arXiv subjects

Yongheng Zhao

Publications and source records attributed to Yongheng Zhao.

86 records · Page 5Linked to original sources

arXiv2010

Eight new quasars discovered by LAMOST in one extragalactic field

We report the discovery of eight new quasars in one extragalactic field (five degree centered at RA=$08^h58^m08.2^s$, Dec=$01^o32'29.7''$) with the LAMOST commissioning observations on December 18, 2009. These quasars, with $i$ magnitudes from 16.44 to 19.34 and redshifts from 0.898 to 2.773, were not identified in the SDSS spectroscopic survey, though six of them with redshifts less than 2.5 were selected as quasar targets in SDSS. Except one source without near-IR $Y$-band data, seven of these eight new quasars meet a newly proposed quasar selection criterion involving both near-IR and optical colors. Two of them were found in the 'redshift desert' for quasars ($z$ from 2.2 to 3), indicating that the new criterion is efficient for recovering the missing quasars with similar optical colors as stars. Although LAMOST met some problems during the commissioning observations, we were still able to identify other 38 known SDSS quasars in this field, with $i$ magnitudes from 16.24 to 19.10 and redshifts from 0.297 to 4.512. Our identifications imply that a substantial fraction of quasars may be missing in the previous quasar surveys. The implication of our results to the future LAMOST quasar survey is discussed.

astro-ph.CO↗

arXiv2010

A very bright i=16.44 quasar in the `redshift desert' discovered by LAMOST

The redshift range from 2.2 to 3, is known as the 'redshift desert' of quasars because quasars with redshift in this range have similar optical colors as normal stars and are thus difficult to be found in optical sky surveys. A quasar candidate, SDSS J085543.40-001517.7, which was selected by a recently proposed criterion involving near-IR $Y-K$ and optical $g-z$ colors, was identified spectroscopically as a new quasar with redshift of 2.427 by the LAMOST commissioning observation in December 2009 and confirmed by the observation made with the NAOC/Xinglong 2.16m telescope in March 2010. This quasar was not targeted in the SDSS spectroscopic survey because it locates in the stellar locus of the optical color-color diagrams, while it is clearly separated from stars in the $Y-K$ vs. $g-z$ diagram. Comparing with other SDSS quasars we found this new quasar with $i$ magnitude of 16.44 is apparently the brightest one in the redshift range from 2.3 to 2.7. From the spectral properties we derived its central black hole mass as $(1.4\sim3.9) \times 10^{10} M_\odot$ and the bolometric luminosity as $3.7\times 10^{48}$ \ergs, which indicates that this new quasar is intrinsically very bright and belongs to the most luminous quasars in the universe. Our identification supports that quasars in the redshift desert can be found by the quasar selection criterion involving the near-IR colors. More missing quasars are expected to be recovered by the future LAMOST spectroscopic surveys, which is important to the study of the cosmological evolution of quasars at redshift higher than 2.2.

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arXiv2010

A VO-driven Astronomical Data Grid in China

With the implementation of many ambitious observation projects, including LAMOST, FAST, and Antarctic observatory at Doom A, observational astronomy in China is stepping into a brand new era with emerging data avalanche. In the era of e-Science, both these cutting-edge projects and traditional astronomy research need much more powerful data management, sharing and interoperability. Based on data-grid concept, taking advantages of the IVOA interoperability technologies, China-VO is developing a VO-driven astronomical data grid environment to enable multi-wavelength science and large database science. In the paper, latest progress and data flow of the LAMOST, architecture of the data grid, and its supports to the VO are discussed.

astro-ph.IM↗

arXiv2009

New Supernova Candidates from SDSS-DR7 of Spectral Survey

The letter presents 25 discovered supernova candidates from SDSS-DR7 with our dedicated method, called Sample Decrease, and 10 of them were confirmed by other research groups, and listed in this letter. Another 15 are first discovered including 14 type Ia and one type II based on Supernova Identification (SNID) analysis. The results proved that our method is reliable, and the description of the method and some detailed spectra analysis procedures were also presented in this letter.

astro-ph.CO↗

arXiv2009

Forecasting the Dark Energy Measurement with Baryon Acoustic Oscillations: Prospects for the LAMOST surveys

