Hot Subdwarf Stars Identified in Gaia DR2 with Spectra of LAMOST DR6 and DR7. I. Single-lined Spectra

Lei, Zhenxin; Zhao, Jingkun; Németh, P.; Zhao, Gang

doi:10.3847/1538-4357/ab660a

Cited by 35 publications

(74 citation statements)

References 94 publications

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“…10, we show the distributions of the radial velocities of the hot subdwarfs for different α CE , where their orbital inclinations i are uniformly generated in cos i. Here, we only show the cases with τ sp = 10 7 yr, because a spindown time scale of a few 10 6 yr may be necessary for SNe Ia (Soker 2018; Meng & Podsiadlowski (Németh et al 2012) and LAMOST (Luo et al 2016(Luo et al , 2019Lei et al 2019Lei et al , 2020 surveys, where the stars with significant binary signals are excluded. Two sequences for He weak subdwarfs and more He-rich ones are shown by dashed and dot-dashed lines, respectively.…”

Section: The Mass and Orbital Velocity Of The Companions At Supernova...mentioning

confidence: 99%

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Hot subdwarfs from the surviving companions of the white dwarf + main-sequence channel of Type Ia supernovae

Meng,

Luo

2021

Preprint

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Some surviving companions of type Ia supernovae (SNe Ia) from the white-dwarf + main-sequence (WD + MS) channel may evolve to hot subdwarfs. In this paper, we preformed stellar evolution calculations for surviving companions of close WD + MS systems in the spin-up/spin-down model and the canonical non-rotating model to map out the initial parameter spaces in the orbital period -secondary mass plane in which the surviving companions can evolve to hot subdwarfs. Based on these results, we carried out a series of binary population synthesis calculation to obtain the Galactic birth rate of the hot subdwarfs from the WD + MS channel, which is 2.3 − 6 × 10 −4 yr −1 for the spin-up/spin-down model and 0.7 − 3 × 10 −4 yr −1 for the canonical non-rotating model. We also show the distributions of some integral properties of the hot subdwarfs, e.g. the mass and the space velocity, for different models. In addition, comparing our results with the observations of the intermediate helium-rich (iHe-rich) hot subdwarfs, the hot subdwarfs from the WD + MS channel may explain some observational features of the iHe-rich hot subdwarfs, especially for those from the spin-up/spin-down model. Although we expect that the SN Ia channel can only contribute a small fraction of the iHe-rich hot subdwarf population, some of these may help to explain cases with unusual kinematics.

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Section: The Mass and Orbital Velocity Of The Companions At Supernova...mentioning

confidence: 99%

“…Here, we collected the atmospheric parameters of some hot subdwarfs from the GALEX and LAMOST surveys (Németh et al 2012, Luo et al 2016Lei et al 2019Lei et al , 2020, and plotted them into a log(N He /N H ) × log g − log T eff plane (Fig. 11), where the stars with significant binary signals are excluded.…”

Section: Comparison With Observationsmentioning

confidence: 99%

Hot subdwarfs from the surviving companions of the white dwarf + main-sequence channel of Type Ia supernovae

Meng,

Luo

2021

Preprint

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“…K-means method is one of the most widely used clustering methods. It is often used in astronomy to study stellar and galaxy spectra, X-ray spectra, asteroids spectra, and so on [18], [43], [44]. However, K-means is not optimized to detect outliers, which needs to randomly select k objects as the initial centroids and repeat iteratively until reaching convergence.…”

Section: B Unsupervised Learningmentioning

confidence: 99%

“…M. Perpinya-Valles et al reported the discovery of J1953-1019, the first resolved triple white dwarf system based on Gaia DR2 photometry and astrometry combined with the follow-up spectroscopy [11], and many other authors also detect the WDMS from various astronomical data [12]- [16]. In addition, Lei et al identified 294 hot subdwarf stars from LAMOST DR5 [17] and 182 single-lined hot subdwarf stars by using atmospheric parameter and spectral lines from LAMOST DR6 and DR7 [18]. Wang et al proposed a method based Lick-index for excavation and analysis of outliers from massive stellar spectra [19].…”

