fredrick Jenet ◽  
Xavier Siemens ◽  
Maura McLaughlin
Keyword(s):  
Data Analysis ◽  
Gravitational Waves ◽  
Recent Progress ◽  
Simulated Data ◽  
Wave Analysis ◽  
Stochastic Background ◽  
Pulsar Timing ◽  
Timing Data ◽  
Timing Models ◽  
Pulsar Timing Array

AbstractThe Nanohertz Observatory for Gravitational Waves (NANOGrav) collaboration aims to detect gravitational waves (GWs) through the precise timing of millisecond pulsars. GWs will come in the form of a stochastic background, continuous sources and burst sources. Here we will review recent progress on the development of data analysis pipelines aimed at the detection of a stochastic background as well as continuous sources. We will introduce the Optimal Statistic and F-Statistic methods that are used in the stochastic and continuous pipelines, respectively. Both pipelines are fully functional on real pulsar timing data and take into account the timing models for each pulsar. Finally, we will present the efficacy of each pipeline on locally simulated data as well as data from the 2012 IPTA data challenge.

scholarly journals Ensemble Pulsar Time Study by Pulsar Timing Observations

2004 ◽  
Vol 218 ◽  
pp. 439-440
Author(s):  
Tinggao Yang ◽  
Guangren Ni
Keyword(s):  
Time Scale ◽  
Wavelet Decomposition ◽  
Time Study ◽  
Millisecond Pulsar ◽  
Millisecond Pulsars ◽  
Pulsar Time ◽  
Pulsar Timing ◽  
Timing Data ◽  
Gravitational Wave Background

Long term timing of multiple millisecond pulsars can contribute to the study of an ensemble pulsar time scale PTens. A wavelet decomposition algorithm (WDA) was applied to define a PTens using the available millisecond pulsar timing datA. The PTens obtained from WDA is more stable than those resulting from other algorithms. The Chinese 50 m radio telescope is specially designed for PTens study and detection of gravitational wave background via millisecond pulsars timing observations. A scheme for multiple millisecond pulsar timing and ensemble pulsar time study is discussed in some detail.

scholarly journals Hyper-efficient model-independent Bayesian method for the analysis of pulsar timing data

2013 ◽  
Vol 87 (10) ◽  
Author(s):  
Lindley Lentati ◽  
P. Alexander ◽  
M. P. Hobson ◽  
S. Taylor ◽  
J. Gair ◽  
...  
Keyword(s):  
Bayesian Method ◽  
Pulsar Timing ◽  
Timing Data ◽  
Model Independent

scholarly journals The International Pulsar Timing Array: second data release

2019 ◽  
Vol 490 (4) ◽  
pp. 4666-4687 ◽  
Author(s):  
B B P Perera ◽  
M E DeCesar ◽  
P B Demorest ◽  
M Kerr ◽  
L Lentati ◽  
...  
Keyword(s):  
Gravitational Waves ◽  
High Precision ◽  
Noise Analysis ◽  
Low Frequency ◽  
The North ◽  
Pulsar Timing ◽  
Data Release ◽  
Timing Data ◽  
Noise Models ◽  
Pulsar Timing Array

ABSTRACT In this paper, we describe the International Pulsar Timing Array second data release, which includes recent pulsar timing data obtained by three regional consortia: the European Pulsar Timing Array, the North American Nanohertz Observatory for Gravitational Waves, and the Parkes Pulsar Timing Array. We analyse and where possible combine high-precision timing data for 65 millisecond pulsars which are regularly observed by these groups. A basic noise analysis, including the processes which are both correlated and uncorrelated in time, provides noise models and timing ephemerides for the pulsars. We find that the timing precisions of pulsars are generally improved compared to the previous data release, mainly due to the addition of new data in the combination. The main purpose of this work is to create the most up-to-date IPTA data release. These data are publicly available for searches for low-frequency gravitational waves and other pulsar science.

Gaussian process approach for metric learning

2019 ◽  
Vol 87 ◽  
pp. 17-28 ◽  
Author(s):  
Ping Li ◽  
Songcan Chen
Keyword(s):  
Gaussian Process ◽  
Metric Learning ◽  
Process Approach
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