Bayesian inference for radio observations

dc.contributor.authorLochner, Michelle
dc.contributor.authorNatarajan, Iniyan
dc.contributor.authorZwart, Jonathan T L
dc.contributor.authorSmirnov, Oleg
dc.contributor.authorBassett, Bruce A
dc.contributor.authorOozeer, Nadeem
dc.contributor.authorKunz, Martin
dc.date.accessioned2016-08-15T12:45:12Z
dc.date.available2016-08-15T12:45:12Z
dc.date.issued25
dc.date.updated2016-08-12T13:11:39Z
dc.description.abstractNew telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inadequate uncertainty estimates and biased results because any correlations between parameters are ignored. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realization of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. This enables it to derive both correlations and accurate uncertainties, making use of the flexible software MEQTREES to model the sky and telescope simultaneously. We demonstrate BIRO with two simulated sets of Westerbork Synthesis Radio Telescope data sets. In the first, we perform joint estimates of 103 scientific (flux densities of sources) and instrumental (pointing errors, beamwidth and noise) parameters. In the second example, we perform source separation with BIRO. Using the Bayesian evidence, we can accurately select between a single point source, two point sources and an extended Gaussian source, allowing for ‘super-resolution’ on scales much smaller than the synthesized beam.en_ZA
dc.identifierhttp://dx.doi.org/10.1093/mnras/stv679
dc.identifier.apacitationLochner, M., Natarajan, I., Zwart, J. T. L., Smirnov, O., Bassett, B. A., Oozeer, N., & Kunz, M. (25). Bayesian inference for radio observations. <i>Monthly Notices of the Royal Astronomical Society</i>, http://hdl.handle.net/11427/21247en_ZA
dc.identifier.chicagocitationLochner, Michelle, Iniyan Natarajan, Jonathan T L Zwart, Oleg Smirnov, Bruce A Bassett, Nadeem Oozeer, and Martin Kunz "Bayesian inference for radio observations." <i>Monthly Notices of the Royal Astronomical Society</i> (25) http://hdl.handle.net/11427/21247en_ZA
dc.identifier.citationLochner, M., Natarajan, I., Zwart, J. T., Smirnov, O., Bassett, B. A., Oozeer, N., & Kunz, M. (2015). Bayesian inference for radio observations. Monthly Notices of the Royal Astronomical Society, 450(2), 1308-1319.en_ZA
dc.identifier.issn0035-8711en_ZA
dc.identifier.ris TY - Journal Article AU - Lochner, Michelle AU - Natarajan, Iniyan AU - Zwart, Jonathan T L AU - Smirnov, Oleg AU - Bassett, Bruce A AU - Oozeer, Nadeem AU - Kunz, Martin AB - New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inadequate uncertainty estimates and biased results because any correlations between parameters are ignored. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realization of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. This enables it to derive both correlations and accurate uncertainties, making use of the flexible software MEQTREES to model the sky and telescope simultaneously. We demonstrate BIRO with two simulated sets of Westerbork Synthesis Radio Telescope data sets. In the first, we perform joint estimates of 103 scientific (flux densities of sources) and instrumental (pointing errors, beamwidth and noise) parameters. In the second example, we perform source separation with BIRO. Using the Bayesian evidence, we can accurately select between a single point source, two point sources and an extended Gaussian source, allowing for ‘super-resolution’ on scales much smaller than the synthesized beam. DA - 25 DB - OpenUCT DP - University of Cape Town J1 - Monthly Notices of the Royal Astronomical Society LK - https://open.uct.ac.za PB - University of Cape Town PY - 25 SM - 0035-8711 T1 - Bayesian inference for radio observations TI - Bayesian inference for radio observations UR - http://hdl.handle.net/11427/21247 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/21247
dc.identifier.vancouvercitationLochner M, Natarajan I, Zwart JTL, Smirnov O, Bassett BA, Oozeer N, et al. Bayesian inference for radio observations. Monthly Notices of the Royal Astronomical Society. 25; http://hdl.handle.net/11427/21247.en_ZA
dc.language.isoeng
dc.publisherOxford University Pressen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0)*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_ZA
dc.sourceMonthly Notices of the Royal Astronomical Societyen_ZA
dc.source.urihttp://oxfordjournals.org/our_journals/mnras/altmetrics_articles.html
dc.subject.otherMethods
dc.subject.otherData analysis – methods
dc.subject.otherStatistical – techniques
dc.subject.otherInterferometric
dc.titleBayesian inference for radio observationsen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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