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Register a new userInvestigating stellar surface rotation using observations of starspots
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H. Korhonen
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Rapid rotation enhances the dynamo operating in stars, and thus also introducessignificantly stronger magnetic activity than is seen in slower rotators. Many young cool stars still have the rapid, primordial rotation rates induced by the interstellar molecular cloud from which they were formed. Also older stars in close binary systems are often rapid rotators. These types of stars can show strong magnetic activity and large starspots. In the case of large starspots which cause observable changes in the brightness of the star, and even in the shapes of the spectral line profiles, one can get information on the rotation of the star. At times even information on the spot rotation at different stellar latitudes can be obtained, similarly to the solar surface differential rotation measurements using magnetic features as tracers. Here, I will review investigations of stellar rotation based on starspots. I will discuss what we can obtain from ground-based photometry and how that improves with the uninterrupted, high precision, observations from space. The emphasis will be onhow starspots, and even stellar surface differential rotation, can be studied using high resolution spectra.
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In this work the latitude dependent stellar spot rotation is investigated based on dynamo models. The maps of the magnetic pressure at the surface from the dynamo calculations are treated similarly to the temperature maps obtained using Doppler imaging techniques. A series of snapshots from the dynamo models are cross-correlated to obtain the shift of the magnetic patterns at each latitude and time point. The surface differential rotation patterns obtained from the snapshots of the dynamo calculations show in all studied cases variability over the activity cycle. In the models using only the large scale dynamo field the measured rotation patterns are only at times similar to the input rotation law. This is due to the spot motion being mainly determined by the geometric properties of the large scale dynamo field. In the models with additional small scale magnetic field the surface differential rotation measured from the model follows well the input rotation law. The results imply that the stellar spots caused by the large scale dynamo field are not necessarily tracing the stellar differential rotation, whereas the spots formed from small scale fields trace well the surface flow patterns. It can be questioned whether the large spots observed in active stars could be caused by small scale fields. Therefore, it is not clear that the true stellar surface rotation can be recovered using measurements of large starspots, which are currently the only ones that can be observed.
Upcoming missions, including the James Webb Space Telescope, will soon characterize the atmospheres of terrestrial-type exoplanets in habitable zones around cool K- and M-type stars searching for atmospheric biosignatures. Recent observations suggest that the ionizing radiation and particle environment from active cool planet hosts may be detrimental for exoplanetary habitability. Since no direct information on the radiation field is available, empirical relations between signatures of stellar activity, including the sizes and magnetic fields of starspots, are often used. Here, we revisit the empirical relation between the starspot size and the effective stellar temperature and evaluate its impact on estimates of stellar flare energies, coronal mass ejections, and fluxes of the associated stellar energetic particle events.
High-quality time series provided by space instrumentation such as CoRoT and Kepler, allow us to measure modulations in the light curves due to changes in the surface of stars related to rotation and activity. Therefore, we are able to infer the surface (possibly differential) rotation rate. However, instrumental perturbations can also produce artificial modulations in the light curves that would mimic those of truly stellar origin. In this work we will concentrate on Kepler observations in order to review an optimal way to extract reliable surface rotation rates.
The rotational evolution of cool dwarfs is poorly constrained after around 1-2 Gyr due to a lack of precise ages and rotation periods for old main-sequence stars. In this work we use velocity dispersion as an age proxy to reveal the temperature-dependent rotational evolution of low-mass Kepler dwarfs, and demonstrate that kinematic ages could be a useful tool for calibrating gyrochronology in the future. We find that a linear gyrochronology model, calibrated to fit the period-Teff relationship of the Praesepe cluster, does not apply to stars older than around 1 Gyr. Although late-K dwarfs spin more slowly than early-K dwarfs when they are young, at old ages we find that late-K dwarfs rotate at the same rate or faster than early-K dwarfs of the same age. This result agrees qualitatively with semi-empirical models that vary the rate of surface-to-core angular momentum transport as a function of time and mass. It also aligns with recent observations of stars in the NGC 6811 cluster, which indicate that the surface rotation rates of K dwarfs go through an epoch of inhibited evolution. We find that the oldest Kepler stars with measured rotation periods are late-K and early-M dwarfs, indicating that these stars maintain spotted surfaces and stay magnetically active longer than more massive stars. Finally, based on their kinematics, we confirm that many rapidly rotating GKM dwarfs are likely to be synchronized binaries.
We used a convolutional neural network to infer stellar rotation periods from a set of synthetic light curves simulated with realistic spot evolution patterns. We convolved these simulated light curves with real TESS light curves containing minimal intrinsic astrophysical variability to allow the network to learn TESS systematics and estimate rotation periods despite them. In addition to periods, we predict uncertainties via heteroskedastic regression to estimate the credibility of the period predictions. In the most credible half of the test data, we recover 10%-accurate periods for 46% of the targets, and 20%-accurate periods for 69% of the targets. Using our trained network, we successfully recover periods of real stars with literature rotation measurements, even past the 13.7-day limit generally encountered by TESS rotation searches using conventional period-finding techniques. Our method also demonstrates resistance to half-period aliases. We present the neural network and simulated training data, and introduce the software butterpy used to synthesize the light curves using realistic star spot evolution.
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