arXiv subjects

Yongheng Zhao

Publications and source records attributed to Yongheng Zhao.

At least 55 records · Page 3Linked to original sources

arXiv2019

The galaxy luminosity function in the LAMOST Complete Spectroscopic Survey of Pointing Area at the Southern Galactic Cap

We present optical luminosity functions (LFs) of galaxies in the $^{0.1}g$, $^{0.1}r$, and $^{0.1}i$ bands, calculated using data in $\sim 40$ $deg^{2}$ sky area of LAMOST Complete Spectroscopic Survey of Pointing Area (LaCoSSPAr) in Southern Galactic Cap. Redshifts for galaxies brighter $r = 18.1$ were obtained mainly with LAMOST. In each band, LFs derived using both parametric and non-parametric maximum likelihood methods agree well with each other. In the $^{0.1}r$ band, our fitting parameters of the Schechter function are $ϕ_{*}=(1.65\pm0.36)\times10^{-2}h^{3}Mpc^{-3}$, $M_{*}=-20.69\pm0.06$ mag, and $α=-1.12\pm0.08$, in agreements with previous studies. Separate LFs are also derived for emission line galaxies and absorption line galaxies, respectively. The LFs of absorption line galaxies show a dip at $^{0.1}r \sim 18.5$ and can be well fitted by a double-Gaussian function, suggesting a bi-modality in passive galaxies.

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arXiv2019

Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning

We aim to select quasar candidates based on the two large survey databases, Pan-STARRS and AllWISE. Exploring the distribution of quasars and stars in the color spaces, we find that the combination of infrared and optical photometry is more conducive to select quasar candidates. Two new color criterions (yW1W2 and izW1W2) are constructed to distinguish quasars from stars efficiently. With izW1W2, 98.30% of star contamination is eliminated, while 99.50% of quasars are retained, at least to the magnitude limit of our training set of stars. Based on the optical and infrared color features, we put forward an efficient schema to select quasar candidates and high redshift quasar candidates, in which two machine learning algorithms (XGBoost and SVM) are implemented. The XGBoost and SVM classifiers have proven to be very effective with accuracy of 99.46% when 8Color as input pattern and default model parameters. Applying the two optimal classifiers to the unknown Pan-STARRS and AllWISE cross-matched data set, a total of 2,006,632 intersected sources are predicted to be quasar candidates given quasar probability larger than 0.5 (i.e. P_QSO>0.5). Among them, 1,201,211 have high probability (P_QSO>0.95). For these newly predicted quasar candidates, a regressor is constructed to estimate their redshifts. Finally 7,402 z>3.5 quasars are obtained. Given the magnitude limitation and site of the LAMOST telescope, part of these candidates will be used as the input catalogue of the LAMOST telescope for follow-up observation, and the rest may be observed by other telescopes.

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arXiv2018

Imbalanced learning for RR Lyrae stars

We apply machine learning and Convex-Hull algorithms to separate RR Lyrae stars from other stars, like main sequence stars, white dwarf stars, carbon stars, CVs and carbon-lines stars, based on the Sloan Digital Sky Survey (SDSS) and Galaxy Evolution Explorer (GALEX). In the low-dimensional space, the Convex-Hull algorithm is applied to select RR Lyrae stars. Given different input patterns of (u-g, g-r), (g-r, r-i), (r-i, i-z), (u-g, g-r, r-i), (g-r, r-i, i-z), (u-g, g-r, i-z) and (u-g, r-i, i-z), different convex hulls can be built for RR Lyrae stars. Comparing the performance of different input patterns, u-g, g-r, i-z is the best input pattern. For this input pattern, the efficiency (the fraction of true RR Lyrae stars in the predicted RR Lyrae sample) is 4.2% with a completeness (the fraction of recovered RR Lyrae stars in the whole RR Lyrae sample) of 100%, increases to 9.9% with 97% completeness and to 16.1% with 53% completeness by removing some outliers. In the high-dimensional space, machine learning algorithms are used with input patterns (u-g, g-r, r-i, i-z), (u-g, g-r, r-i, i-z, r), (NUV-u, u-g, g-r, r-i, i-z) and (NUV-u, u-g, g-r, r-i, i-z, r). RR Lyrae stars, which belong to the class of interest in our paper, are rare compared to other stars. For the highly imbalanced data, cost-sensitive Support Vector Machine (SVM), cost-sensitive Random Forest and Fast Boxes are used. The results show that information from GALEX is helpful for identifying RR Lyrae stars and Fast Boxes are the best performers on the skewed data in our case.