The Large Area Multi-Object Spectroscopic Telescope (LAMOST) is a dedicated spectroscopic survey telescope being built in China, with an effective aperture of 4 meters and equiped with 4000 fibers. Using the LAMOST telescope, one could make redshift survey of the large scale structure (LSS). The baryon acoustic oscillation (BAO) features in the LSS power spectrum provide standard rulers for measuring dark energy and other cosmological parameters. In this paper we investigate the meaurement precision achievable for a few possible surveys: (1) a magnitude limited survey of all galaxies, (2) a survey of color selected red luminous galaxies (LRG), and (3) a magnitude limited, high density survey of z<2 quasars. For each survey, we use the halo model to estimate the bias of the sample, and calculate the effective volume. We then use the Fisher matrix method to forecast the error on the dark energy equation of state and other cosmological parameters for different survey parameters. In a few cases we also use the Markov Chain Monte Carlo (MCMC) method to make the same forecast as a comparison. The fiber time required for each of these surveys is also estimated. These results would be useful in designing the surveys for LAMOST.

astro-ph↗

arXiv2008

Morphology Classification and Photometric Redshift Measurement of Galaxies

Based on the Sloan Digital Sky Survey Data Release 5 Galaxy Sample, we explore photometric morphology classification and redshift estimation of galaxies using photometric data and known spectroscopic redshifts. An unsupervised method, k-means algorithm, is used to separate the whole galaxy sample into early- and late-type galaxies. Then we investigate the photometric redshift measurement with different input patterns by means of artificial neural networks (ANNs) for the total sample and the two subsamples. The experimental result indicates that ANNs show better performance when the more parameters are applied in the training set, and the mixed accuracy $σ_{mix}=\sqrt{σ_{early}^2+{σ_{late}}^2}$ of photometric redshift estimation for the two subsets is superior to $σ_{z}$ for the overall sample alone. For the optimal result, the rms deviation of photometric redshifts for the mixed sample amounts to 0.0192, that for the overall sample is 0.0196, meanwhile, that for early- and late-type galaxies adds up to 0.0164 and 0.0217, respectively.

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arXiv2008

The Application of kd-tree in Astronomy

The basic idea of the kd-tree algorithm is to recursively partition a point set P by hyperplanes, and to store the obtained partitioning in a binary tree. Due to its immense popularity, many applications in astronomy have been implemented. The algorithm can been used to solve a near neighbor problem for cross-identification of huge catalogs and realize the classification of astronomical objects. Since kd-tree can speed up query and partition spaces, some approaches based on it have been applied for photometric redshift measurement. We give the case studies of kd-tree in astronomy to show its importance and performance.

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arXiv2007

Worldwide R&D of Virtual Observatory

Virtual Observatory (VO) is a data intensive online astronomical research and education environment, taking advantages of advanced information technologies to achieve seamless and uniform access to astronomical information. The concept of VO was introduced in late of 1990s to meet challenges brought up with data avalanche in astronomy. This paper reviews current status of International Virtual Observatory Alliance, technical highlights from world wide VO projects, and a brief introduction of Chinese Virtual Observatory.

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arXiv2007

Multi-parameter estimating photometric redshifts with artificial neural networks

We calculate photometric redshifts from the Sloan Digital Sky Survey Data Release 2 Galaxy Sample using artificial neural networks (ANNs). Different input patterns based on various parameters (e.g. magnitude, color index, flux information) are explored and their performances for redshift prediction are compared. For ANN technique, any parameter may be easily incorporated as input, but our results indicate that using dereddening magnitude produces photometric redshift accuracies often better than the Petrosian magnitude or model magnitude. Similarly, the model magnitude is also superior to Petrosian magnitude. In addition, ANNs also show better performance when the more effective parameters increase in the training set. Finally, the method is tested on a sample of 79, 346 galaxies from the SDSS DR2. When using 19 parameters based on the dereddening magnitude, the rms error in redshift estimation is sigma(z)=0.020184. The ANN is highly competitive tool when compared with traditional template-fitting methods where a large and representative training set is available.

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arXiv2006

[O III] emission line in narrow-line Seyfert 1 galaxies

Three sets of two-component profiles are used to simultaneously model the [O III]$λλ$4959, 5007 and H$β$ lines for the Fe II-subtracted spectra of 149 narrow line Seyfert 1 galaxies (NLSls) from the Sloan Digital Sky Survey (SDSS). Using the linewidth of the narrow/core component of [O III]$λ$5007 to trace the stellar velocity dispersion instead of using the total linewidth of [O III]$λ$5007, we found that the SDSS NLSls are still deviated from the $M_{bh}-σ$ relation found in the nearby inactive galaxies, suggesting that the linewidth of the [O III] narrow/core component is likely not a good tracer of bulge velocity dispersion in NLSls, since some other studies indicate that NLS1s, like other AGN, should follow the $M_{bh}-σ$ relation. If we assume that the [O III]5007/H$β_{n}$ line ratio emitted in narrow line region ranges from one to ten, 63 objects are found to satisfied with this criterion and their H$β$ broad components should be used to calculate their virial black hole masses. These 63 objects are statically consistent with the $M_{bh}-σ_{[O III]}$ relation. With the Chandra observation of some SDSS NLSls, for one object in these 63 objects, J143030.22-001115.1, we found that it can't be classified as a genuine NLS1. Its narrow component of H$β$ is coming from narrow line regions (NLRs). This is consistent with its very flat hard X-ray spectrum found by williams et al. (2004).