Section: Introductionmentioning

confidence: 99%

Rare Object Search From Low-S/N Stellar Spectra in SDSS

Pan

et al. 2020

IEEE Access

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Rare objects such as white dwarf+main sequence (WDMS) and cataclysmic variables (CVs) are very important for studying the evolution of the galaxy and the universe. The large amount of spectra obtained by the large sky surveys such as the Sloan Digital Sky Survey (SDSS) are rich sources of these rare objects. However, a considerable fraction of these spectra are low-S/N spectra. These low-S/N spectra contain similar useful information as the high-S/N spectra, and making better use of these spectra can significantly improve the chance of finding rare objects. Nevertheless, little research has been done on them. In this study we propose a novel method based on the combination of PCA (Principal Components Analysis) and CFSFDP (Clustering by Fast Search and Find of Density Peak) to search for rare objects from low-S/N spectra. The PCA first extracts principal components from high-S/N spectra to generate general feature spectra and reconstructs low-S/N stellar spectra with these general feature spectra. Then the CFSFDP calculates the Local Density ρ and the Distance δ of the reconstructed spectra, and select the outliers through the decision graph quickly and accurately. We first apply our method to spectra in SDSS stellar classification template library with adding white gaussian noise to search for rare objects (carbon stars, carbon white dwarfs, carbon_lines, white dwarfs and white dwarfs magnetic). Then we apply our method to observed spectra with different low-S/Ns from SDSS and compared with Lick-index+K-means and Support Vector Machines (SVM). The experimental results show that our method has a higher efficiency compared to other methods.

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“…SDSS (the Sloan Digital Sky Survey [1,2]) and LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope [3,4]) are the two most ambitious spectroscopic surveys worldwide. The latest and final data release of optical spectra of SDSS is data release 16. LAMOST has been upgraded to have both low-and medium-resolution modes of observation and has finished data release 7 (DR7) [5] which is the product of the medium-resolution spectra (MRS) survey…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Massive Spectra Based on Enhanced Multi-Scale Coded Convolutional Neural Network

Bin

Wei²,

Liu³

et al. 2020

Universe

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The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has produced massive medium-resolution spectra. Data mining for special and rare stars in massive LAMOST spectra is of great significance. Feature extraction plays an important role in the process of automatic spectra classification. The proper classification network can extract most of the common spectral features with minimum noise and individual features. Such a network has better generalization capabilities and can extract sufficient features for classification. A variety of classification networks of one dimension and two dimensions are both designed and implemented systematically in this paper to verify whether spectra is easier to deal with in a 2D situation. The experimental results show that the fully connected neural network cannot extract enough features. Although convolutional neural network (CNN) with a strong feature extraction capability can quickly achieve satisfactory results on the training set, there is a tendency for overfitting. Signal-to-noise ratios also have effects on the network. To investigate the problems above, various techniques are tested and the enhanced multi-scale coded convolutional neural network (EMCCNN) is proposed and implemented, which can perform spectral denoising and feature extraction at different scales in a more efficient manner. In a specified search, eight known and one possible cataclysmic variables (CVs) in LAMOST MRS are identified by EMCCNN including four CVs, one dwarf nova and three novae. The result supplements the spectra of CVs. Furthermore, these spectra are the first medium-resolution spectra of CVs. The EMCCNN model can be easily extended to search for other rare stellar spectra.Universe 2020, 6, 60 2 of 23 (R∼7500) 1 . LAMOST DR7 was released in March 2020. The massive spectra from the survey telescope has provided a new opportunity for searching for special and unusual celestial bodies such as cataclysmic variables (CVs) [6][7][8].Over recent decades, spectra classification has mainly focused on 1D processing and achieved recognized results [9][10][11]. With the continuous development of deep learning technology [12], CNN (convolutional neural network) [13] is giving an excellent performance for feature extraction and combination. As a classic deep convolutional network, VGGNet (Visual Geometry Group Network) [14] is widely used in image classification tasks. ResNet (Residual Network) is the network structure [15]; its innovative use of residual learning allows deep networks to extract more effective features. DenseNet (Densely Connected Convolutional Networks) [16] first proposes that the output of each layer is the input of the next layer, allowing the network to extract features of more dimensions. A multi-layer convolutional neural network was first introduced based on the learnable kernel into the field of spectral classification but did not achieve satisfactory results [17]; this method tried to fold the 1D spectra, but did not achieve satisfactory results.This paper verifies ...

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Hot Subdwarf Stars Identified in Gaia DR2 with Spectra of LAMOST DR6 and DR7. I. Single-lined Spectra

Cited by 35 publications

References 94 publications

Hot subdwarfs from the surviving companions of the white dwarf + main-sequence channel of Type Ia supernovae

Hot subdwarfs from the surviving companions of the white dwarf + main-sequence channel of Type Ia supernovae

Rare Object Search From Low-S/N Stellar Spectra in SDSS

Automated Classification of Massive Spectra Based on Enhanced Multi-Scale Coded Convolutional Neural Network

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