4.7 Article

Robust parameter estimation from pulsar timing data

期刊

出版社

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stac2810

关键词

gravitational waves; methods: data analysis; pulsars: general

资金

  1. European Union's H2020 ERC Consolidator Grant 'Binary Massive Black Hole Astrophysics' [818691]
  2. Alexander von Humboldt foundation
  3. Stavros Niarchos Foundation (SNF)
  4. Hellenic Foundation for Research and Innovation (H.F.R.I.) under the 2nd Call of 'Science and Society' Action Always strive for excellence -'Theodoros Papazoglou' [01431]
  5. Ontario Research Fund-research Excellence Program (ORF-RE)
  6. Natural Sciences and Engineering Research Council of Canada (NSERC) [CRD 523638-18]
  7. 'Programme National de Cosmologie et Galaxies' (PNCG)
  8. 'Programme National Hautes Energies' (PNHE) of CNRS/INSU, France

向作者/读者索取更多资源

This study focuses on searching for a nano-Hertz gravitational wave background signal using pulsar timing data. Although no definite evidence has been found yet, the researchers expect to make a statistically significant detection in the future with more data. The study compares different sampling algorithms and proposes two robustness checks for the data.
Recently, global pulsar timing arrays have released results from searching for a nano-Hertz gravitational wave background signal. Although there has not been any definite evidence of the presence of such a signal in residuals of pulsar timing data yet, with more and improved data in future, a statistically significant detection is expected to be made. Stochastic algorithms are used to sample a very large parameter space to infer results from data. In this paper, we attempt to rule out effects arising from the stochasticity of the sampler in the inference process. We compare different configurations of nested samplers and the more commonly used markov chain monte carlo method to sample the pulsar timing array parameter space and account for times taken by the different samplers on same data. Although we obtain consistent results on parameters from different sampling algorithms, we propose two different samplers for robustness checks on data in the future to account for cross-checks between sampling methods as well as realistic run-times.

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