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arXiv2004

Integrating Existing Software Toolkits into VO System

Virtual Observatory (VO) is a collection of interoperating data archives and software tools. Taking advantages of the latest information technologies, it aims to provide a data-intensively online research environment for astronomers all around the world. A large number of high-qualified astronomical software packages and libraries are powerful and easy of use, and have been widely used by astronomers for many years. Integrating those toolkits into the VO system is a necessary and important task for the VO developers. VO architecture greatly depends on Grid and Web services, consequently the general VO integration route is "Java Ready - Grid Ready - VO Ready". In the paper, we discuss the importance of VO integration for existing toolkits and discuss the possible solutions. We introduce two efforts in the field from China-VO project, "gImageMagick" and " Galactic abundance gradients statistical research under grid environment". We also discuss what additional work should be done to convert Grid service to VO service.

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arXiv2004

Automated Clustering Algorithms for Classification of Astronomical Objects

Data mining is an important and challenging problem for the efficient analysis of large astronomical databases and will become even more important with the development of the Global Virtual Observatory. In this study, learning vector quantization (LVQ), single-layer perceptron (SLP) and support vector machines (SVM) were put forward for multi-wavelength data classification. A feature selection technique was used to evaluate the significance of the considered features to the results of classification. From the results, we conclude that in the situation of less features, LVQ and SLP show better performance. In contrast, SVM shows better performance when considering more features. The focus of the automatic classification is on the development of efficient feature-based classifier. The classifiers trained by these methods can be used for preselecting AGN candidates.

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arXiv2001

Variability of Soft X-ray Spectral Shape in Blazars Observed by ROSAT

In paper 1 (Cheng et al. 2001) we have shown that the soft X-ray spectra of two types of Seyfert 1 galaxies statistically vary differently with increasing intensity. In order to understand how the spectrum of blazars changes, the spectral shape variability of 18 blazars observed by ROSAT/PSPC mode are studied by presenting the correlation of Hardness Ratio 1 versus Count Rates (HR1-CTs). According to our criteria, 10 blazars show a positive HR1-CTs relation, and only 2 blazars display an anti-correlation of HR1 versus CTs. The rest 6 blazars do not indicate any clear correlation. From these we can see that most blazars of our sample statistically show a hardening spectrum during overall flux increase, though some vary randomly. By investigating the photon index of these objects and different radiation theories, we argue that the dominance of the synchrotron or inverse Compton emission in the soft X-ray band may interpret the dichotomy of spectral variability well, and that different spectral variations might represent a sequence of synchrotron peaked frequency.

astro-ph↗

arXiv2001

Variability of Soft X-ray Spectral Shape: Narrow-Line Seyfert 1 Galaxies versus Broad-Line Seyfert 1 Galaxies

In order to understand how the soft X-ray spectra vary we present the Hardness Ratio 1 versus Count Rates (HR1-CTs) correlation of 8 Narrow-line Seyfert 1 Galaxies (NLS1s) and 14 Broad-line Seyfert1 Galaxies (BLS1s) obtained during the ROSAT PSPC pointing observations. According to our criteria, six of the NLS1s show a positive HR1-CTs correlation, and seven of the BLS1s display an anti-correlation of HR1 versus CTs. The other 2 NLS1s and 7 BLS1s do not show a clear HR1-CTs correlation. From these we can see that the NLS1s statistically show a different spectral shape variability with flux change from the BLS1s: the spectra of NLS1s become harder as total flux increases while those of BLS1s soften. We attribute the different spectral variations to a strong stable 'soft excess' in NLS1s, while it is weak in BLS1s. For two types of objects, the power law component similarly becomes softer with increasing intensity. These imply that the soft excess originates from the Big Blue Bump and power law emission is from Compton upscattering of UV or Soft X-ray photons. Our results are consistent with what is widely accepted that NLS1s have smaller black hole masses and higher accretion rates than BLS1s.

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