| ORIGINAL ARTICLE | Publication URI: http://www.livingreviews.org/lrr-2005-3 |
|---|---|
| Title | Gravitational-Wave Data Analysis. Formalism and Sample Applications: The Gaussian Case |
| Author | Piotr Jaranowski / Andrzej Królak |
| Date | accepted 3 March 2005, published 21 March 2005 |
| FAST-TRACK REVISION | |
| Date | accepted 24 July 2007, published 26 July 2007 |
| Changes | Section 2 and 4.4.1 have been extended, some notations were changed and Figure 1 was updated. Equations (28) and (36) have been corrected. 15 references were added.
For detailed description see
here
.
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| UPDATE | Publication URI: http://www.livingreviews.org/lrr-2012-4 |
| Title | Gravitational-Wave Data Analysis. Formalism and Sample Applications: The Gaussian Case |
| Author | Piotr Jaranowski / Andrzej Królak |
| Date | accepted 14 February 2012, published 9 March 2012 |
| Changes | Material of the previous version of the review was partially reorganized and updated, 46 new references were added. 1. Section 2 was rewritten and extended, several new references were added. 2. Some parts of the former Section 4 were moved to the present Section 3, which is now a brief general introduction to the statistical theory of signal detection and of estimation of signals parameters. Some new references were added. 3. The present Section 4 is a partially rewritten (using some new, more convenient notation) and extended version of the former Sections 4.3 – 4.9. The gravitational-wave signal considered here was generalized from a 4-amplitude-parameter case to an n-amplitude-parameter case, where n is arbitrary. New Section 4.1.1 about targeted searches was added, and new Section 4.4.1 on the covering problem was created with references to constructions of various grids of templates for searches of continuous gravitational waves. 4. The present Section 5 is an expanded version of the former Section 4.10 with addition of several recent references. 5. The present Section 6 is an expanded version of the former Section 4.11 with new discussion of optimal filtering for non-stationary data and description of a test (Grubbs’ test) to detect outliers in data. |