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We have developed a method to estimate surface gravity (log g) from light curves by measuring the granulation background, similar to the "flicker" method by Bastien et al. (2016) but working in the Fourier power spectrum. We calibrated the…

太阳与恒星天体物理 · 物理学 2018-08-01 Durlabh Pande , Timothy R. Bedding , Daniel Huber , Hans Kjeldsen

It has been demonstrated that the time variability of a star's 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…

Asteroseismic data can be used to determine surface gravities with precisions of < 0.05 dex by using the global seismic quantities Deltanu and nu_max along with Teff and [Fe/H]. Surface gravity is also one of the four stellar properties to…

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 for understanding the…

太阳与恒星天体物理 · 物理学 2020-05-21 Kirsten Blancato , Melissa Ness , Daniel Huber , Yuxi Lu , Ruth Angus

In Bastien et al. (2013) we found that high quality light curves, such as those obtained by Kepler, may be used to measure stellar surface gravity via granulation-driven light curve "flicker". Here, we update and extend the relation…

太阳与恒星天体物理 · 物理学 2016-02-17 Fabienne A. Bastien , Keivan G. Stassun , Gibor Basri , Joshua Pepper

Asteroseismology is a powerful tool to precisely determine the evolutionary status and fundamental properties of stars. With the unprecedented precision and nearly continuous photometric data acquired by the NASA Kepler mission, parameters…

太阳与恒星天体物理 · 物理学 2016-06-22 Liang Wang , Wei Wang , Yue Wu , Gang Zhao , Yinbi Li , Ali Luo , Chao Liu , Yong Zhang , Yonghui Hou , Yuefei Wang

We demonstrate a new procedure to derive accurate and precise surface gravities from high resolution spectra without the use of external constraints. Our analysis utilizes Spectroscopy Made Easy (SME) with robust spectral line constraints…

太阳与恒星天体物理 · 物理学 2015-06-03 John M. Brewer , Debra A. Fischer , Sarbani Basu , Jeff A. Valenti , Nikolai Piskunov

Thanks to missions like Kepler and TESS, we now have access to tens of thousands of high precision, fast cadence, and long baseline stellar photometric observations. In principle, these light curves encode a vast amount of information about…

太阳与恒星天体物理 · 物理学 2021-09-08 Rodrigo Luger , Daniel Foreman-Mackey , Christina Hedges , David W. Hogg

We present a methodology for the determination of empirical masses of single stars through the combination of three direct observables with Gaia and TESS: (i) the surface gravity via granulation-driven variations in the TESS light curve,…

太阳与恒星天体物理 · 物理学 2017-12-27 Keivan G. Stassun , Enrico Corsaro , Joshua Pepper , Scott Gaudi

Precise spectroscopic classification of planet hosts is an important tool of exoplanet research at both the population and individual system level. In the era of large-scale surveys, data-driven methods offer an efficient approach to…

太阳与恒星天体物理 · 物理学 2024-10-08 Isabel Angelo , Megan Bedell , Erik Petigura , Melissa Ness

Distances from the Gaia mission will no doubt improve our understanding of stellar physics by providing an excellent constraint on the luminosity of the star. However, it is also clear that high precision stellar properties from, for…

太阳与恒星天体物理 · 物理学 2012-12-07 O. L. Creevey , F. Thévenin

Reliable estimation of stellar surface gravity (log $g$) for a large sample is crucial for evaluating stellar evolution models and understanding galactic structure; However, it is not easy to accomplish due to the difficulty in gathering a…

Several large stellar spectroscopic surveys are producing overwhelming amounts of data that can be used for determining stellar atmospheric parameters and chemical abundances. Nonetheless, the accuracy achieved in the derived astrophysical…

太阳与恒星天体物理 · 物理学 2021-06-30 Riano E. Giribaldi , André R. da Silva , Rodolfo Smiljanic , Deysi Cornejo Espinoza

Estimating stellar masses for billions of galaxies in upcoming surveys requires methods that are both accurate and computationally efficient. We present a new approach using symbolic regression trained on a simulation to derive simple,…

Stellar granulation arises from near-surface convection and is imprinted in stellar photometric time series, yet links between granulation observables and fundamental stellar properties remain underexploited. We aim to establish a…

太阳与恒星天体物理 · 物理学 2026-05-27 Jens R. Larsen , Mia S. Lundkvist , Guy R. Davies , Martin B. Nielsen

Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…

(Abridged) We present the first APOKASC catalog of spectroscopic and asteroseismic data for 415 dwarfs and subgiants. Asteroseismic data have been obtained by Kepler in short cadence. The spectroscopic parameters are based on spectra taken…

(abridged) We present an analysis of high resolution optical spectra for a sample of very young, mid- to late M, low-mass stellar and substellar objects in Upper Sco and Taurus. Effective temperatures (T_eff) and surface gravities are…

天体物理学 · 物理学 2009-11-10 Subhanjoy Mohanty , Gibor Basri , Ray Jayawardhana , France Allard , Peter Hauschildt , David Ardila

Large Galactic stellar surveys and new generations of stellar atmosphere models and spectral line formation computations need to be subjected to careful calibration and validation and to benchmark tests. We focus on cool stars and aim at…

太阳与恒星天体物理 · 物理学 2015-10-07 U. Heiter , P. Jofré , B. Gustafsson , A. J. Korn , C. Soubiran , F. Thévenin

We introduce Astroconformer, a Transformer-based model to analyze stellar light curves from the Kepler mission. We demonstrate that Astrconformer can robustly infer the stellar surface gravity as a supervised task. Importantly, as…

太阳与恒星天体物理 · 物理学 2022-07-07 Jiashu Pan , Yuan-Sen Ting , Jie Yu
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