Obtaining measurements of chromospheric and photometric activity of stars with near-solar fundamental parameters and rotation periods is important for a better understanding of solar–stellar connection. We select a sample of 2603 stars with near-solar fundamental parameters from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)-Kepler field and use LAMOST spectra to measure their chromospheric activity and Kepler light curves to measure their photospheric activity (i.e., the amplitude of the photometric variability). While the rotation periods of 1556 of these stars could not be measured due to the low amplitude of the photometric variability and highly irregular temporal profile of light curves, 254 stars were further identified as having near-solar rotation periods. We show that stars with near-solar rotation periods have chromospheric activities that are systematically higher than stars with undetected rotation periods. Furthermore, while the solar level of photospheric and chromospheric activity appears to be typical for stars with undetected rotation periods, the Sun appears to be less active than most stars with near-solar rotation periods (both in terms of photospheric and chromospheric activity).

This paper presents a correction to the scaling relations for red giant stars using model-based masses and radii. We measure radial-mode frequencies from Kepler observations for 3642 solar-like oscillators on the red giant branch and use them to characterize the stars with grid-based modeling. We determine fundamental stellar parameters with good precision: the typical uncertainty is 4.5% for mass, 16% for age, 0.006 dex for surface gravity, and 1.7% for radius. We also achieve good accuracy for estimated masses and radii, based on a comparison with those determined for eclipsing binaries. We find a systematic offset of ∼15% in mass and ∼7% in radius between the modeling solutions and the scaling relations. Further investigation indicates that these offsets are mainly caused by a systematic bias in the Δν scaling relation: the original scaling relation underestimates the Δν value by ∼4%, on average, and it is important to correct for the surface term in the calibration. We find no significant offset in the ν max scaling relation, although a clear metallicity dependence is seen and we suggest including a metallicity term in the formulae. Lastly, we calibrate new scaling relations for red giant stars based on observed global seismic parameters, spectroscopic effective temperatures and metallicities, and modeling-inferred masses and radii.

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High precision and long-lasting Kepler data enabled us to estimate stellar properties with asteroseismology as an accurate tool. We performed asteroseismic analysis on six solar-like stars observed by the Kepler mission: KIC 6064910, KIC 6766513, KIC 7107778, KIC 10079226, KIC 10147635 and KIC 12069127. The extraction of seismic information includes two parts. First, we obtained two global asteroseismic parameters, mean large separation ∆ν and frequency of maximum power ν max , with autocorrelation function and collapsed autocorrelation function. Second, we extracted individual oscillation modes ν nl with low-l degree using a least-squares fit. Stellar grid models were built with Yale Rotating Stellar Evolution Code (YREC) to analyze stellar properties. They covered the range of M = 0.8 ∼ 1.8 M ⊙ with a step of 0.02 M ⊙ and [Fe/H] = −0.3 ∼ 0.4 dex with a step of 0.1 dex. We used a Bayesian approach to estimate stellar fundamental parameters of the six stars, under the constraints of asteroseismic parameters (∆ν, ν max ) and non-asteroseismic parameters (T eff , [Fe/H]). We discover that the six targets include five sub-giant stars with 1.2 ∼ 1.5 M ⊙ and one main-sequence star with 1.08M ⊙ , and with ages in the range of 3 ∼ 5 Gyr.

The identification of the angular degrees l of oscillation modes is essential for asteroseismology and it depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial (l = 0) mode frequencies are distributed linearly in frequency, while non-radial (l ≥ 1) modes are p–g mixed modes that have a complex distribution in frequency that increases the difficulty of identifying l. In this study, we trained a one-dimensional convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved 95 per cent accuracy for Kepler data.