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Register a new userRecognition of M-type stars in the unclassified spectra of LAMOST DR5 using a hash learning method
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Our study aims to recognize M-type stars which are classified as UNKNOWN due to bad quality in Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) DR5 V1. A binary nonlinear hashing algorithm based on Multi-Layer Pseudo Inverse Learning (ML-PIL) is proposed to effectively learn spectral features for the M-type star detection, which can overcome the bad fitting problem of template matching, particularly for low S/N spectra. The key steps and the performance of the search scheme are presented. A positive dataset is obtained by clustering the existing M-type spectra to train the ML-PIL networks. By employing this new method, we find 11,410 M-type spectra out of 642,178 UNKNOWN spectra, and provide a supplemental catalogue. Both the supplemental objects and released M-type stars in DR5 V1 are composed a whole M type sample, which will be released in the official DR5 to the public in June 2019, All the M-type stars in the dataset are classified to giants and dwarfs by two suggested separators: 1) color diagram of H versus J~K from 2MASS; 2) line indices CaOH versus CaH1, and the separation is validated with HRD derived from Gaia DR2. The magnetic activities and kinematics of M dwarfs are also provided with the EW of H_alpha emission line and the astrometric data from Gaia DR2 respectively.
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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.
We report the first systematic study to identify and characterize a sample of classical Ae stars in the Galaxy. The spectra of these stars were retrieved from the A-star catalog using the Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) survey. We identified the emission-line stars in this catalog from which 159 are confirmed as classical Ae stars. This increases the sample of known classical Ae stars by about nine times from the previously identified 21 stars. The evolutionary phase of classical Ae stars in this study is confirmed from the relatively small mid- and far-infrared excess and from their location in the optical color-magnitude diagram. We estimated the spectral type using MILES spectral templates and identified Classical Ae stars beyond A3, for the first time. The prominent emission lines in the spectra within the wavelength range 3700 -- 9000 {AA} are identified and compared with the features present in classical Be stars. The H{alpha} emission strength of the stars in our sample show a steady decrease from late-B type to Ae stars, suggesting that the disc size may be dependent on the spectral type. Interestingly, we noticed emission lines of Fe{sc ii}, O{sc i} and Paschen series in the spectrum of some classical Ae stars. These lines are supposed to fade out by late B-type and should not be present in Ae stars. Further studies, including spectra with better resolution, is needed to correlate these results with the rotation rates of classical Ae stars.
We present the determination of stellar parameters and individual elemental abundances for 6 million stars from $sim$8 million low-resolution ($Rsim1800$) spectra from LAMOST DR5. This is based on a modeling approach that we dub $The$ $Data$--$Driven$ $Payne$ ($DD$--$Payne$), which inherits essential ingredients from both {it The Payne} citep{Ting2019} and $The$ $Cannon$ citep{Ness2015}. It is a data-driven model that incorporates constraints from theoretical spectral models to ensure the derived abundance estimates are physically sensible. Stars in LAMOST DR5 that are in common with either GALAH DR2 or APOGEE DR14 are used to train a model that delivers stellar parameters ($T_{rm eff}$, $log g$, $V_{rm mic}$) and abundances for 16 elements (C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, and Ba) when applied to LAMOST spectra. Cross-validation and repeat observations suggest that, for ${rm S/N}_{rm pix}ge 50$, the typical internal abundance precision is 0.03--0.1,dex for the majority of these elements, with 0.2--0.3,dex for Cu and Ba, and the internal precision of $T_{rm eff}$ and $log g$ is better than 30,K and 0.07,dex, respectively. Abundance systematics at the $sim$0.1,dex level are present in these estimates, but are inherited from the high-resolution surveys training labels. For some elements, GALAH provides more robust training labels, for others, APOGEE. We provide flags to guide the quality of the label determination and to identify binary/multiple stars in LAMOST DR5. The abundance catalogs are publicly accessible via href{url}{http://dr5.lamost.org/doc/vac}.
We present an automated statistical method that uses medium-resolution spectroscopic observations of a set of stars to select those that show evidence of possessing significant amounts of neutron-capture elements. Our tool was tested against a sample of $sim 70,000$ F- and G-type stars distributed among $215$ plates from the Galactic Understanding and Exploration (SEGUE) survey, including $13$ that were directed at stellar Galaxy clusters. Focusing on five spectral lines of europium in the visible window, our procedure ranked the stars by their likelihood of having enhanced content of this atomic species and identifies the objects that exhibit signs of being rich in neutron-capture elements as those scoring in the upper $2.5%$. We find that several of the cluster plates contain relatively large numbers of stars with significant absorption around at least three of the five selected lines. The most prominent is the globular cluster M3, where we measured a fraction of stars that are potentially rich in heavy nuclides, representing at least $15%$.
We construct a sample of nearly 30,000 main-sequence stars with 4500K $<Trm_{eff}<$ 5000K and stellar ages estimated by the chromospheric activity$-$age relation. This sample is used to determine the age distribution in the $R-Z$ plane of the Galaxy, where $R$ is the projected Galactocentric distance in the disk midplane and $Z$ is the height above the disk midplane. As $|Z|$ increases, the percentage of old stars becomes larger. It is known that scale-height of Galactic disk increases as $R$ increases, which is called flare. A mild flare from $R$ $sim$ 8.0 to 9.0 kpc in stellar age distribution is found. We also find that the velocity dispersion increases with age as confirmed by previous studies. Finally we present spiral-shaped structures in $Z-upsilon_{Z}$ phase space in three stellar age bins. The spiral is clearly seen in the age bin of [0, 1] Gyr, which suggests that a vertical perturbation to the disk probably took place within the last $sim$ 1.0 Gyr.
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