Bayesian inference for radio observations

 

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dc.contributor.author Lochner, Michelle
dc.contributor.author Natarajan, Iniyan
dc.contributor.author Zwart, Jonathan T L
dc.contributor.author Smirnov, Oleg
dc.contributor.author Bassett, Bruce A
dc.contributor.author Oozeer, Nadeem
dc.contributor.author Kunz, Martin
dc.date.accessioned 2016-08-15T12:45:12Z
dc.date.available 2016-08-15T12:45:12Z
dc.date.issued 25
dc.identifier http://dx.doi.org/10.1093/mnras/stv679
dc.identifier.citation Lochner, 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.issn 0035-8711 en_ZA
dc.identifier.uri http://hdl.handle.net/11427/21247
dc.description.abstract 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. en_ZA
dc.language.iso eng
dc.publisher Oxford University Press en_ZA
dc.rights Creative Commons Attribution 4.0 International (CC BY 4.0) *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en_ZA
dc.source Monthly Notices of the Royal Astronomical Society en_ZA
dc.source.uri http://oxfordjournals.org/our_journals/mnras/altmetrics_articles.html
dc.subject.other Methods
dc.subject.other Data analysis – methods
dc.subject.other Statistical – techniques
dc.subject.other Interferometric
dc.title Bayesian inference for radio observations en_ZA
dc.type Journal Article en_ZA
dc.date.updated 2016-08-12T13:11:39Z
uct.type.publication Research en_ZA
uct.type.resource Article en_ZA
dc.publisher.institution University of Cape Town
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Lochner, 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/11427/21247 en_ZA
dc.identifier.chicagocitation Lochner, 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/11427/21247 en_ZA
dc.identifier.vancouvercitation Lochner 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.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


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