期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 502, 期 4, 页码 5839-5853出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stab379
关键词
methods: statistical; techniques: polarimetric; radio continuum: general
资金
- UK Newton Fund via the Science & Technology Facilities Council (STFC) through the Development in Africa with Radio Astronomy (DARA) Big Data project [ST/R001898/1]
- Alan Turing Institute AI fellowship [EP/V030302/1]
- UK Science & Technology Facilities Council (STFC)
- National Agency for Research and Development (ANID) Scholarship Program DOCTORADO BECAS CHILE [2018-72190574]
The incomplete sampling of data in complex polarization measurements negatively affects RM transfer function and Faraday depth spectra. The novel method presented in this paper, based on Gaussian processes, improves the resolution of reconstructed Faraday depth spectra by inferring missing polarization data.
The incomplete sampling of data in complex polarization measurements from radio telescopes negatively affects both the rotation measure (RM) transfer function and the Faraday depth spectra derived from these data. Such gaps in polarization data are mostly caused by flagging of radio frequency interference and their effects worsen as the percentage of missing data increases. In this paper we present a novel method for inferring missing polarization data based on Gaussian processes (GPs). GPs are stochastic processes that enable us to encode prior knowledge in our models. They also provide a comprehensive way of incorporating and quantifying uncertainties in regression modelling. In addition to providing non-parametric model estimates for missing values, we also demonstrate that GP modelling can be used for recovering rotation measure values directly from complex polarization data, and that inferring missing polarization data using this probabilistic method improves the resolution of reconstructed Faraday depth spectra.
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