The Swan: Data-driven Inference of Stellar Surface Gravities for Cool Stars from Photometric Light Curves

Sayeed, Maryum; Huber, Daniel; Wheeler, Adam; Ness, Melissa

doi:10.3847/1538-3881/abdf4c

Cited by 10 publications

(10 citation statements)

References 95 publications

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“…Each local model is constructed by defining a neighborhood around each object in some data space (e.g. see Sayeed et al 2021, for an example of this using Kepler data). This approach takes on the following steps for our N = 27, 000 stars, where the parameters that we select as predictors are Y = (T eff , log g, [Mg/H], [Fe/H]) and our goal is to predict eight abundances X = (Si, O, Ca, Ti, Ni, Al, Mn, Cr):…”

Section: Methodsmentioning

confidence: 99%

The homogeneity of the star forming environment of the Milky Way disk over time

Ness,

Wheeler,

McKinnon

et al. 2021

Preprint

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Stellar abundances and ages afford the means to link chemical enrichment to galactic formation. In the Milky Way, individual element abundances show tight correlations with age, which vary in slope across ([Fe/H]-[α/Fe]). Here, we step from characterising abundances as measures of age, to understanding how abundances trace properties of stellar birth-environment in the disk over time. Using measurements from ∼27,000 APOGEE stars (R=22,500, SNR>200), we build simple local linear models to predict a sample of elements (X = Si, O, Ca, Ti, Ni, Al, Mn, Cr) using (Fe, Mg) abundances alone, as fiducial tracers of supernovae production channels. Given [Fe/H] and [Mg/H], we predict these elements, [X/H], to about double the uncertainty of their measurements. The intrinsic dispersion, after subtracting measurement errors in quadrature is ≈ 0.015 − 0.04 dex. The residuals of the prediction (measurement − model) for each element demonstrate that each element has an individual link to birth properties at fixed (Fe, Mg). Residuals from primarily massive-star supernovae (i.e. Si, O, Al) partially correlate with guiding radius. Residuals from primarily supernovae Ia (i.e. Mn, Ni) partially correlate with age. A fraction of the intrinsic scatter that persists at fixed (Fe, Mg), however, after accounting for correlations, does not appear to further discriminate between birth properties that can be traced with present-day measurements. Presumably, this is because the residuals are also, in part, a measure of the typical (in)-homogeneity of the disk's stellar birth environments, previously inferred only using open-cluster systems. Our study implies at fixed birth radius and time, there is a median scatter of ≈ 0.01 − 0.015 dex in elements generated in supernovae sources.

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Section: Methodsmentioning

confidence: 99%

The homogeneity of the star forming environment of the Milky Way disk over time

Ness,

Wheeler,

McKinnon

et al. 2021

Preprint

Self Cite

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“…However, the approach requires that flicker timescales be temporally resolved, which is not always the case for Kepler long-cadence data (the data available for the vast majority of Kepler targets), where the 30 min cadence fails to resolve, for instance, G-dwarf granular timescales on the order of 10 min. An additional approach that has been demonstrated is to use machine learning to extract stellar parameters such as surface gravity from the power spectra of stellar variability (Sayeed et al 2020).…”

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confidence: 99%

Magnetic Activity of F-, G-, and K-type Stars in the LAMOST–Kepler Field

Zhang

et al. 2020

ApJS

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Monitoring chromospheric and photospheric indexes of magnetic activity can provide valuable information, especially the interaction between different parts of the atmosphere and their response to magnetic fields. We extract chromospheric indexes, S and + R HK , for 59,816 stars from LAMOST spectra in the LAMOST-Kepler program, and photospheric index, R eff , for 5575 stars from Kepler light curves. The log R eff shows positive correlation with log + R HK . We estimate the power-law indexes between R eff and + R HK for F-, G-, and K-type stars, respectively. We also confirm the dependence of both chromospheric and photospheric activity on stellar rotation. Ca II H and K emissions and photospheric variations generally decrease with increasing rotation periods for stars with rotation periods exceeding a few days. The power-law indexes in exponential decay regimes show different characteristics in the two activity-rotation relations. The updated largest sample including the activity proxies and reported rotation periods provides more information to understand the magnetic activity for cool stars.

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“…Indeed, as the relation between the time-domain variability and parameters that describe this flux change over the range of the parameter space, a very flexible and therefore powerful model such as a neural network is very appealing, despite difficulties in interpretability. A simple but flexible local linear approach, however, may mitigate the challenge of interpretability (Sayeed et al 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Trained on ∼4 yr baseline data, Ness et al (2018) find the variance of their log g estimator to be <0.1 dex and the variance of their T eff estimator to be <100 K, with the information required to learn these properties being contained in ACF lags up to 35 days and 370 days, respectively, for log g and T eff . Taking a similar approach, Sayeed et al (2021) learn a local linear regression model between the power density at each frequency of smoothed Kepler power spectra and stellar properties. For upper-main-sequence and RGB stars that do not exhibit rotation, Sayeed et al (2021) learn a log g estimator with a variance <0.07 dex based on the 10 nearest neighbors in the frequency domain of the training set.…”

Section: Introductionmentioning

confidence: 99%

“…Taking a similar approach, Sayeed et al (2021) learn a local linear regression model between the power density at each frequency of smoothed Kepler power spectra and stellar properties. For upper-main-sequence and RGB stars that do not exhibit rotation, Sayeed et al (2021) learn a log g estimator with a variance <0.07 dex based on the 10 nearest neighbors in the frequency domain of the training set. Neural networks have also been implemented for RGB asteroseismology.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven Derivation of Stellar Properties from Photometric Time Series Data Using Convolutional Neural Networks

Blancato

Ness²,

Huber³

et al. 2022

ApJ

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Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar observations with stellar physics and to understand the distribution of stellar populations within the context of galaxy formation. Numerous ongoing and upcoming missions are charting brightness fluctuations of stars over time, which encode information about physical processes such as the rotation period, evolutionary state (such as effective temperature and surface gravity), and mass (via asteroseismic parameters). Here, we explore how well we can predict these stellar properties, across different evolutionary states, using only photometric time-series data. To do this, we implement a convolutional neural network, and with data-driven modeling we predict stellar properties from light curves of various baselines and cadences. Based on a single quarter of Kepler data, we recover the stellar properties, including the surface gravity for red giant stars (with an uncertainty of ≲0.06 dex) and rotation period for main-sequence stars (with an uncertainty of ≲5.2 days, and unbiased from ≈5 to 40 days). Shortening the Kepler data to a 27 days Transiting Exoplanet Survey Satellite–like baseline, we recover the stellar properties with a small decrease in precision, ∼0.07 for log g and ∼5.5 days for P rot, unbiased from ≈5 to 35 days. Our flexible data-driven approach leverages the full information content of the data, requires minimal or no feature engineering, and can be generalized to other surveys and data sets. This has the potential to provide stellar property estimates for many millions of stars in current and future surveys.

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The Swan: Data-driven Inference of Stellar Surface Gravities for Cool Stars from Photometric Light Curves

Cited by 10 publications

References 95 publications

The homogeneity of the star forming environment of the Milky Way disk over time

The homogeneity of the star forming environment of the Milky Way disk over time

Magnetic Activity of F-, G-, and K-type Stars in the LAMOST–Kepler Field

Data-driven Derivation of Stellar Properties from Photometric Time Series Data Using Convolutional Neural Networks

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