The Preparation of a Primary Standard of Tritium Water

Farr, J. D.; Vier, D.T.

doi:10.2172/4355033

Cited by 1 publication

(1 citation statement)

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“…The technique of constructing a proposal distribution for a highdimensional space which has been sparsely covered by a Markov chain was described with more detail in [23]. Binning the search chains into a proposal distribution can most likely be made more efficient by using binary space partitioning algorithms -a technique that has previously been studied for use in LIGO/Virgo analyses to approximate the posterior distribution from a Markov chain [52].…”

Section: Characterization and Evaluationmentioning

confidence: 99%

Separating gravitational wave signals from instrument artifacts

Littenberg

Cornish

2010

Phys. Rev. D

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Central to the gravitational wave detection problem is the challenge of separating features in the data produced by astrophysical sources from features produced by the detector. Matched filtering provides an optimal solution for Gaussian noise, but in practice, transient noise excursions or "glitches" complicate the analysis. Detector diagnostics and coincidence tests can be used to veto many glitches which may otherwise be misinterpreted as gravitational wave signals. The glitches that remain can lead to long tails in the matched filter search statistics and drive up the detection threshold. Here we describe a Bayesian approach that incorporates a more realistic model for the instrument noise allowing for fluctuating noise levels that vary independently across frequency bands, and deterministic "glitch fitting" using wavelets as "glitch templates", the number of which is determined by a trans-dimensional Markov chain Monte Carlo algorithm. We demonstrate the method's effectiveness on simulated data containing low amplitude gravitational wave signals from inspiraling binary black hole systems, and simulated non-stationary and non-Gaussian noise comprised of a Gaussian component with the standard LIGO/Virgo spectrum, and injected glitches of various amplitude, prevalence, and variety. Glitch fitting allows us to detect significantly weaker signals than standard techniques.

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Section: Characterization and Evaluationmentioning

confidence: 99%