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Taming outliers in pulsar-timing data sets with hierarchical likelihoods and Hamiltonian sampling

Vallisneri, Michele and van Haasteren, Rutger (2017) Taming outliers in pulsar-timing data sets with hierarchical likelihoods and Hamiltonian sampling. Monthly Notices of the Royal Astronomical Society, 466 (4). pp. 4954-4959. ISSN 0035-8711. https://resolver.caltech.edu/CaltechAUTHORS:20170623-123731334

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Abstract

Pulsar-timing data sets have been analysed with great success using probabilistic treatments based on Gaussian distributions, with applications ranging from studies of neutron-star structure to tests of general relativity and searches for nanosecond gravitational waves. As for other applications of Gaussian distributions, outliers in timing measurements pose a significant challenge to statistical inference, since they can bias the estimation of timing and noise parameters, and affect reported parameter uncertainties. We describe and demonstrate a practical end-to-end approach to perform Bayesian inference of timing and noise parameters robustly in the presence of outliers, and to identify these probabilistically. The method is fully consistent (i.e. outlier-ness probabilities vary in tune with the posterior distributions of the timing and noise parameters), and it relies on the efficient sampling of the hierarchical form of the pulsar-timing likelihood. Such sampling has recently become possible with a ‘no-U-turn’ Hamiltonian sampler coupled to a highly customized reparametrization of the likelihood; this code is described elsewhere, but it is already available online. We recommend our method as a standard step in the preparation of pulsar-timing-array data sets: even if statistical inference is not affected, follow-up studies of outlier candidates can reveal unseen problems in radio observations and timing measurements; furthermore, confidence in the results of gravitational-wave searches will only benefit from stringent statistical evidence that data sets are clean and outlier-free.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/stx069DOIArticle
https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stx069PublisherArticle
https://arxiv.org/abs/1609.02144arXivDiscussion Paper
ORCID:
AuthorORCID
Vallisneri, Michele0000-0002-4162-0033
van Haasteren, Rutger0000-0002-6428-2620
Alternate Title:Taming outliers in pulsar-timing datasets with hierarchical likelihoods and Hamiltonian sampling
Additional Information:© 2017 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2017 January 10. Received 2016 December 17; in original form 2016 September 19. Published: 20 January 2017. We thank B. Bassett, E. Cameron, N. Cornish, C. Cutler, J. Ellis, J. Lazio, C. Mingarelli, D. Nice, S. Taylor and the anonymous MNRAS referee for useful comments. MV was supported by the Jet Propulsion Laboratory RTD programme. RvH was supported by NASA Einstein Fellowship grant PF3-140116. This research was supported in part by National Science Foundation Physics Frontier Center award no. 1430284, and by grant PHYS-1066293 and the hospitality of the Aspen Center for Physics. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract to the National Aeronautics and Space Administration. Copyright 2016 California Institute of Technology. Government sponsorship acknowledged.
Group:TAPIR
Funders:
Funding AgencyGrant Number
NASA Einstein FellowshipPF3-140116
NSF1430284
NSFPHYS-1066293
NASA/JPL/CaltechUNSPECIFIED
Subject Keywords:gravitational waves – methods: data analysis – pulsars: general
Issue or Number:4
Record Number:CaltechAUTHORS:20170623-123731334
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170623-123731334
Official Citation:Michele Vallisneri, Rutger van Haasteren; Taming outliers in pulsar-timing data sets with hierarchical likelihoods and Hamiltonian sampling. Mon Not R Astron Soc 2017; 466 (4): 4954-4959. doi: 10.1093/mnras/stx069
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:78518
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 Jun 2017 19:50
Last Modified:09 Mar 2020 13:19

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