arXiv subjects

Yaguang Li

Publications and source records attributed to Yaguang Li.

83 records · Page 5Linked to original sources

arXiv2020

Asteroseismology of 36 \emph{Kepler} subgiants -- I. Oscillation frequencies, linewidths and amplitudes

The presence of mixed modes makes subgiants excellent targets for asteroseismology, providing a probe for the internal structure of stars. Here we study 36 \emph{Kepler} subgiants with solar-like oscillations and report their oscillation mode parameters. We performed a so-called peakbagging exercise, i.e. estimating oscillation mode frequencies, linewidths, and amplitudes with a power spectrum model, fitted in the Bayesian framework and sampled with a Markov Chain Monte Carlo algorithm. The uncertainties of the mode frequencies have a median value of 0.180 $μ$Hz. We obtained seismic parameters from the peakbagging, analysed their correlation with stellar parameters, and examined against scaling relations. The behaviour of seismic parameters (e.g. $Δν$, $ν_{\rm max}$, $ε_p$) is in general consistent with theoretical predictions. We presented the observational p--g diagrams: $γ_1$--$Δν$ for early subgiants and $ΔΠ_1$--$Δν$ for late subgiants, and demonstrate their capability to estimate stellar mass. We also found a $\log g$ dependence on the linewidths and a mass dependence on the oscillation amplitudes and the widths of oscillation excess. This sample will be valuable constraints for modelling stars and studying mode physics such as excitation and damping.

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arXiv2020

Solar-type Stars Observed by LAMOST and Kepler

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).

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arXiv2020

DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis

Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence. Existing work on trajectory clustering often relies on similarity measurements that utilize raw spatial and/or temporal information of trajectories. These measures are incapable of identifying similar moving behaviors that exhibit varying spatio-temporal scales of movement. In addition, the expense of labeling massive trajectory data is a barrier to supervised learning models. To address these challenges, we propose an unsupervised neural approach for mobility behavior clustering, called the Deep Embedded TrajEctory ClusTering network (DETECT). DETECT operates in three parts: first it transforms the trajectories by summarizing their critical parts and augmenting them with context derived from their geographical locality (e.g., using POIs from gazetteers). In the second part, it learns a powerful representation of trajectories in the latent space of behaviors, thus enabling a clustering function (such as $k$-means) to be applied. Finally, a clustering oriented loss is directly built on the embedded features to jointly perform feature refinement and cluster assignment, thus improving separability between mobility behaviors. Exhaustive quantitative and qualitative experiments on two real-world datasets demonstrate the effectiveness of our approach for mobility behavior analyses.

cs.LG↗

arXiv2020

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

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 Rhk+, for 59,816 stars from LAMOST spectra in the LAMOST-Kepler program, and photospheric index, Reff, for 5575 stars from Kepler light curves. The log Reff shows positive correlation with log Rhk+. We estimate the power-law indexes between Reff and Rhk+ 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.

astro-ph.SR↗

arXiv2020

Population synthesis of helium white dwarf--red giant star mergers and the formation of lithium-rich giants and carbon stars

The formation histories of lithium-rich and carbon-rich red giants are not yet understood. It has been proposed that the merger of a helium-core white dwarf with a red giant branch star might provide a solution. We have computed an extended grid of post-merger evolution models and combined these with predictions of binary star population synthesis. The results strongly support the proposal that the merger of a helium white dwarf with a red giant branch star can provide the progenitors of both lithium-rich red clump stars and early-R carbon stars. The distribution of post-merger models in $T_{\rm eff}$, $\log g$, $\log L$, the surface abundances of lithium and carbon, and the predicted space densities agree well with the observed distributions of these parameters for Li-rich and early-R stars in the Galaxy.

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arXiv2019

Ages and Masses of 0.64 million Red Giant Branch stars from the LAMOST Galactic Spectroscopic Survey

We present a catalog of stellar age and mass estimates for a sample of 640\,986 red giant branch (RGB) stars of the Galactic disk from the LAMOST Galactic Spectroscopic Survey (DR4). The RGB stars are distinguished from the red clump stars utilizing period spacing derived from the spectra with a machine learning method based on kernel principal component analysis (KPCA). Cross-validation suggests our method is capable of distinguishing RC from RGB stars with only 2 per cent contamination rate for stars with signal-to-noise ratio (SNR) higher than 50. The age and mass of these RGB stars are determined from their LAMOST spectra with KPCA method by taking the LAMOST - $Kepler$ giant stars having asteroseismic parameters and the LAMOST-TGAS sub-giant stars based on isochrones as training sets. Examinations suggest that the age and mass estimates of our RGB sample stars with SNR $>$ 30 have a median error of 30 per cent and 10 per cent, respectively. Stellar ages are found to exhibit positive vertical and negative radial gradients across the disk, and the age structure of the disk is strongly flared across the whole disk of $6<R<13$\,kpc. The data set demonstrates good correlations among stellar age, [Fe/H] and [$α$/Fe]. There are two separate sequences in the [Fe/H] -- [$α$/Fe] plane: a high--$α$ sequence with stars older than $\sim$\,8\,Gyr and a low--$α$ sequence composed of stars with ages covering the whole range of possible ages of stars. We also examine relations between age and kinematic parameters derived from the Gaia DR2 parallax and proper motions. Both the median value and dispersion of the orbital eccentricity are found to increase with age. The vertical angular momentum is found to fairly smoothly decrease with age from 2 to 12\,Gyr, with a rate of about $-$50\,kpc\,km\,s$^{-1}$\,Gyr$^{-1}$. A full table of the catalog is public available online.

