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    TEMPONEST: A bayesian approach to pulsar timing analysis

    Access Status
    Open access via publisher
    Authors
    Lentati, L.
    Alexander, P.
    Hobson, P.
    Feroz, F.
    van Haasteren, R.
    Lee, K.
    Shannon, Ryan
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Lentati, L. and Alexander, P. and Hobson, P. and Feroz, F. and van Haasteren, R. and Lee, K. and Shannon, R. 2014. TEMPONEST: A bayesian approach to pulsar timing analysis. Monthly Notices of the Royal Astronomical Society. 437 (3): pp. 3004-3023.
    Source Title
    Monthly Notices of the Royal Astronomical Society
    DOI
    10.1093/mnras/stt2122
    ISSN
    0035-8711
    School
    Curtin Institute of Radio Astronomy (Physics)
    URI
    http://hdl.handle.net/20.500.11937/41689
    Collection
    • Curtin Research Publications
    Abstract

    A new Bayesian software package for the analysis of pulsar timing data is presented in the form of TEMPONEST which allows for the robust determination of the non-linear pulsar timing solution simultaneously with a range of additional stochastic parameters. This includes both red spin noise and dispersion measure variations using either power-law descriptions of the noise, or through a model-independent method that parametrizes the power at individual frequencies in the signal. We use TEMPONEST to show that at noise levels representative of current data sets in the European Pulsar Timing Array and International Pulsar Timing Array the linear timing model can underestimate the uncertainties of the timing solution by up to an order of magnitude. We also show how to perform Bayesian model selection between different sets of timing model and stochastic parameters, for example, by demonstrating that in the pulsar B1937+21 both the dispersion measure variations and spin noise in the data are optimally modelled by simple power laws. Finally, we show that not including the stochastic parameters simultaneously with the timing model can lead to unpredictable variation in the estimated uncertainties, compromising the robustness of the scientific results extracted from such analysis.

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