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arXiv2017

Cosmic Ray Removal in Fiber Spectroscopic Image

Single-exposure spectra in large spectral surveys are valuable for time domain studies such as stellar variability, but there is no available method to eliminate cosmic rays for single-exposure, multi-fiber spectral images. In this paper, we describe a new method to detect and remove cosmic rays in multi-fiber spectroscopic single exposures. Through the use of two-dimensional profile fitting and a noise model that considers the position-dependent errors, we successfully detect as many as 80% of the cosmic rays and correct the cosmic ray polluted pixels to an average accuracy of 97.8%. Multiple tests and comparisons with both simulated data and real LAMOST data show that the method works properly in detection rate, false detection rate, and validity of cosmic ray correction.

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arXiv2017

LAMOST Spectroscopic Survey of the Galactic Anticentre (LSS-GAC): the second release of value-added catalogues

We present the second release of value-added catalogues of the LAMOST Spectroscopic Survey of the Galactic Anticentre (LSS-GAC DR2). The catalogues present values of radial velocity $V_{\rm r}$, atmospheric parameters --- effective temperature $T_{\rm eff}$, surface gravity log$g$, metallicity [Fe/H], $α$-element to iron (metal) abundance ratio [$α$/Fe] ([$α$/M]), elemental abundances [C/H] and [N/H], and absolute magnitudes ${\rm M}_V$ and ${\rm M}_{K_{\rm s}}$ deduced from 1.8 million spectra of 1.4 million unique stars targeted by the LSS-GAC since September 2011 until June 2014. The catalogues also give values of interstellar reddening, distance and orbital parameters determined with a variety of techniques, as well as proper motions and multi-band photometry from the far-UV to the mid-IR collected from the literature and various surveys. Accuracies of radial velocities reach 5kms$^{-1}$ for late-type stars, and those of distance estimates range between 10 -- 30 per cent, depending on the spectral signal-to-noise ratios. Precisions of [Fe/H], [C/H] and [N/H] estimates reach 0.1dex, and those of [$α$/Fe] and [$α$/M] reach 0.05dex. The large number of stars, the contiguous sky coverage, the simple yet non-trivial target selection function and the robust estimates of stellar radial velocities and atmospheric parameters, distances and elemental abundances, make the catalogues a valuable data set to study the structure and evolution of the Galaxy, especially the solar-neighbourhood and the outer disk.

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arXiv2016

Estimating stellar atmospheric parameters, absolute magnitudes and elemental abundances from the LAMOST spectra with Kernel-based Principal Component Analysis

Accurate determination of stellar atmospheric parameters and elemental abundances is crucial for Galactic archeology via large-scale spectroscopic surveys. In this paper, we estimate stellar atmospheric parameters -- effective temperature T_{\rm eff}, surface gravity log g and metallicity [Fe/H], absolute magnitudes M_V and M_{Ks}, α-element to metal (and iron) abundance ratio [α/M] (and [α/Fe]), as well as carbon and nitrogen abundances [C/H] and [N/H] from the LAMOST spectra with amultivariate regressionmethod based on kernel-based principal component analysis, using stars in common with other surveys (Hipparcos, Kepler, APOGEE) as training data sets. Both internal and external examinations indicate that given a spectral signal-to-noise ratio (SNR) better than 50, our method is capable of delivering stellar parameters with a precision of ~100K for Teff, ~0.1 dex for log g, 0.3 -- 0.4mag for M_V and M_{Ks}, 0.1 dex for [Fe/H], [C/H] and [N/H], and better than 0.05 dex for [α/M] ([α/Fe]). The results are satisfactory even for a spectral SNR of 20. The work presents first determinations of [C/H] and [N/H] abundances from a vast data set of LAMOST, and, to our knowledge, the first reported implementation of absolute magnitude estimation directly based on the observed spectra. The derived stellar parameters for millions of stars from the LAMOST surveys will be publicly available in the form of value-added catalogues.

