arXiv subjects

JiFeng Liu

Publications and source records attributed to JiFeng Liu.

12 recordsLinked to original sources

arXiv2020

The SiTian project

SiTian is an ambitious ground-based all-sky optical monitoring project, developed by the Chinese Academy of Sciences. The concept is an integrated network of dozens of 1-m-class telescopes deployed partly in China and partly at various other sites around the world. The main science goals are the detection, identification and monitoring of optical transients (such as gravitational wave events, fast radio bursts, supernovae) on the largely unknown timescales of less than 1 day; SiTian will also provide a treasure trove of data for studies of AGN, quasars, variable stars, planets, asteroids, and microlensing events. To achieve those goals, SiTian will scan at least 10,000 square deg of sky every 30 min, down to a detection limit of $V \approx 21$ mag. The scans will produce simultaneous light-curves in 3 optical bands. In addition, SiTian will include at least three 4-m telescopes specifically allocated for follow-up spectroscopy of the most interesting targets. We plan to complete the installation of 72 telescopes by 2030 and start full scientific operations in 2032.

astro-ph.IM↗

arXiv2019

Machine Learning Regression of extinction in the second $Gaia$ Data Release

Machine learning has become a popular tool to help us make better decisions and predictions, based on experiences, observations and analysing patterns within a given data set without explicitly functions. In this paper, we describe an application of the supervised machine-learning algorithm to the extinction regression for the second Gaia data release, based on the combination of Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Extension for Galactic Understanding and Exploration and the Apache Point Observatory Galactic Evolution Experiment. The derived extinction in our training sample is consistent with other spectrum-based estimates, and its standard deviation of the cross validations is 0.0127 mag. A blind test is carried out using the RAdial Velocity Experiment catalog, and the standard deviation is 0.0372 mag. Such precise training sample enable us to regress the extinction, E(BP-RP), for 133 million stars in the second Gaia data release. Of these, 106 million stars have the uncertainties less than 0.1 mag, which suffer less bias from the external regression. We also find that there are high deviations between the extinctions form photometry-based methods, and between spectrum- and photometry-based methods. This implies that spectrum-based method could bring more signal to a regressing model than multi-band photometry, and a higher signal-to-noise ratio would acquire a more reliable result.

astro-ph.SR↗

arXiv2019

Machine Learning Regression of stellar effective temperatures in the second $Gaia$ Data Release

This paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second $Gaia$ data release, based on the combination of the stars in four spectroscopic surveys: Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Extension for Galactic Understanding and Exploration, the Apache Point Observatory Galactic Evolution Experiment and the RAdial Velocity Extension. This combination, about four million stars, enables us to construct one of the largest training sample for the regression, and further predict reliable stellar temperatures with a root-mean-squared error of 191 K. This result is more precise than that given by $Gaia$ second data release that is based on about sixty thousands stars. After a series of data cleaning processes, the input features that feed the regressor are carefully selected from the $Gaia$ parameters, including the colors, the 3D position and the proper motion. These $Gaia$ parameters is used to predict effective temperatures for 132,739,323 valid stars in the second $Gaia$ data release. We also present a new method for blind tests and a test for external regression without additional data. The machine-learning algorithm fed with the parameters only in one catalog provides us an effective approach to maximize sample size for prediction, and this methodology has a wide application prospect in future studies of astrophysics.

astro-ph.SR↗

arXiv2018

Machine learning Applied to Star-Galaxy-QSO Classification and Stellar Effective Temperature Regression

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised machine-learning algorithm, random forests (RF), to the star/galaxy/QSO classification and the stellar effective temperature regression based on the combination of LAMOST and SDSS spectroscopic data. This combination enable us to obtain reliable predictions with one of the largest training sample ever used. The training samples are built with nine-color data set of about three million objects for the classification and seven-color data set of over one million stars for the regression. The performance of the classification and regression is examined with the validation and the blind tests on the objects in the RAVE, 6dFGS, UVQS and APOGEE surveys. We demonstrate that the RF is an effective algorithm with the classification accuracies higher than 99\% for the stars and the galaxies, and higher than 94\% for the QSOs. These accuracies are higher than the machine-learning results in the former studies. The total standard deviations of the regression are smaller than 200 K that is similar to those of some spectrum-based methods. The machine-learning algorithm with the broad-band photometry provides us a more efficient approach to deal with massive amounts of astrophysical data than traditional color-cuts and SED fit.

