Nested sampling as a tool for LISA data analysis

Gair, J. R.; Feroz, Farhan; Babak, S.; Graff, P. B.; Hobson, M.; Porter, E. K.

doi:10.1088/1742-6596/228/1/012010

Cited by 9 publications

(7 citation statements)

References 18 publications

(20 reference statements)

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“…Today, Monte Carlo sampling techniques, such as Markov Chain Monte Carlo (MCMC) and Nested Sampling, are used to sample the posterior and estimate the evidence (Skilling 2006 ; Gair et al. 2010 ). Successfully applying these techniques is something of an art, but in principle, once the likelihood and prior have been written down, the implementation of Bayesian inference is purely mechanical.…”

Section: Statistical Inferencementioning

confidence: 99%

Detection methods for stochastic gravitational-wave backgrounds: a unified treatment

2017

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We review detection methods that are currently in use or have been proposed to search for a stochastic background of gravitational radiation. We consider both Bayesian and frequentist searches using ground-based and space-based laser interferometers, spacecraft Doppler tracking, and pulsar timing arrays; and we allow for anisotropy, non-Gaussianity, and non-standard polarization states. Our focus is on relevant data analysis issues, and not on the particular astrophysical or early Universe sources that might give rise to such backgrounds. We provide a unified treatment of these searches at the level of detector response functions, detection sensitivity curves, and, more generally, at the level of the likelihood function, since the choice of signal and noise models and prior probability distributions are actually what define the search. Pedagogical examples are given whenever possible to compare and contrast different approaches. We have tried to make the article as self-contained and comprehensive as possible, targeting graduate students and new researchers looking to enter this field.Electronic supplementary materialThe online version of this article (doi:10.1007/s41114-017-0004-1) contains supplementary material, which is available to authorized users.

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Section: Statistical Inferencementioning

confidence: 99%

Detection methods for stochastic gravitational-wave backgrounds: a unified treatment

2017

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“…The NS technique has been quickly taken up in the field of astrophysics [31][32][33][34][35] and gravitational wave data analysis [36,37], and has gradually been adapted to explore the parameter space in a wide range of disciplines, such as data analysis [38], signal processing [39], phylogenetics [40], and systems biology [41,42].…”

Section: The Nested Sampling Methodsmentioning

confidence: 99%

Nested sampling for materials

2021

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We review the materials science applications of the nested sampling (NS) method, which was originally conceived for calculating the evidence in Bayesian inference. We describe how NS can be adapted to sample the potential energy surface (PES) of atomistic systems, providing a straightforward approximation for the partition function and allowing the evaluation of thermodynamic variables at arbitrary temperatures. After an overview of the basic method, we describe a number of extensions, including using variable cells for constant pressure sampling, the semi-grand-canonical approach for multicomponent systems, parallelizing the algorithm, and visualizing the results. We cover the range of materials applications of NS from the past decade, from exploring the PES of Lennard–Jones clusters to that of multicomponent condensed phase systems. We highlight examples how the information gained via NS promotes the understanding of materials properties through a novel way of visualizing the PES, identifying thermodynamically relevant basins, and calculating the entire pressure–temperature(–composition) phase diagram. Graphic abstract

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“…A template depends on both the GW strain and the response of the instrument to a GW, which includes evaluation of TDI. Many proposed approaches to LISA data analysis use Bayesian techniques [121,93,34], but other approaches that have been suggested include genetic algorithms [191,94] and machine learning.…”

Section: Global Fit Strategiesmentioning

confidence: 99%

Space-based Gravitational Wave Observatories

Gair,

Hewitson,

Petiteau

et al. 2022

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Nested sampling as a tool for LISA data analysis

Cited by 9 publications

References 18 publications

Detection methods for stochastic gravitational-wave backgrounds: a unified treatment

Detection methods for stochastic gravitational-wave backgrounds: a unified treatment

Nested sampling for materials

Space-based Gravitational Wave Observatories

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