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arXiv2016

Astronomical Data Fusion Tool Based on PostgreSQL

With the application of advanced astronomical technologies, equipments and methods all over the world, astronomy covers from radio, infrared, visible light, ultraviolet, X-ray and gamma ray band, and enters into the era of full wavelength astronomy. How to effectively integrate data from different ground- and space-based observation equipments, different observers, different bands, different observation time, requires the data fusion technology. In this paper we introduce the cross-match tool that is developed by the Python language and based on the PostgreSQL database and uses Q3C as the core index, facilitating the cross-match work of massive astronomical data. It provides four different cross-match functions, namely: I) cross-match of custom error range; II) cross-match of catalog error; III) cross-match based on the elliptic error range; IV) cross-match of the nearest algorithm. The cross-match result set provides good foundation for subsequent data mining and statistics based on multiwavelength data. The most advantage of this tool is a user-oriented tool applied locally by users. By means of this tool, users may easily create their own databases, manage their own data and cross-match databases according to their requirements. In addition, this tool is also able to transfer data from one database into another database. More importantly, the tool is easy to get started and used by astronomers without writing any code.

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arXiv2016

Distance and extinction determination for APOGEE stars with Bayesian method

Using a Bayesian technology we derived distances and extinctions for over 100,000 red giant stars observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey by taking into account spectroscopic constraints from the APOGEE stellar parameters and photometric constraints from 2MASS, as well as a prior knowledge on the Milky Way. Derived distances are compared with those from four other independent methods, the Hipparcos parallaxes, star clusters, APOGEE red clump stars, and asteroseismic distances from APOKASC (Rodrigues et al. 2014) and SAGA Catalogues (Casagrande et al. 2014). These comparisons covers four orders of magnitude in the distance scale from 0.02 kpc to 20 kpc. The results show that our distances agree very well with those from other methods: the mean relative difference between our Bayesian distances and those derived from other methods ranges from -4.2% to +3.6%, and the dispersion ranges from 15% to 25%. The extinctions toward all stars are also derived and compared with those from several other independent methods: the Rayleigh-Jeans Color Excess (RJCE) method, Gonzalez's two-dimensional extinction map, as well as three-dimensional extinction maps and models. The comparisons reveal that, overall, estimated extinctions agree very well, but RJCE tends to overestimate extinctions for cool stars and objects with low logg.

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arXiv2016

Photometric Redshift Estimation for Quasars by Integration of KNN and SVM

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem for a long time and exists in the current photometric redshift estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and support vector machine (SVM) methods together. In the first stage, we apply KNN algorithm on photometric data and estimate their corresponding z$_{\rm phot}$. By analysis, we find two dense regions with catastrophic failure, one in the range of z$_{\rm phot}\in[0.3,1.2]$, the other in the range of z$_{\rm phot}\in [1.2,2.1]$. In the second stage, we map the photometric input pattern of points falling into the two ranges from original attribute space into a high dimensional feature space by Gaussian kernel function in SVM. In the high dimensional feature space, many outlier points resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and further be corrected. Experimental results based on the SDSS (the Sloan Digital Sky Survey) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |$Δ$z| ranges and rms (root mean square) error by the integrated method are $83.47\%$, $89.83\%$, $90.90\%$ and 0.192, respectively, compared to the results by KNN ($71.96\%$, $83.78\%$, $89.73\%$ and 0.204).

