I am applying what we know about the Sun to other solar-type stars, to model surface patterns of activity on stars more active than the Sun. Here are some results below (backward in time).


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Forward modelling brightness variability in solar-type stars (2023)

Application #4 of the FEAT model

The left panel shows light curves for 1-8 times solar rotation and activity level (from top to bottom). In each case the black and the coloured curves represent zero and 70% nesting, respectively. On the right panel, we show the observed (grey dots McQuillan et al. 2014, black dots Santos et al. 2021, using Kepler) and simulated (coloured dots; blue low, yellow high inclination) variability amplitudes as a function of the rotation rate.

The left panel shows light curves for 1-8 times solar rotation and activity level (from top to bottom). In each case the black and the coloured curves represent zero and 70% nesting, respectively. On the right panel, we show the observed (grey dots McQuillan et al. 2014, black dots Santos et al. 2021, using Kepler) and simulated (coloured dots; blue low, yellow high inclination) variability amplitudes as a function of the rotation rate.

Screenshot 2024-05-10 at 23.00.10.png

Starspots and faculae are magnetic structures. They are responsible for the observed brightness variations on solar-type stars. As stars rotate, the disc-integrated brightness is modulated, showing temporal patterns that are related to the surface distribution and evolution of these features. To shed some light on solar-type stellar light curves ranging from very active, rapidly rotating stars to near-solar activity levels, we took the output of the FEAT models for solar to 8 times solar rotation rate and activity level (Işık et al. 2018, scroll below) and synthesised light curves. The study was led by Nina E. Nèmec, who developed a model to generate light curves using time-resolved full-surface maps of magnetic flux distribution from surface flux transport simulations. We showed that, the combined effects of increasing degree of nesting, the activity level, and the rotation rate have led to more regular light curves. In particular, the nesting tendency of active region emergence boosts up light-curve amplitudes.

Reference

Nèmec, N.-E., Shapiro, A.I., Işık, E., Solanki, S.K., Reinhold, T. 2023. Astronomy & Astrophysics, 672, A138


How spots survive on active suns, amid facular cannibalism (2022)

Activity-related brightening (positive) or dimming (negative), as a function of the mean chromospheric activity level. Blue and orange lines show the numerical simulation results for axial inclinations of 90˚ and 0˚. The stellar sample is from Radick et al. (2018), based on observations made at Lowell and Fairborn observatories. Black stars have effective temperatures ±200 K around the solar value and with relative brightening uncertainty below 0.01. Grey stars are the rest of the sample. The grey-shaded band is the posterior distribution of a Bayesian linear regression to solar-like sample using Gaussian priors for a quadratic function.

Activity-related brightening (positive) or dimming (negative), as a function of the mean chromospheric activity level. Blue and orange lines show the numerical simulation results for axial inclinations of 90˚ and 0˚. The stellar sample is from Radick et al. (2018), based on observations made at Lowell and Fairborn observatories. Black stars have effective temperatures ±200 K around the solar value and with relative brightening uncertainty below 0.01. Grey stars are the rest of the sample. The grey-shaded band is the posterior distribution of a Bayesian linear regression to solar-like sample using Gaussian priors for a quadratic function.

Solar-type stars undergo a change in their patterns of variability, as they get older and become less active. Such a low-activity star is our Sun, becoming slightly brighter when more active (every decade in the course of its activity cycle), due to a slight overcompensation of bright faculae to dark spots at its visible surface. Departing from the solar activity level towards more active solar-like stars, spots start to dominate faculae when the star gets more active. In this study, led by Nina-Elisabeth Nèmec (MPS, now at Uni Göttingen), we explained for the first time why this happens. By carrying out surface flux transport simulations at various activity levels (bipolar region emergence frequencies), we showed that, as the star gets more and more active, magnetic flux cancellation gets more efficient among the active-region network (responsible for faculae to appear near the solar/stellar limb) than for the spots, which occupy a smaller area fraction of active regions. As the flux emergence rate increases, facular cancellation dominates over spot cancellation, making stellar variability dominated by spots. In short, spots survive, while faculae kill each other! Let me speculate a bit: this finding is also an indication that ‘large starspots’ inferred on active stars are not likely to be big, monolithic monsters, but rather, constellations of sunspot-size spots extended over large portions of the star. For otherwise, spots would also eat up each other.

Reference

Nèmec, N.-E., Shapiro, A.I., Işık, E., Sowmya, K., Solanki, S.K., Krivova, N.A., Cameron, R.H., Gizon, L. 2022, Astrophys. J. Lett. 934, L23


Predicting astrometric jitter for Sun-like stars: fast-rotating suns (2022)

Application #3 of the FEAT model

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Spot distribution from the FEAT simulation of a star that rotates 8 times faster than the Sun and also 8 times more active. Left: no nesting applied. Middle: active-longitude-type nesting. Right: Strong free nesting with a probability of 99%.

Spot distribution from the FEAT simulation of a star that rotates 8 times faster than the Sun and also 8 times more active. Left: no nesting applied. Middle: active-longitude-type nesting. Right: Strong free nesting with a probability of 99%.