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Register a new userPower spectra of solar brightness variations at different inclinations
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Added by
Nina-Elisabeth N\\`emec
Publication date
2020
fields
Physics
and research's language is
English
Authors
N.-E. N`emec
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Magnetic features on the surfaces of cool stars cause variations of their brightness. Such variations have been extensively studied for the Sun. Recent planet-hunting space telescopes allowed measuring brightness variations in hundred thousands of other stars. The new data posed the question of how typical is the Sun as a variable star. Putting solar variability into the stellar context suffers, however, from the bias of solar observations being made from its near-equatorial plane, whereas stars are observed at all possible inclinations. We model solar brightness variations at timescales from days to years as they would be observed at different inclinations. In particular, we consider the effect of the inclination on the power spectrum of solar brightness variations. The variations are calculated in several passbands routinely used for stellar measurements. We employ the Surface Flux Transport Model (SFTM) to simulate the time-dependent spatial distribution of magnetic features on both near- and far-sides of the Sun. This distribution is then used to calculate solar brightness variations following the SATIRE (Spectral And Total Irradiance REconstruction) approach. We have quantified the effect of the inclination on solar brightness variability at timescales down to a day. Thus, our results allow making solar brightness records directly comparable to those obtained by the planet-hunting space telescopes. Furthermore, we decompose solar brightness variations into the components originating from the solar rotation and from the evolution of magnetic features.
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The solar brightness varies on timescales from minutes to decades. Determining the sources of such variations, often referred to as solar noise, is of importance for multiple reasons: a) it is the background that limits the detection of solar oscillations, b) variability in solar brightness is one of the drivers of the Earths climate system, c) it is a prototype of stellar variability which is an important limiting factor for the detection of extra-solar planets. Here we show that recent progress in simulations and observations of the Sun makes it finally possible to pinpoint the source of the solar noise. We utilise high-cadence observations from the Solar Dynamic Observatory and the SATIRE model to calculate the magnetically-driven variations of solar brightness. The brightness variations caused by the constantly evolving cellular granulation pattern on the solar surface are computed with the MURAM code. We find that surface magnetic field and granulation can together precisely explain solar noise on timescales from minutes to decades, i.e. ranging over more than six orders of magnitude in the period. This accounts for all timescales that have so far been resolved or covered by irradiance measurements. We demonstrate that no other sources of variability are required to explain the data. Recent measurements of Sun-like stars by CoRoT and Kepler uncovered brightness variations similar to that of the Sun but with much wider variety of patterns. Our finding that solar brightness variations can be replicated in detail with just two well-known sources will greatly simplify future modelling of existing CoRoT and Kepler as well as anticipated TESS and PLATO data.
Comparing solar and stellar brightness variations is hampered by the difference in spectral passbands used in observations as well as by the possible difference in the inclination of their rotation axes from the line of sight. We calculate the rotational variability of the Sun as it would be measured in passbands used for stellar observations. In particular, we consider the filter systems used by the CoRoT, $Kepler$, TESS, and $Gaia$ space missions. We also quantify the effect of the inclination of the rotation axis on the solar rotational variability. We employ the Spectral And Total Irradiance REconstructions (SATIRE) model to calculate solar brightness variations in different filter systems as observed from the ecliptic plane. We then combine the simulations of the surface distribution of the magnetic features at different inclinations using a surface flux transport model (SFTM) with the SATIRE calculations to compute the dependence of the variability on the inclination. For an ecliptic-bound observer, the amplitude of the solar rotational variability, as observed in the total solar irradiance (TSI) is 0.68 mmag (averaged over solar cycles 21-24). We obtained corresponding amplitudes in the $Kepler$ (0.74 mmag), CoRoT (0.73 mmag), TESS (0.62 mmag), $Gaia~ $ (0.74 mmag), $Gaia~ G_{RP}$ (0.62 mmag), and ), $Gaia~ G_{BP}$ (0.86 mmag) passbands. Decreasing the inclination of the rotation axis decreases the rotational variability. For a sample of randomly inclined stars, the variability is on average 15% lower in all filter systems considered in this work. This almost compensates for the difference in the amplitudes of the variability in TSI and $Kepler$ passbands, making the amplitudes derived from the TSI records an ideal representation of the solar rotational variability for comparison to $Kepler$ stars with unknown inclinations.
