Statistics of stellar variability from<i>Kepler</i>

McQuillan, A.; Aigrain, S.; Roberts, Stephen

doi:10.1051/0004-6361/201016148

Cited by 64 publications

(77 citation statements)

References 25 publications

(18 reference statements)

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“…The question "is there any detectable variability" may still be relevant for the fainter stars observed in these surveys. One may be interested in identifying stars more variable than the Sun (McQuillan, Aigrain & Roberts 2012) or the ones showing periodic variability (Debosscher et al 2009(Debosscher et al , 2011) -these problems require a different set of light curve features than the ones considered here. The variability detection approach presented here will likely not be useful for space astroseismology missions like MOST (Walker et al 2003), BRITE (Weiss et al 2014, Pablo et al 2016, Popowicz et al 2017) and the upcoming transit photometry mission CHEOPS (Broeg et al 2013) as they observe (with superior accuracy) only one or few stars at a time.…”

Section: Applicability To Other Photometric Data Setsmentioning

confidence: 99%

Machine learning search for variable stars

Pashchenko

Sokolovsky

Gavras

2017

Monthly Notices of the Royal Astronomical Society

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Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SV M), k Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF) and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC) photometry (30265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SV M and RF show a higher efficiency compared to LR and kNN.

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Section: Applicability To Other Photometric Data Setsmentioning

confidence: 99%

Machine learning search for variable stars

Pashchenko

Sokolovsky

Gavras

2017

Monthly Notices of the Royal Astronomical Society

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“…The most dominant one is due to differential velocity aberration manifesting in upward and downward low-frequency trends of the light curves. Their removal is nontrivial (McQuillan et al 2012;Kinemuchi et al 2012;Petigura & Marcy 2012) since one has to decide which trends are purely instrumental and which ones are due to true stellar variability. The uncorrected data are marked as SAP_FLUX in the FITS files.…”

Section: Kepler Data and Reductionmentioning

confidence: 99%

Rotation and differential rotation of activeKeplerstars

Reinhold

Reiners

Basri

2013

A&A

353

397

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Context. The Kepler space telescope monitors more than 160 000 stars with an unprecedented precision providing the opportunity to study the rotation of thousands of stars. Aims. We present rotation periods for thousands of active stars in the Kepler field derived from Q3 data. In most cases a second period close to the rotation period was detected that we interpreted as surface differential rotation (DR). We show how the absolute and relative shear (ΔΩ and α = ΔΩ/Ω, respectively) correlate with rotation period and effective temperature. Methods. Active stars were selected from the whole sample using the range of the variability amplitude. To detect different periods in the light curves we used the Lomb-Scargle periodogram in a pre-whitening approach to achieve parameters for a global sine fit. The most dominant periods from the fit were associated to different surface rotation periods. Our purely mathematical approach is capable of detecting different periods but cannot distinguish between the physical origins of periodicity. We ascribe the existence of different periods to DR, but spot evolution could also play a role. Because of the large number of stars the period errors are estimated statistically. We thus cannot exclude the existence of false positives among our periods.Results. In our sample of 40 661 active stars we found 24 124 rotation periods P 1 between 0.5 and 45 days, with a mean of P 1 = 16.3 days. The distribution of stars with 0.5 < B − V < 1.0 and ages derived from angular momentum evolution that are younger than 300 Myr is consistent with a constant star-formation rate; the detection among older stars is incomplete probably because of our active sample selection. A second period P 2 within ±30% of the rotation period P 1 was found in 18 616 stars (77.2%). Attributing these two periods to DR we found that for active stars other than the Sun the relative shear α increases with rotation period, and slightly decreases with effective temperature. The absolute shear ΔΩ slightly increases from ΔΩ = 0.079 rad d −1 at T eff = 3500 K to ΔΩ = 0.096 rad d −1 at T eff = 6000 K. Above 6000 K, ΔΩ shows much larger scatter. The dependence of ΔΩ on rotation period is weak over a large period range. Conclusions. Latitudinal differential rotation measured for the first time in more than 18 000 stars provides a comprehensive picture of stellar surface shear. This picture is consistent with major predictions from mean-field theory, and seems to support these models. To what extent our observations are prone to false positives and selection bias has not been fully explored, and needs to be addressed using other data, including the full Kepler time coverage.

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“…The interest in solar-stellar comparisons has recently been rekindled (see e.g. McQuillan et al 2012;Basri et al 2013) by the unprecedented precision of broadband stellar photometry achieved with the launch of the Kepler (Borucki et al 2010) and Corot (Bordé et al 2003;Baglin et al 2006) space missions and the anticipation of the upcoming PLATO mission (Rauer et al 2014).…”

Section: Introductionmentioning

confidence: 99%

The role of the Fraunhofer lines in solar brightness variability

Шапиро

Solanki

Krivova

et al. 2015

A&A

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Context. The solar brightness varies on timescales from minutes to decades. A clear identification of the physical processes behind such variations is needed for developing and improving physics-based models of solar brightness variability and reconstructing solar brightness in the past. This is, in turn, important for better understanding the solar-terrestrial and solar-stellar connections. Aims. We estimate the relative contributions of the continuum, molecular, and atomic lines to the solar brightness variations on different timescales. Methods. Our approach is based on the assumption that variability of the solar brightness on timescales greater than a day is driven by the evolution of the solar surface magnetic field. We calculated the solar brightness variations employing the solar disc area coverage of magnetic features deduced from the MDI/SOHO observations. The brightness contrasts of magnetic features relative to the quiet Sun were calculated with a non-LTE radiative transfer code as functions of disc position and wavelength. By consecutive elimination of molecular and atomic lines from the radiative transfer calculations, we assessed the role of these lines in producing solar brightness variability. Results. We show that the variations in Fraunhofer lines define the amplitude of the solar brightness variability on timescales greater than a day and even the phase of the total solar irradiance variability over the 11-year cycle. We also demonstrate that molecular lines make substantial contribution to solar brightness variability on the 11-year activity cycle and centennial timescales. In particular, our model indicates that roughly a quarter of the total solar irradiance variability over the 11-year cycle originates in molecular lines. The maximum of the absolute spectral brightness variability on timescales greater than a day is associated with the CN violet system between 380 and 390 nm.

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Statistics of stellar variability fromKepler

Cited by 64 publications

References 25 publications

Machine learning search for variable stars

Machine learning search for variable stars

Rotation and differential rotation of activeKeplerstars

The role of the Fraunhofer lines in solar brightness variability

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