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Register a new userTesting the recovery of stellar rotation signals from Kepler light curves using a blind hare-and-hounds exercise
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We present the results of a blind exercise to test the recoverability of stellar rotation and differential rotation in Kepler light curves. The simulated light curves lasted 1000 days and included activity cycles, Sun-like butterfly patterns, differential rotation and spot evolution. The range of rotation periods, activity levels and spot lifetime were chosen to be representative of the Kepler data of solar like stars. Of the 1000 simulated light curves, 770 were injected into actual quiescent Kepler light curves to simulate Kepler noise. The test also included five 1000-day segments of the Suns total irradiance variations at different points in the Suns activity cycle. Five teams took part in the blind exercise, plus two teams who participated after the content of the light curves had been released. The methods used included Lomb-Scargle periodograms and variants thereof, auto-correlation function, and wavelet-based analyses, plus spot modelling to search for differential rotation. The results show that the `overall period is well recovered for stars exhibiting low and moderate activity levels. Most teams reported values within 10% of the true value in 70% of the cases. There was, however, little correlation between the reported and simulated values of the differential rotation shear, suggesting that differential rotation studies based on full-disk light curves alone need to be treated with caution, at least for solar-type stars. The simulated light curves and associated parameters are available online for the community to test their own methods.
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Context: Detailed oscillation spectra comprising individual frequencies for numerous solar-type stars and red giants are or will become available. These data can lead to a precise characterisation of stars. Aims: Our goal is to test and compare different methods for obtaining stellar properties from oscillation frequencies and spectroscopic constraints, in order to evaluate their accuracy and the reliability of the error bars. Methods: In the context of the SpaceInn network, we carried out a hare-and-hounds exercise in which one group produced observed oscillation spectra for 10 artificial solar-type stars, and various groups characterised these stars using either forward modelling or acoustic glitch signatures. Results: Results based on the forward modelling approach were accurate to 1.5 % (radius), 3.9 % (mass), 23 % (age), 1.5 % (surface gravity), and 1.8 % (mean density). For the two 1 Msun stellar targets, the accuracy on the age is better than 10 % thereby satisfying PLATO 2.0 requirements. The average accuracies for the acoustic radii of the base of the convection zone, the He II ionisation, and the Gamma_1 peak were 17 %, 2.4 %, and 1.9 %, respectively. Glitch fitting analysis seemed to be affected by aliasing problems for some of the targets. Conclusions: Forward modelling is the most accurate approach, but needs to be complemented by model-independent results from, e.g., glitch analysis. Furthermore, global optimisation algorithms provide more robust error bars.
We report on initial results from the first phase of Exercise 1 of the asteroFLAG hare and hounds. The asteroFLAG group is helping to prepare for the asteroseismology component of NASAs Kepler mission, and the first phase of Exercise 1 is concerned with testing extraction of estimates of the large and small frequency spacings of the low-degree p modes from Kepler-like artificial data. These seismic frequency spacings will provide key input for complementing the exoplanet search data.
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.
A large fraction of cool, low-mass stars exhibit brightness fluctuations that arise from a combination of convective granulation, acoustic oscillations, magnetic activity, and stellar rotation. Much of the short-timescale variability takes the form of stochastic noise, whose presence may limit the progress of extrasolar planet detection and characterization. In order to lay the groundwork for extracting useful information from these quasi-random signals, we focus on the origin of the granulation-driven component of the variability. We apply existing theoretical scaling relations to predict the star-integrated variability amplitudes for 508 stars with photometric light curves measured by the Kepler mission. We also derive an empirical correction factor that aims to account for the suppression of convection in F-dwarf stars with magnetic activity and shallow convection zones. So that we can make predictions of specific observational quantities, we performed Monte Carlo simulations of granulation light curves using a Lorentzian power spectrum. These simulations allowed us to reproduce the so-called flicker floor (i.e., a lower bound in the relationship between the full light-curve range and power in short-timescale fluctuations) that was found in the Kepler data. The Monte Carlo model also enabled us to convert the modeled fluctuation variance into a flicker amplitude directly comparable with observations. When the magnetic suppression factor described above is applied, the model reproduces the observed correlation between stellar surface gravity and flicker amplitude. Observationally validated models like these provide new and complementary evidence for a possible impact of magnetic activity on the properties of near-surface convection.
The Wavelength-Oriented Microwave Background Analysis Team (WOMBAT) is constructing microwave skymaps which will be more realistic than previous simulations. Our foreground models represent a considerable improvement: where spatial templates are available for a given foreground, we predict the flux and spectral index of that component at each place on the sky and estimate the uncertainties in these quantities. We will produce maps containing simulated Cosmic Microwave Background anisotropies combined with all major expected foreground components. The simulated maps will be provided to the cosmology community as the WOMBAT Challenge, a hounds and hares exercise where such maps can be analyzed to extract cosmological parameters by scientists who are unaware of their input values. This exercise will test the efficacy of current foreground subtraction, power spectrum analysis, and parameter estimation techniques and will help identify the areas most in need of progress.
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