astro-ph.GA↗

arXiv2018

Machine Learning Classification of Gaia Data Release 2

Machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We apply the machine learning classification to 85,613,922 objects in the $Gaia$ data release 2, based on the combination of the Pan-STARRS 1 and AllWISE data. The classification results are cross-matched with Simbad database, and the total accuracy is 91.9%. Our sample is dominated by stars, $\sim$ 98%, and galaxies makes up 2%. For the objects with negative parallaxes, about 2.5\% are galaxies and QSOs, while about 99.9% are stars if the relative parallax uncertainties are smaller than 0.2. Our result implies that using the threshold of 0 $< σ_π/π<$ 0.2 could yield a very clean stellar sample.

astro-ph.SR↗

arXiv2018

Gaia Calibrated UV Luminous Stars in LAMOST

We take advantage of the Gaia data release 2 to present 275 and 1,774 ultraviolet luminous stars in the FUV and the NUV. These stars are 5$σ$ exceeding the centers of the reference frame that is built with over one million UV stars in the log $g$ vs $T_\textrm{eff}$ diagram. The Galactic extinction is corrected with the 3D dusty map. In order to limit the Lutz-Kelker effect to an insignificant level, we select the stars with the relative uncertainties of the luminosity less than 40% and the trigonometric parallaxes less than 20%. We cross-identified our sample with the catalogs of RR Lyr stars and possible white dwarf main-sequence binaries, and find they compose $\sim$ 62% and $\sim$ 16% of our sample in the FUV and NUV, respectively. This catalog provides a unique sample to study stellar activity, spectrally unresolved compact main-sequence binaries and variable stars.

astro-ph.SR↗

arXiv2018

The UV Emission of Stars in LAMOST Survey I. Catalogs

We present the ultraviolet magnitudes for over three million stars in the LAMOST survey, in which 2,202,116 stars are detected by $GALEX$. For 889,235 undetected stars, we develop a method to estimate their upper limit magnitudes. The distribution of (FUV $-$ NUV) shows that the color declines with increasing effective temperature for stars hotter than 7000 K in our sample, while the trend disappears for the cooler stars due to upper atmosphere emission from the regions higher than their photospheres. For stars with valid stellar parameters, we calculate the UV excesses with synthetic model spectra, and find that the (FUV $-$ NUV) vs. $R'_{\mathrm{FUV}}$ can be fitted with a linear relation and late-type dwarfs tend to have high UV excesses. There are 87,178 and 1,498,103 stars detected more than once in the visit exposures of $GALEX$ in the FUV and NUV, respectively. We make use of the quantified photometric errors to determine statistical properties of the UV variation, including intrinsic variability and the structure function on the timescale of days. The overall occurrence of possible false positives is below 1.3\% in our sample. UV absolute magnitudes are calculated for stars with valid parallaxes, which could serve as a possible reference frame in the NUV. We conclude that the colors related to UV provide good criteria to distinguish between M giants and M dwarfs, and the variability of RR Lyrae stars in our sample is stronger than that of other A and F stars.

astro-ph.SR↗

arXiv2016

Time-Variable Aluminum Absorption in the Polar AR Ursae Majoris, and an Updated Estimate for the Mass of the White Dwarf

We present spectra of the extreme polar AR Ursae Majoris (AR UMa) which display a clear Al I absorption doublet, alongside spectra taken less than a year earlier in which that feature is not present. Re-examination of earlier SDSS spectra indicates that the Al I absorption doublet was also present $\approx$8 years before our first non-detection. We conclude that this absorbing material is unlikely to be on the surface of either the white dwarf (WD) or the donor star. We suggest that this Al I absorption feature arises in circumstellar material, perhaps produced by the evaporation of asteroids as they approach the hot WD. The presence of any remaining reservoir of rocky material in AR UMa might help to constrain the prior evolution of this unusual binary system. We also apply spectral decomposition to find the stellar parameters of the M dwarf companion, and attempt to dynamically measure the mass of the WD in AR UMa by considering both the radial velocity curves of the H$_β$ emission line and the Na I absorption line. Thereby we infer a mass range for the WD in AR UMa of 0.91 $M_{\odot}$ $<$ $M_{\mathrm{WD}}$ $<$ 1.24 $M_{\odot}$.

astro-ph.HE↗

arXiv2015

Does the 62-day X-ray periodicity come from ULX M82 X-1?

