Thorough characterisation of the 16 Cygni system

I. Forward seismic modelling with WhoSGlAd

¹ Institut d’Astrophysique et Géophysique de l’Université de Liège, Allée du 6 août 17, 4000 Liège, Belgium
e-mail: martin.farnir@uliege.be
² Observatoire de Genève, Université de Genève, 51 Ch. Des Maillettes, 1290 Sauverny, Switzerland
³ LESIA, Observatoire de Paris, PSL Research University, CNRS, Université Pierre et Marie Curie, Université Paris Diderot, 92195 Meudon, France

Received: 28 May 2020
Accepted: 7 October 2020

Abstract

Context. Being part of the brightest solar-like stars, and close solar analogues, the 16 Cygni system is of great interest to the scientific community and may provide insight into the past and future evolution of our Sun. It has been observed thoroughly by the Kepler satellite, which provided us with data of an unprecedented quality.

Aims. This paper is the first of a series aiming to extensively characterise the system. We test several choices of micro- and macro-physics to highlight their effects on optimal stellar parameters and provide realistic stellar parameter ranges.

Methods. We used a recently developed method, WhoSGlAd, that takes the utmost advantage of the whole oscillation spectrum of solar-like stars by simultaneously adjusting the acoustic glitches and the smoothly varying trend. For each choice of input physics, we computed models which account, at best, for a set of seismic indicators that are representative of the stellar structure and are as uncorrelated as possible. The search for optimal models was carried out through a Levenberg-Marquardt minimisation. First, we found individual optimal models for both stars. We then selected the best candidates to fit both stars while imposing a common age and composition.

Results. We computed realistic ranges of stellar parameters for individual stars. We also provide two models of the system regarded as a whole. We were not able to build binary models with the whole set of choices of input physics considered for individual stars as our constraints seem too stringent. We may need to include additional parameters to the optimal model search or invoke non-standard physical processes.