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arXiv2015

Quiescent luminous red galaxies (LRGs) as cosmic chronometers: on the significance of mass and environmental dependence

Massive luminous red galaxies (LRGs) are believed to be evolving passively and can be used as cosmic chronometers to estimate the Hubble constant. However, different LRGs may be located in different environments. The environmental effects may limit the use of the LRGs as cosmic chronometers. We aim to investigate the environmental and mass dependence of the formation of 'quiescent' LRGs selected from the Sloan Digital Sky Survey Date Release 8 and to pave the way for using LRGs as cosmic chronometers. Using the population synthesis software STARLIGHT, we derive the stellar populations in each LRG through the full spectrum fitting and obtain the mean age distribution and the mean star formation history (SFH) of those LRGs. We find that there is no apparent dependence of the mean age and the SFH of quiescent LRGs on their environment, while the ages of those quiescent LRGs depend weakly on their mass. We compare the SFHs of the SDSS LRGs with those obtained from a semi-analytical galaxy formation model, and find that they are roughly consistent with each other if we consider the errors in the STARLIGHT-derived ages. We find that a small fraction of later star formation in LRGs leads to a systematical overestimation (~28 %) of the Hubble constant by the differential age method, and the systematical errors in the STARLIGHT-derived ages may lead to an underestimation (~ 16 %) of the Hubble constant. However, these errors can be corrected by a detailed study of the mean SFH of those LRGs and by calibrating the STARLIGHT-derived ages to those obtained independently by other methods. The environmental effects do not play significant role in the age estimates of quiescent LRGs, and the quiescent LRGs as a population can be used securely as cosmic chronometers.

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arXiv2015

A Sample of E+A Galaxy Candidates in LAMOST Data Released 2

A sample of 70 E+A galaxies are selected from 37, 206 galaxies in the second data release (DR2) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) according to the criteria for E+A galaxies defined by Goto, and each of these objects is further visually identified. In this sample, most objects are low redshift E+A galaxies with z < 0.25, and locate in the high latitude sky area with magnitude among 14 to 18 mag in g, r and i bands. A stellar population analysis for the whole sample indicates that the E+A galaxies are characterized by both young and old stellar populations (SPs), and the metal-rich SPs have relatively higher contributions than the metal-poor ones. Additionally, a morphological classification for these objects is performed based on the images taken from the Sloan Digital Sky Survey (SDSS).

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arXiv2015

SDSS/SEGUE Spectral Feature Analysis For Stellar Atmospheric Parameter Estimation

Large-scale and deep sky survey missions are rapidly collecting a large amount of stellar spectra, which necessitate the estimation of atmospheric parameters directly from spectra and makes it feasible to statistically investigate latent principles in a large dataset. We present a technique for estimating parameters $T_{eff}$, log$~g$ and [Fe/H] from stellar spectra. With this technique, we first extract features from stellar spectra using the LASSO algorithm; then, the parameters are estimated from the extracted features using the SVR. On a subsample of 20~000 stellar spectra from SDSS with reference parameters provided by SDSS/SEGUE Pipeline SSPP, estimation consistency are 0.007458 dex for log$~T_{eff}$ (101.609921 K for $T_{eff}$), 0.189557 dex for log$~g$ and 0.182060 for [Fe/H], where the consistency is evaluated by mean absolute error. Prominent characteristics of the proposed scheme are sparseness, locality, and physical interpretability. In this work, every spectrum consists of 3821 fluxes, and 10, 19, and 14 typical wavelength positions are detected respectively for estimating $T_{eff}$, log$~g$ and [Fe/H]. It is shown that the positions are related to typical lines of stellar spectra. This characteristic is important in investigating physical indications from analysis results. Then, stellar spectra can be described by the individual fluxes on the detected positions (PD) or local integration of fluxes near them (LI). The abovementioned consistency is the result based on features described by LI. If features are described by PD, consistency are 0.009092 dex for log$~T_{eff}$ (124.545075 K for $T_{eff}$), 0.198928 dex for log$~g$, and 0.206814 dex for [Fe/H].

