Inflection point in the power spectrum of stellar brightness variations

Шапиро, А. И.; Amazo-Gómez, E. M.; Krivova, N. A.; Solanki, S. K.

doi:10.1051/0004-6361/201936018

Cited by 36 publications

(57 citation statements)

References 69 publications

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“…2.5 where the double peak structure is more easily visible). Shapiro et al (2020) explained such a double-peak structure by the cancellation of spot and facular contribution to the rotation signal. Witzke et al (2020a) further analysed the connection between the power spectrum profile and detectability of the rotation period.…”

Section: Solar Brightness Variations As Seen By An Ecliptic Bound Observer 231 Tsi Variability During Activity Cycles Of Different Strengmentioning

confidence: 98%

“…Let us also check how the different free parameters of our model affect the power spectrum of solar brightness variations returned by the model. The effects of the spot de-2.3 Solar brightness variations as seen by an ecliptic bound observer With decreasing spot decay rate, R d , the overall area coverage of the spots is increasing, which affects timescales longer than about 10 days (Shapiro et al 2020). The prominent peak at the rotation period for the R d = 26.5 MSH day −1 is a result of the long lifetime of the spots.…”

Section: Model Parametersmentioning

confidence: 99%

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Power spectra of solar brightness variations at various inclinations

Nèmec

Шапиро

Krivova

et al. 2020

A&A

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Context. 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. Aims. 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. Methods. 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. Results. 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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Section: Solar Brightness Variations As Seen By An Ecliptic Bound Observer 231 Tsi Variability During Activity Cycles Of Different Strengmentioning

confidence: 98%

Section: Model Parametersmentioning

confidence: 99%

Power spectra of solar brightness variations at various inclinations

Nèmec

Шапиро

Krivova

et al. 2020

A&A

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“…The determination of rotation periods of stars with activity levels similar to those of our Sun is a challenging task, even when using high-quality data from space-borne photometric missions. In Shapiro et al (2020), we proposed the GPS method specifically aimed at the determination of periods in old inactive stars, like our Sun. The main idea of the method is to calculate the gradient of the power spectrum of stellar brightness variations and identify the inflection point, which is the point where the concavity of the power spectrum changes its sign.…”

Section: Discussion and Summarymentioning

confidence: 99%

Inflection point in the power spectrum of stellar brightness variations

Amazo-Gómez

Шапиро

Solanki

et al. 2020

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Context. Young and active stars generally have regular, almost sinusoidal, patterns of variability attributed to their rotation, while the majority of older and less active stars, including the Sun, have more complex and non-regular light curves, which do not have clear rotational-modulation signals. Consequently, the rotation periods have been successfully determined only for a small fraction of the Sun-like stars (mainly the active ones) observed by transit-based planet-hunting missions, such as CoRoT, Kepler, and TESS. This suggests that only a small fraction of such systems have been properly identified as solar-like analogues. Aims. We aim to apply a new method of determining rotation periods of low-activity stars, such as the Sun. The method is based on calculating the gradient of the power spectrum (GPS) of stellar brightness variations and identifying a tell-tale inflection point in the spectrum. The rotation frequency is then proportional to the frequency of that inflection point. In this paper, we compare this GPS method to already-available photometric records of the Sun. Methods. We applied GPS, auto-correlation functions, Lomb-Scargle periodograms, and wavelet analyses to the total solar irradiance (TSI) time series obtained from the Total Irradiance Monitor on the Solar Radiation and Climate Experiment and the Variability of solar IRradiance and Gravity Oscillations experiment on the SOlar and Heliospheric Observatory missions. We analysed the performance of all methods at various levels of solar activity. Results. We show that the GPS method returns accurate values of solar rotation independently of the level of solar activity. In particular, it performs well during periods of high solar activity, when TSI variability displays an irregular pattern, and other methods fail. Furthermore, we show that the GPS and light curve skewness can give constraints on facular and spot contributions to brightness variability. Conclusions. Our results suggest that the GPS method can successfully determine the rotational periods of stars with both regular and non-regular light curves.

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“…Surface rotation periods can be studied with different methods, namely: periodogram analysis (e.g., Reinhold et al 2013;Nielsen et al 2013); Gaussian processes (e.g., Angus et al 2018); gradient power spectrum analysis (e.g., Shapiro et al 2020;Amazo-Gómez et al 2020); autocorrelation function (ACF, e.g., McQuillan et al 2013McQuillan et al , 2014; time-period analysis based on wavelets (e.g., Mathur et al 2010;García et al 2014a); or with a combination of different diagnostics such as the composite spectrum (CS, e.g., Ceillier et al 2016Ceillier et al , 2017Santos et al 2019). Using artificial data, Aigrain et al (2015) compared the performance of different pipelines used to determine the rotation.…”

Section: Methodology To Extract P Rotmentioning

confidence: 99%

ROOSTER: a machine-learning analysis tool for Kepler stellar rotation periods

Breton

Santos

Bugnet

et al. 2021

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In order to understand stellar evolution, it is crucial to efficiently determine stellar surface rotation periods. Indeed, while they are of great importance in stellar models, angular momentum transport processes inside stars are still poorly understood today. Surface rotation, which is linked to the age of the star, is one of the constraints needed to improve the way those processes are modelled. Statistics of the surface rotation periods for a large sample of stars of different spectral types are thus necessary. An efficient tool to automatically determine reliable rotation periods is needed when dealing with large samples of stellar photometric datasets. The objective of this work is to develop such a tool. For this purpose, machine learning classifiers constitute relevant bases to build our new methodology. Random forest learning abilities are exploited to automate the extraction of rotation periods in Kepler light curves. Rotation periods and complementary parameters are obtained via three different methods: a wavelet analysis, the autocorrelation function of the light curve, and the composite spectrum. We trained three different classifiers: one to detect if rotational modulations are present in the light curve, one to flag close binary or classical pulsators candidates that can bias our rotation period determination, and finally one classifier to provide the final rotation period. We tested our machine learning pipeline on 23 431 stars of the Kepler K and M dwarf reference rotation catalogue for which 60% of the stars have been visually inspected. For the sample of 21 707 stars where all the input parameters are provided to the algorithm, 94.2% of them are correctly classified (as rotating or not). Among the stars that have a rotation period in the reference catalogue, the machine learning provides a period that agrees within 10% of the reference value for 95.3% of the stars. Moreover, the yield of correct rotation periods is raised to 99.5% after visually inspecting 25.2% of the stars. Over the two main analysis steps, rotation classification and period selection, the pipeline yields a global agreement with the reference values of 92.1% and 96.9% before and after visual inspection. Random forest classifiers are efficient tools to determine reliable rotation periods in large samples of stars. The methodology presented here could be easily adapted to extract surface rotation periods for stars with different spectral types or observed by other instruments such as K2, TESS or by PLATO in the near future.

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Inflection point in the power spectrum of stellar brightness variations

Cited by 36 publications

References 69 publications

Power spectra of solar brightness variations at various inclinations

Power spectra of solar brightness variations at various inclinations

Inflection point in the power spectrum of stellar brightness variations

ROOSTER: a machine-learning analysis tool for Kepler stellar rotation periods

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