Frequency separation ratios do not suppress magnetic activity effects in solar-like stars
Frequency separation ratios do not suppress magnetic activity effects in solar-like stars
Jérôme Bétrisey, Anne-Marie Broomhall, Sylvain N. Breton, Rafael A. García, Henry Davenport, Oleg Kochukhov
AbstractMagnetic activity effects are typically neglected in asteroseismic modelling of solar-type stars, presuming that these effects can be accounted for in the parametrisation of the surface effects. It was however demonstrated that magnetic activity can have a significant impact on the asteroseismic characterisation using both forward and inverse techniques. We investigated whether frequency separation ratios, which are commonly used to efficiently suppress surface effects, are also able to suppress magnetic activity effects. Based on GOLF and BiSON observations of the Sun-as-a-star, we performed asteroseismic characterisations using frequency separation ratios as constraints to measure the apparent temporal evolution of the stellar parameters and their correlation with the 10.7 cm radio flux. Frequency separation ratios do not suppress the effects of magnetic activity. Both $r_{01}$ and $r_{02}$ ratios exhibit a clear signature of the magnetic activity cycle. Consequently, when these ratios are employed as constraints in asteroseismic modelling, magnetic activity effects are propagated to the stellar characterisation. Additionally, most stellar parameters correlate with the activity cycle, unlike the direct fitting of individual frequencies. Magnetic activity effects significantly impact asteroseismic characterisation, regardless of whether forward modelling or inverse methods are used. Standard techniques to suppress surface effects have proven ineffective against magnetic activity influences and systematic uncertainties of 4.7%, 2.9%, and 1.0% should be considered for the stellar age, mass, and radius, respectively. In preparation for future space-based photometry missions, it is therefore essential to enhance our theoretical understanding of these effects and develop a modelling procedure capable of accounting for or efficiently suppressing them.