TEMPONEST: A bayesian approach to pulsar timing analysis
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 dispe...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/41689 |
| _version_ | 1848756214853271552 |
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| author | Lentati, L. Alexander, P. Hobson, P. Feroz, F. van Haasteren, R. Lee, K. Shannon, Ryan |
| author_facet | Lentati, L. Alexander, P. Hobson, P. Feroz, F. van Haasteren, R. Lee, K. Shannon, Ryan |
| author_sort | Lentati, L. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:08:39Z |
| format | Journal Article |
| id | curtin-20.500.11937-41689 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:08:39Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-416892017-09-13T14:15:44Z TEMPONEST: A bayesian approach to pulsar timing analysis Lentati, L. Alexander, P. Hobson, P. Feroz, F. van Haasteren, R. Lee, K. Shannon, Ryan 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. 2014 Journal Article http://hdl.handle.net/20.500.11937/41689 10.1093/mnras/stt2122 unknown |
| spellingShingle | Lentati, L. Alexander, P. Hobson, P. Feroz, F. van Haasteren, R. Lee, K. Shannon, Ryan TEMPONEST: A bayesian approach to pulsar timing analysis |
| title | TEMPONEST: A bayesian approach to pulsar timing analysis |
| title_full | TEMPONEST: A bayesian approach to pulsar timing analysis |
| title_fullStr | TEMPONEST: A bayesian approach to pulsar timing analysis |
| title_full_unstemmed | TEMPONEST: A bayesian approach to pulsar timing analysis |
| title_short | TEMPONEST: A bayesian approach to pulsar timing analysis |
| title_sort | temponest: a bayesian approach to pulsar timing analysis |
| url | http://hdl.handle.net/20.500.11937/41689 |