Towards a systematic treatment of observational uncertainties in forward asteroseismic modelling of gravity-mode pulsators

Authors: Dominic M. Bowman, Mathias Michielsen

A&A 656, A158 (2021)
arXiv: 2109.10776v1 - DOI (astro-ph.SR)
21 pages, proposed for acceptance for publication in A&A
License: CC BY 4.0

Abstract: Context. In asteroseismology the pulsation mode frequencies of a star are the fundamental data that are compared to theoretical predictions to determine a star's interior physics. Recent significant advances in the numerical, theoretical and statistical asteroseismic methods applied to main sequence stars with convective cores have renewed the interest in investigating the propagation of observational uncertainties within a forward asteroseismic modelling framework. Aims. We aim to quantify the impact of various choices made throughout the observational aspects of extracting pulsation mode frequencies in main sequence stars with gravity modes. Methods. We use a well-studied benchmark slowly pulsating B star, KIC 7760680, to investigate the sensitivity of forward asteroseismic modelling to various sources of observational uncertainty that affect the precision of the input pulsation mode frequencies. Results. We quantify the impact of the propagation of the observational uncertainties involved in forward asteroseismic modelling. We find that one of the largest sources of uncertainty in our benchmark star is in the manual building of period spacing patterns, such that the inclusion of a potentially ambiguous pulsation mode frequency may yield differences in model parameters of up to 10% for mass and age depending on the radial order of the mode. Conclusions. We conclude that future asteroseismic studies of main sequence stars with a convective core should quantify and include observational uncertainties introduced by the light curve extraction, iterative pre-whitening and the building of period spacing patterns, as these propagate into the final modelling results.

Submitted to arXiv on 22 Sep. 2021

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.