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arXiv2015

Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters

We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H]. "Linearly supporting" means that the atmospheric parameters can be accurately estimated from the extracted features through a linear model. The successive steps of the process are as follow: first, decompose the spectrum using a wavelet packet (WP) and represent it by the derived decomposition coefficients; second, detect representative spectral features from the decomposition coefficients using the proposed method Least Absolute Shrinkage and Selection Operator (LARS)$_{bs}$; third, estimate the atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H] from the detected features using a linear regression method. One prominent characteristic of this scheme is its ability to evaluate quantitatively the contribution of each detected feature to the atmospheric parameter estimate and also to trace back the physical significance of that feature. This work also shows that the usefulness of a component depends on both wavelength and frequency. The proposed scheme has been evaluated on both real spectra from the Sloan Digital Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF models. On real spectra, we extracted 23 features to estimate $T_{eff}$, 62 features for log$~g$, and 68 features for [Fe/H]. Test consistencies between our estimates and those provided by the Spectroscopic Sarameter Pipeline of SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log$~T_{eff}$ (83 K for $T_{eff}$), 0.2345 dex for log$~g$, and 0.1564 dex for [Fe/H]. For the synthetic spectra, the MAE test accuracies are 0.0022 dex for log$~T_{eff}$ (32 K for $T_{eff}$), 0.0337 dex for log$~g$, and 0.0268 dex for [Fe/H].

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arXiv2015

A Large Sample of Am Candidates from LAMOST Data Release 1

We present a sample of metallic-line star (Am) candidates from the Large sky Area Multi-Object fiber Spectroscopic Telescope Data Release one (LAMOST DR1). According to the characteristic of under-abundance of calcium and overabundance of iron element of Am stars, we propose an empirical separation curve derived from line indices of Ca II K-line and iron lines we choose for low resolution spectra. 3537 Am candidates are ultimately selected from more than 30,000 stars which are classified as A-type or early-F stars by both LAMOST pipeline and visual inspection. Then we make some analysis on this sample and finally provide a list of these Am candidates with 10 relevant parameters. Comparing with other catalogues, Am candidates selected from LAMOST DR1 are much fainter on the whole. Obviously, our list is an important complementary to already known bright Am catalogues, and it offers valuable material for the research on this type of chemically peculiar stars.

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arXiv2014

Relative Flux Calibration of the LAMOST Spectroscopic Survey of the Galactic Anti-center

We have developed and implemented an iterative algorithm of flux calibration for the LAMOST Spectroscopic Survey of the Galactic anti-center (LSS-GAC). For a given LSS-GAC plate, the spectra are first processed with a set of nominal spectral response curves (SRCs) and used to derive initial stellar atmospheric parameters (effective temperature $T_{\rm eff}$, surface gravity log\,$g$ and metallicity [Fe/H]) as well as dust reddening $E(B-V)$ of all targeted stars. For each of the sixteen spectrographs, several F-type stars of good signal-to-noise ratios (SNRs) are then selected as flux standard stars for further, iterative spectral flux calibration. Comparison of spectrophotometric colours, deduced from the flux-calibrated spectra, with the photometric measurements yields average differences of 0.02$\pm$0.07 and $-$0.04$\pm$0.09\,mag for the $(g-r)$ and $(g-i)$, respectively. The relatively large negative offset in $(g-i)$ is due to the fact that we have opted not to correct for the telluric bands, most notably the atmospheric A-band in the wavelength range of $i$-band. Comparison of LSS-GAC multi-epoch observations of duplicate targets indicates that the algorithm has achieved an accuracy of about 10 per cent in relative flux calibration for the wavelength range 4000 -- 9000\,Å. The shapes of SRC deduced for the individual LAMOST spectrographs are found to vary by up to 30 per cent for a given night, and larger for different nights, indicating that the derivation of SRCs for the individual plates is essential in order to achieve accurate flux calibration for the LAMOST spectra.