M82 X-1 is the brightest ultraluminous X-ray source in starburst galaxy M82 and is one of the best intermediate mass black hole candidates. Previous studies based on the Rossi X-ray Timing Explorer/Proportional Counter Array (RXTE/PCA) reported a regular X-ray flux modulation of M82 with a period of 62 days, and attributed this periodic modulation to M82 X-1. However, this modulation is not necessarily from M82 X-1 because RXTE/PCA has a very poor spatial resolution of ~1 degree. In this work, we analyzed 1000 days of monitoring data of M82 X-1 from the Swift/X-ray telescope (XRT), which has a much better spatial resolution than RXTE/PCA. The periodicity distribution map of M82 reveals that the 62-day periodicity is most likely not from M82 X-1, but from the summed contributions of several periodic X-ray sources 4 arcsec southeast of M82 X-1. However, Swift/XRT is not able to resolve those periodic sources and locate the precise origin of the periodicity of M82. Thus, more long-term observations with higher spatial resolution are required.

astro-ph.HE↗

arXiv2015

Spectroscopic Studies of an Ultraluminous Supersoft X-Ray Source in M81

Ultraluminous supersoft X-ray sources (ULSs) exhibit supersoft X-ray spectra with blackbody temperatures below 0.1 keV and bolometric luminosities above 10$^{39}$ ergs s$^{-1}$. In this Letter, we report the first optical spectroscopic observations of a ULS in M81 using the LRIS spectrograph on the Keck I telescope. The detected Balmer emission lines show a mean intrinsic velocity dispersion of 400$\pm$80 km s$^{-1}$, which is consistent with that from an accretion disk. The spectral index of the continuum on the blue side is also consistent with the multi-color disk model. The H$_α$ emission line exhibits a velocity of $\sim$180 km s$^{-1}$ relative to the local stellar environment, suggesting that this ULS may be a halo system in M81 belonging to an old population. No significant shift is found for the H$_α$ emission line between two observations separated by four nights.

astro-ph.GA↗

arXiv2015

An Updated Ultraviolet Catalog of GALEX Nearby Galaxies

The ultraviolet catalog of nearby galaxies made by \citet{Gil07} presents the integrated photometry and surface brightness profiles for 1034 nearby galaxies observed by \textit{Galaxy Evolution Explorer} (\textit{GALEX}). We provide an updated catalog of 4138 nearby galaxies based on the latest Genral Release (GR6/GR7) of \textit{GALEX}. These galaxies are selected from HyperLeda with apparent diameter larger than 1{\arcmin}. From the surface brightness profiles accurately measured with the deep NUV and FUV images, we have calculated asymptotic magnitudes, aperture (D25) magnitudes, colors, structural parameters (effective radii and concentration indices), luminosities, and effective surface brightness. Archival optical and infrared photometry from HyperLeda, 2MASS, and IRAS are also integrated into the catalog. Our parameter measurements and some analyses are consistent with those of \citet{Gil07}. The (FUV $- K$) color provides a good criterion to distinguish early and late-type galaxies, which can be improved further with the concentration indices. The IRX-$β$ relation is reformulated with our UV-selected nearby galaxies.

astro-ph.GA↗

arXiv2009

A census of X-ray nuclear activity in nearby galaxies

We have studied the X-ray nuclear activity of 187 nearby (distance < 15 Mpc) galaxies observed with Chandra/ACIS. We found that 86 of them have a point-like X-ray core, consistent with an accreting black hole (BH). We argue that the majority of them are nuclear BHs, rather than X-ray binaries. The fraction of galaxies with an X-ray detected nuclear BH is higher (~60 per cent) for ellipticals and early-type spirals (E to Sb), and lower (~30 per cent) for late-type spirals (Sc to Sm). There is no preferential association of X-ray cores with a large-scale bar; in fact, strongly barred galaxies appear to have slightly lower detection fraction and luminosity for their nuclear X-ray sources, compared with non-barred or weakly barred galaxies of similar Hubble types. The cumulative luminosity distribution of the nuclear sources in the 0.3-8 keV band is a power-law with slope ~-0.5, from ~2 x 10^{38} erg/s to ~10^{42} erg/s. The Eddington ratio is lower for ellipticals (L_{X}/L_{Edd} ~ 10^{-8}) and higher for late-type spirals (up to L_{X}/L_{Edd} ~ 10^{-4}), but in all cases, the accretion rate is low enough to be in the radiatively-inefficient regime. The intrinsic NH is generally low, especially for the less luminous sources: there appear to be no Type-2 nuclear BHs at luminosities <~ 10^{39} erg/s. The lack of a dusty torus or of other sources of intrinsic absorption (e.g., an optically-thick disk wind) may be directly related to the lack of a standard accretion disk around those faint nuclear BHs. The fraction of obscured sources increases with the nuclear BH luminosity: 2/3 of the sources with L_{X} > 10^{40} erg/s have a fitted NH > 10^{22} cm^{-2}. This is contrary to the declining trend of the obscured fraction with increasing luminosities, observed in more luminous AGN and quasars.

astro-ph.HE↗