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dc.contributor.authorGu, Junhua
dc.contributor.authorWang, Jingying
dc.date.accessioned2021-02-09T20:02:34Z
dc.date.available2021-02-09T20:02:34Z
dc.date.issued2020
dc.identifier.citationJunhua, G &Jingying, W. (2020) Direct parameter inference from global EoR signal with Bayesian statistics. Monthly Notices of the Royal Astronomical Society, V492,I3.Pg4080–4096. https://doi.org/10.1093/mnras/staa052en_US
dc.identifier.otherDOI: https://doi.org/10.1093/mnras/staa052
dc.identifier.urihttp://hdl.handle.net/10566/5880
dc.description.abstractIn the observation of sky-averaged HI signal from Epoch of Reionization (EoR), model parameter inference can be a computation-intensive work, which makes it hard to perform a direct one-stage model parameter inference by using Markov Chain Monte Carlo (MCMC) sampling method in Bayesian framework. Instead, a two-stage inference is usually used, i.e. the parameters of some characteristic points on the EoR spectrum model are first estimated, which are then used as the input to estimate physical model parameters further. However, some previous works had noticed that this kind of method could bias results, and it could be meaningful to answer the question of whether it is feasible to perform direct one-stage MCMC sampling and obtain unbiased physical model parameter estimations. In this work, we studied this problem and confirmed the feasibility. We find that unbiased estimations to physical model parameters can be obtained with a one-stage direct MCMC sampling method. We also study the influence of some factors that should be considered in practical observations to model parameter inference. We find that a very tiny amplifier gain calibration error (10−5 relative error) with complex spectral structures can significantly bias the parameter estimation; the frequency-dependent antenna beam and geographical position can also influence the results, so that should be carefully handled.en_US
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.subjectMethods: Numericalen_US
dc.subjectMethods: statisticalen_US
dc.subjectCosmology: observationsen_US
dc.subjectDark agesen_US
dc.titleDirect parameter inference from global EoR signal with Bayesian statisticsen_US
dc.typeArticleen_US


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