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arXiv2014

The LAMOST Stellar Parameter Pipeline at Peking University --- LSP3

We introduce the LAMOST Stellar Parameter Pipeline at Peking University --- LSP3, developed and implemented for the determinations of radial velocity $V_{\rm r}$ and stellar atmospheric parameters (effective temperature $T_{\rm eff}$, surface gravity log\,$g$, metallicity [Fe/H]) for the LAMOST Spectroscopic Survey of the Galactic Anti-center (LSS-GAC). We describe the algorithms of LSP3 and examine the accuracy of parameters yielded by it. The precision and accuracy of parameters yielded are investigated by comparing results of multi-epoch observations and of candidate members of open and globular clusters, with photometric calibration, as well as with independent determinations available from a number of external databases, including the PASTEL archive, the APOGEE, SDSS and RAVE surveys, as well as those released in the LAMOST DR1. The uncertainties of LSP3 parameters are characterized and quantified as a function of the spectral signal-to-noise ratio (SNR) and stellar atmospheric parameters. We conclude that the current implementation of LSP3 has achieved an accuracy of 5.0\,km\,s$^{-1}$, 150\,K, 0.25\,dex, 0.15\,dex for the radial velocity, effective temperature, surface gravity and metallicity, respectively, for LSS-GAC spectra of FGK stars of SNRs per pixel higher than 10. The LSP3 has been applied to over a million LSS-GAC spectra collected hitherto. Stellar parameters yielded by the LSP3 will be released to the general public following the data policy of LAMOST, together with estimates of the interstellar extinction $E(B-V)$ and stellar distances, deduced by combining spectroscopic and multi-band photometric measurements using a variety of techniques.

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arXiv2014

LAMOST Spectroscopic Survey of the Galactic Anti-centre (LSS-GAC): target selection and the first release of value-added catalogues

As a major component of the LAMOST Galactic surveys, the LAMOST Spectroscopic Survey of the Galactic Anti-centre (LSS-GAC) aims to survey a significant volume of the Galactic thin/thick discs and halo for a contiguous sky area of over 3,400 deg$^2$ centred on the Galactic anti-centre ($|b| \leq 30^{\circ}$, $150 \leq l \leq 210^{\circ}$), and obtain $λλ$3700 -- 9000 low resolution ($R \sim 1,800$) spectra for a statistically complete sample of $\sim 3$\,M stars of all colours down to a limiting magnitued of $r$ $\sim$ 17.8\,mag (to 18.5\,mag for limited fields). Together with Gaia, the LSS-GAC will yield a unique dataset to advance our understanding of the structure and assemblage history of the Galaxy, in particular its disk(s). In addition to the main survey, the LSS-GAC will also target hundreds of thousands objects in the vicinity fields of M\,31 and M\,33 and survey a significant fraction (over a million) of randomly selected very bright stars (VB; $r \le 14$ mag) in the northern hemisphere. During the Pilot and the first year Regular Surveys of LAMOST, a total of 1,042,586 [750,867] spectra of a signal to noise ratio S/N(7450Å) $\ge$ 10 [S/N(4650Å) $\ge$ 10] have been collected. In this paper, we present a detailed description of the target selection algorithm, survey design, observations and the first data release of value-added catalogues (including radial velocities, effective temperatures, surface gravities, metallicities, values of interstellar extinction, distances, proper motions and orbital parameters) of the LSS-GAC.

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arXiv2014

A Search for Spectral Galaxy Pairs of Overlapping Galaxies based on Fuzzy Recognition

The Spectral Galaxy Pairs (SGPs) are defined as the composite galaxy spectra which contain two independent redshift systems. These spectra are useful for studying dust properties of the foreground galaxies. In this paper, a total of 165 spectra of SGPs are mined out from Sloan Digital Sky Survey (SDSS) Data Release 9 (DR9) using the concept of membership degree from the fuzzy set theory particularly defined to be suitable for fuzzily identifying emission lines. The spectra and images of this sample are classified according to the membership degree and their image features, respectively. Many of these 2nd redshift systems are too small or too dim to select from the SDSS images alone, making the sample a potentially unique source of information on dust effects in low-luminosity or low-surface-brightness galaxies that are underrepresented in morphological pair samples. The dust extinction of the objects with high membership degree is also estimated by Balmer decrement. Additionally, analyses for a series of spectroscopic observations of one SGP from 165 systems indicate that a newly star-forming region of our Milky Way might occur.

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