arXiv subjects

Yin-Bi Li

Publications and source records attributed to Yin-Bi Li.

23 records · Page 2Linked to original sources

arXiv2019

Metallic-Line Stars Identified from Low Resolution Spectra of LAMOST DR5

LAMOST DR5 released more than 200,000 low resolution spectra of early-type stars with S/N>50. Searching for metallic-line (Am) stars in such a large database and study of their statistical properties are presented in this paper. Six machine learning algorithms were experimented with using known Am spectra, and both the empirical criteria method(Hou et al. 2015) and the MKCLASS package(Gray et al. 2016) were also investigated. Comparing their performance, the random forest (RF) algorithm won, not only because RF has high successful rate but also it can derives and ranks features. Then the RF was applied to the early type stars of DR5, and 15,269 Am candidates were picked out. Manual identification was conducted based on the spectral features derived from the RF algorithm and verified by experts. After manual identification, 9,372 Am stars and 1,131 Ap candidates were compiled into a catalog. Statistical studies were conducted including temperature distribution, space distribution, and infrared photometry. The spectral types of Am stars are mainly between F0 and A4 with a peak around A7, which is similar to previous works. With the Gaia distances, we calculated the vertical height Z from the Galactic plane for each Am star. The distribution of Z suggests that the incidence rate of Am stars shows a descending gradient with increasing jZj. On the other hand, Am stars do not show a noteworthy pattern in the infrared band. As wavelength gets longer, the infrared excess of Am stars decreases, until little or no excess in W1 and W2 bands.

astro-ph.SR↗

arXiv2018

A new Hyper-Runaway star discovered from LAMOST and GAIA: ejected almost in the galactic rotation direction

In this paper, we report the discovery of a new late-B type unbound hyper-runaway star (LAMOST-HVS4) from the LAMOST spectroscopic surveys. According to its atmospheric parameters, it is either a B-type main sequence (MS) star or a blue horizontal branch (BHB) star. Its Galactocentric distance and velocity are 30.3 +/- 1.6 kpc and 586 +/- 7 km/s if it is an MS star, and they are 13.2 +/- 3.7 kpc and 590 +/- 7 km/s if a BHB star. We track its trajectories back, and find that the trajectories intersect with the Galactic disk and the Galactic center lies outside of the intersection region at the 3 sigma confidence level. We investigate a number of mechanisms that could be responsible for the ejection of the star, and find that it is probably ejected from the Galactic disk by supernova explosion or multiple-body interactions in dense young stellar clusters.

astro-ph.SR↗

arXiv2018

Spectral feature extraction for DB white dwarfs through machine learning applied to new discoveries in the SDSS DR12 and DR14

Using a machine learning (ML) method, we mine DB white dwarfs (DBWDs) from the Sloan Digital Sky Survey (SDSS) Data Release (DR) 12 and DR14. The ML method consists of two parts: feature extraction and classification. The least absolute shrinkage and selection operator (LASSO) is used for the spectral feature extraction by comparing high quality data of a positive sample group with negative sample groups. In both the training and testing sets, the positive sample group is composed of a selection of 300 known DBWDs, while the negative sample groups are obtained from all types of SDSS spectra. In the space of the LASSO detected features, a support vector machine is then employed to build classifiers that are used to separate the DBWDs from the non DBWDs for each individual type. Depending on the classifiers, the DBWD candidates are selected from the entire SDSS dataset. After visual inspection, 2808 spectra (2029 objects) are spectroscopically confirmed. By checking the samples with the literature, there are 58 objects with 60 spectra that are newly identified, including a newly discovered AM CVn. Finally, we measure their effective temperatures (Teff), surface gravities (log g), and radial velocities, before compiling them into a catalog.

astro-ph.SR↗

arXiv2018

Carbon stars identified from LAMOST DR4 using Machine Learning

In this work, we present a catalog of 2651 carbon stars from the fourth Data Release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). Using an efficient machine-learning algorithm, we find out these stars from more than seven million spectra. As a by-product, 17 carbon-enhanced metal-poor (CEMP) turnoff star candidates are also reported in this paper, and they are preliminarily identified by their atmospheric parameters. Except for 176 stars that could not be given spectral types, we classify the other 2475 carbon stars into five subtypes including 864 C-H, 226 C-R, 400 C-J, 266 C-N, and 719 barium stars based on a series of spectral features. Furthermore, we divide the C-J stars into three subtypes of CJ( H), C-J(R), C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Beside spectroscopic classification, we also match these carbon stars to multiple broadband photometries. Using ultraviolet photometry data, we find that 25 carbon stars have FUV detections and they are likely to be in binary systems with compact white dwarf companions.

astro-ph.SR↗

arXiv2016

LAMOST Spectrograph Response Curves: Stability and Application to flux calibration

The task of flux calibration for LAMOST (Large sky Area Multi-Object Spectroscopic Telescope) spectra is difficult due to many factors. For example, the lack of standard stars, flat fielding for large field of view, and variation of reddening between different stars especially at low galactic latitudes etc. Poor selection, bad spectral quality, or extinction uncertainty of standard stars not only might induce errors to the calculated spectral response curve (SRC), but also might lead to failures in producing final 1D spectra. In this paper, we inspected spectra with Galactic latitude |b|>=60 degree and reliable stellar parameters, determined through the LAMOST Stellar Parameter Pipeline (LASP), to study the stability of the spectrograph. To guarantee the selected stars had been observed by each fiber, we selected 37,931 high quality exposures of 29,000 stars from LAMOST DR2, and more than 7 exposures for each fiber. We calculated the SRCs for each fiber for each exposure, and calculated the statistics of SRCs for spectrographs with both the fiber variations and time variations. The result shows that the average response curve of each spectrograph (henceforth ASPSRC) is relatively stable with statistical errors <= 10%. From the comparison between each ASPSRC and the SRCs for the same spectrograph obtained by 2D pipeline, we find that the ASPSRCs are good enough to use for the calibration. The ASPSRCs have been applied to spectra which were abandoned by LAMOST 2D pipeline due to the lack of standard stars, increasing the number of LAMOST spectra by 52,181 in DR2. Comparing those same targets with SDSS, the relative flux differences between SDSS spectra and that of LAMOST spectra with the ASPSRC method are less than 10%, which underlines that the ASPSRC method is feasible for LAMOST flux calibration.

astro-ph.IM↗