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arXiv2018

Multi-threshold Change Plane Model: Estimation Theory and Applications in Subgroup Identification

We propose a multi-threshold change plane regression model which naturally partitions the observed subjects into subgroups with different covariate effects. The underlying grouping variable is a linear function of covariates and thus multiple thresholds form parallel change planes in the covariate space. We contribute a novel 2-stage approach to estimate the number of subgroups, the location of thresholds and all other regression parameters. In the first stage we adopt a group selection principle to consistently identify the number of subgroups, while in the second stage change point locations and model parameter estimates are refined by a penalized induced smoothing technique. Our procedure allows sparse solutions for relatively moderate- or high-dimensional covariates. We further establish the asymptotic properties of our proposed estimators under appropriate technical conditions. We evaluate the performance of the proposed methods by simulation studies and provide illustration using two medical data. Our proposal for subgroup identification may lead to an immediate application in personalized medicine.

stat.ME↗

arXiv2018

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.

cs.LG↗

arXiv2018

Asteroseismology of KIC 7107778: a binary comprising almost identical subgiants

We analyze an asteroseismic binary system: KIC 7107778, a non-eclipsing, unresolved target, with solar-like oscillations in both components. We used \emph{Kepler} short cadence time series spanning nearly two years to obtain the power spectrum. Oscillation mode parameters were determined using Bayesian inference and a nested sampling Monte Carlo algorithm with the \texttt{DIAMONDS} package. The power profiles of the two components fully overlap, indicating their close similarity. We modelled the two stars with \texttt{MESA} and calculated oscillation frequencies with \texttt{GYRE}. Stellar fundamental parameters (mass, radius and age) were estimated by grid modelling with atmospheric parameters and the oscillation frequencies of $l=0,2$ modes as constraints. Most $l=1$ mixed modes were identified with models searched using a bisection method. Stellar parameters for the two sub-giant stars are: $M_A=1.42\pm0.06$ $M_{\astrosun}$, $M_B=1.39\pm0.03$ $M_{\astrosun}$, $R_A=2.93\pm0.05$ $R_{\astrosun}$, $R_B=2.76\pm0.04$ $R_{\astrosun}$, $t_A=3.32\pm0.54$ Gyr and $t_B=3.51\pm0.33$ Gyr. The mass difference of the system is $\sim$1\%. The results confirm their simultaneous birth and evolution, as is expected from binary formation. KIC 7107778 comprises almost identical twins, and is the first asteroseismic sub-giant binary to be detected.

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arXiv2017

Mass and Age of Red Giant Branch Stars Observed with LAMOST and \emph{Kepler}

Obtaining accurate and precise masses and ages for large numbers of giant stars is of great importance for unraveling the assemblage history of the Galaxy. In this paper, we estimate masses and ages of 6940 red giant branch (RGB) stars with asteroseismic parameters deduced from \emph{Kepler} photometry and stellar atmospheric parameters derived from LAMOST spectra. The typical uncertainties of mass is a few per cent, and that of age is $\sim$\,20 per cent. The sample stars reveal two separate sequences in the age -- [$α$/Fe] relation -- a high--$α$ sequence with stars older than $\sim$\,8\,Gyr and a low--$α$ sequence composed of stars with ages ranging from younger than 1\,Gyr to older than 11\,Gyr. We further investigate the feasibility of deducing ages and masses directly from LAMOST spectra with a machine learning method based on kernel based principal component analysis, taking a sub-sample of these RGB stars as a training data set. We demonstrate that ages thus derived achieve an accuracy of $\sim$\,24 per cent. We also explored the feasibility of estimating ages and masses based on the spectroscopically measured carbon and nitrogen abundances. The results are quite satisfactory and significantly improved compared to the previous studies.

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arXiv2017

Seismic diagnostics of solar-like oscillating stars

High precision and long-lasting \emph{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 \emph{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 $Δν$ and frequency of maximum power $ν_{\rm{max}}$, 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 $M=0.8\sim1.8$ $M_{\odot}$ with a step of 0.02 $M_{\odot}$ and $\rm{[Fe/H]}=-0.3\sim0.4$ $\rm{dex}$ with a step of 0.1 $\rm{dex}$. We used a Bayesian approach to estimate stellar fundamental parameters of the six stars, under the constraints of asteroseismic parameters ($Δν$, $ν_{\rm{max}}$) and non-asteroseismic parameters ($T_{\rm{eff}}$, $\rm{[Fe/H]}$). We discover that the six targets include five sub-giant stars with $1.2\sim1.5$ $M_{\odot}$ and one main-sequence star with $1.08M_{\odot}$, and with ages in the range of $3\sim5$ $\rm{Gyr}$.

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