PBjam 2
Peakbagging made easy
PBjam is toolbox for analyzing the oscillation spectra of solar-like oscillators. This involves two main parts: identifying a set of modes of interest in a spectrum of oscillations, and accurately modeling those modes to measure their frequencies.
The mode identification works by fitting the asymptotic relation for p-modes to the l=2,0 pairs, which is followed by a applying selection of models for fitting the l=1 modes where each model is suitable for different stages of evolution. The process relies on of large set of previous observations of the model parameters, which are then used to construct a prior distribution to inform the sampling. The observations have been gathered from the Kepler, K2 and TESS missions, and expanding it to improve accuracy is an on-going process.
Modeling the modes, or ‘peakbagging’, is done using the a nested sampling or MCMC algorithm, where Lorentzian profiles are fit to each of the identified modes, with much fewer contraints than during the mode ID process. This allows for a more accurate model of the spectrum of frequencies than the heavily parameterized models like the asymptotic relations.
To get started with PBjam please see the docs at pbjam.readthedocs.io.
Contributing
If you want to raise an issue or contribute code to PBjam, see the guidelines on contributing.
Acknowledgements
If you use PBjam in your work please cite the one of the PBjam papers (Paper I Nielsen et al. 2021, Paper II Nielsen et al. 2023 ), and if possible provide links to the GitHub repository.
We encourage users to also cite the packages and publications that PBjam makes use of.
Release files for pbjam 2.0.4
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