Context. Comparison studies of Sun-like stars with the Sun suggest an anomalously low photometric variability of the Sun compared to Sun-like stars with similar magnetic activity. Comprehensive understanding of stellar variability is needed, to find a physical reasoning for this observation. Aims. We investigate the effect of metallicity and effective temperature on the photometric brightness change of Sun-like stars seen at different inclinations. The considered range of fundamental stellar parameters is sufficiently small so the stars, investigated here, still count as Sun-like or even as solar twins. Methods. To model the brightness change of stars with solar magnetic activity, we extend a well established model of solar brightness variations, SATIRE (which stands for Spectral And Total Irradiance Reconstruction), which is based on solar spectra, to stars with different fundamental parameters. For that we calculate stellar spectra for different metallicities and effective temperature using the radiative transfer code ATLAS9. Results. We show that even a small change (e.g. within the observational error range) of metallicity or effective temperature significantly affects the photometric brightness change compared to the Sun. We find that for Sun-like stars, the amplitude of the brightness variations obtained for Stromgren (b + y)/2 reaches a local minimum for fundamental stellar parameters close to the solar metallicity and effective temperature. Moreover, our results show that the effect of inclination decreases for metallicity values greater than the solar metallicity. Overall, we find that an exact determination of fundamental stellar parameters is crucially important for understanding stellar brightness changes.
Context. Considerable effort has been put into using light curves observed by space telescopes such as CoRoT, Kepler and TESS for determining stellar rotation periods. While rotation periods of active stars can be reliably determined, the light curves of many older and less active stars (e.g. stars similar to the Sun) are quite irregular, which hampers determination of their periods. Aims. We examine the factors causing the irregularities in stellar brightness variations and develop a method for determining rotation periods of low activity stars with irregular light curves. Methods. We extend the Spectral And Total Irradiance Reconstruction (SATIRE) approach for modelling solar brightness variations to Sun-like stars. We calculate the power spectra of stellar brightness variations for various combinations of parameters defining the surface configuration and evolution of stellar magnetic features. Results. The short lifetime of spots in comparison to the stellar rotation period as well as the interplay between spot and facular contributions to brightness variations of stars with near solar activity cause irregularities in their light curves. The power spectra of such stars often lack a peak associated with the rotation period. Nevertheless, the rotation period can still be determined by measuring the period where the concavity of the power spectrum plotted in the log-log scale changes sign, i.e. by identifying the position of the inflection point. Conclusions. The inflection point of the (log-log) power spectrum is found to be a new diagnostic for stellar rotation periods that is shown to work even in cases where the power spectrum shows no peak at the rotation rate.
It has been demonstrated that the time variability of a stars brightness at different frequencies can be used to infer its surface gravity, radius, mass, and age. With large samples of light curves now available from Kepler and K2, and upcoming surveys like TESS, we wish to quantify the overall information content of this data and identify where the information resides. As a first look into this question we ask which stellar parameters we can predict from the brightness variations in red-giant stars data and to what precision, using a data-driven model. We demonstrate that the long-cadence (30-minute) Kepler light curves for 2000 red-giant stars can be used to predict their $T_{rm eff}$ and $log g$. Our inference makes use of a data-driven model of a part of the autocorrelation function (ACF) of the light curve, where we posit a polynomial relationship between stellar parameters and the ACF pixel values. We find that this model, trained using 1000 stars, can be used to recover the temperature $T_{rm eff}$ to $<$100 K, the surface gravity to $<$ 0.1 dex, and the asteroseismic power-spectrum parameters $rm u_{max}$ and $rm Delta{ u}$ to $<11$ $mu$Hz and $<0.9$ $mu$Hz ($lesssim$ 15%). We recover $T_{rm eff}$ from range of time-lags 0.045 $<$ $T_{rm lag}$ $<$ 370 days and the $log g$, $rm u_{max}$ and $rm Delta{ u}$ from the range 0.045 $<$ $T_{rm lag}$ $<$ 35 days. We do not discover any information about stellar metallicity. The information content of the data about each parameter is empirically quantified using this method, enabling comparisons to theoretical expectations about convective granulation.
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