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All stellar-mass black holes have hitherto been identified by X-rays emitted from gas that is accreting onto the black hole from a companion star. These systems are all binaries with a black-hole mass that is less than 30 times that of the Sun 1 – 4 . Theory predicts, however, that X-ray-emitting systems form a minority of the total population of star–black-hole binaries 5 , 6 . When the black hole is not accreting gas, it can be found through radial-velocity measurements of the motion of the companion star. Here we report radial-velocity measurements taken over two years of the Galactic B-type star, LB-1. We find that the motion of the B star and an accompanying Hα emission line require the presence of a dark companion with a mass of 68 − 13 + 11 solar masses, which can only be a black hole. The long orbital period of 78.9 days shows that this is a wide binary system. Gravitational-wave experiments have detected black holes of similar mass, but the formation of such massive ones in a high-metallicity environment would be extremely challenging within current stellar evolution theories. Radial-velocity measurements of a Galactic B-type star show a dark companion that seems to be a black hole of about 68 solar masses, in a widely spaced binary system.

The double revolving fiber positioning unit (FPU) is one of the key technologies of The Large Sky Area Multi-Object Fiber Spectroscope Telescope (LAMOST). The positioning accuracy of the computer controlled FPU depends on robot accuracy as well as the initial parameters of FPU. These initial parameters may deteriorate with time when FPU is running in non-supervision mode, which would lead to bad fiber position accuracy and further efficiency degradation in the subsequent surveys. In this paper, we present an algorithm based on deep learning to detect the FPU’s initial angle using the front illuminated image of LAMOST focal plane. Preliminary test results show that the detection accuracy of the FPU initial angle is better than 2.°5, which is good enough to distinguish those obvious bad FPUs. Our results are further well verified by direct measurement of fiber position from the back illuminated image and the correlation analysis of the spectral flux in LAMOST survey data.

The double revolving fiber positioning unit (FPU) is one of the key technologies of The Large Sky Area Multi-Object Fiber Spectroscope Telescope (LAMOST). The positioning accuracy of the computer controlled FPU depends on robot accuracy as well as the initial parameters of FPU. These initial parameters may deteriorate with time when FPU is running in non-supervision mode, which would lead to bad fiber position accuracy and further efficiency degradation in the subsequent surveys. In this paper, we present an algorithm based on deep learning to detect the FPU’s initial angle using the front illuminated image of LAMOST focal plane. Preliminary test results show that the detection accuracy of the FPU initial angle is better than 2°.5, which is good enough to distinguish those obvious bad FPUs. Our results are further well verified by direct measurement of fiber position from the back illuminated image and the correlation analysis of the spectral flux in LAMOST survey data.

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.

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.

Currently large sky area spectral surveys like SDSS, 2dF, and LAMOST, using the new generation of telescopes and observatories, have provided massive spectral data sets for astronomical research. Most of the data can be automatically handled with pipelines, but visually inspection by human eyes is still necessary in several situations, like low SNR spectra, QSO recognition and peculiar spectra mining. Using ASERA, A Spectrum Eye Recognition Assistant, we can set up a team spectral inspection platform. On a preselected spectral data set, members of a team can individually view spectra one by one, find the best match template and estimate the redshift. Results from different members will be gathered and merged to raise the team work efficiency. ASERA mainly targets the spectra of SDSS and LAMOST fits data formats. Other formats can be supported with some conversion. Spectral templates from SDSS and LAMOST pipelines are embedded and users can easily add their own templates. Convenient cross identification interfaces with SDSS, SIMBAD, VIZIER, NED and DSS are also provided. An application example targeting finding strong emission line spectra from LAMOST DR2 is presented.

With the concern that observed flux is very sensitive upon fiber positions, we develop a novel approach measuring the error of fiber positions in this work. More specifically, we compute two orthogonal groups of the flux ratio before and after moving the fiber a few arcseconds, and correct the system coordinate transformation based on the computed fiber position error.

We first introduce the primary target allocation requirements and restrictions for the parallel control multiple fiber system, which is used in the LAMOST spectroscopic survey. The fiber positioner anti-collision model is imported. Then several target allocation methods and features are discussed in detail, including a network flow algorithm, high priority for fiber unit holding less target number, target allocation algorithm for groups, target allocation method for add-ons and target reallocation. Their virtues and weaknesses are analyzed for various kinds of scientific research situations. Furthermore an optimization concept using the Simulate Anneal Arithmetic (SAA) is developed to improve the fiber utilizing efficiency.

The Extremely Low Mass White Dwarfs (ELM WDs) and pre-ELM WDs are helium core white dwarfs with mass \\(<\\sim 0.3M_{\\odot}\\). Evolution simulations show that a lower mass limit for ELM WDs exists at \\(\\approx0.14M_{\\odot}\\) and no one is proposed by observation to be less massive than that. Here we report the discovery of a binary system, LAMOST J224040.77-020732.8 (J2240 in short), which consists of a very low mass hot star and a compact companion. Multi-epoch spectroscopy shows an orbital period \\(P_{orb} =\\)0.219658\\(\\pm0.000002\\) days and a radial velocity semi-amplitude \\(K1=318.5\\pm3.3km/s\\), which gives the mass function of 0.74\\(M_{\\odot}\\), indicating the companion is a compact star. The F-type low resolution spectra illustrate no emission features, and the temperature (\\(\\sim 7400K\\)) is consistent with that from Spectral Energy Distribution fitting and multi-color light curve solution. The optical light curves, in ZTF g, r and i bands and Catalina V band, show ellipsoidal variability with amplitudes \\(\\sim30\\%\\), suggesting that the visible component is heavily tidally distorted. Combining the distance from Gaia survey, the ZTF light curves are modeled with Wilson-Devinney code and the result shows that the mass of the visible component is \\(M1=0.085^{+0.036}_{-0.024}M_{\\odot}\\), and the mass of the invisible component is \\(M2=0.98^{+0.16}_{-0.09}M_{\\odot}\\). The radius of the visible component is \\(R1=0.29^{+0.04}_{-0.03}R_{\\odot}\\). The inclination angle is approximately between 60\\(^{\\circ}\\) and 90\\(^{\\circ}\\). The observations indicate the system is most likely a pre-ELM WD + WD/NS binary, and the mass of pre-ELM is possibly lower than the \\(0.14M_{\\odot}\\) theoretical limit.

We report the discovery of one possible neutron star binary (\\(P_{\\rm orb} =\\) 0.8666 day) by using the LAMOST low-resolution spectroscopic data. The visible companion is a late A-type dwarf (\\(T_{\\rm eff} = 7900 \\pm 200\\) K; log\\(g\\) \\(=\\) 4.3\\(\\pm\\)0.2; \\(M =\\) 1.7\\(\\pm\\)0.1 M\\(_{\\odot}\\); \\(R\\ =\\ 1.7\\pm0.2\\) R\\(_{\\odot}\\)), at a distance of 1.11\\(\\pm0.03\\) kpc. No double-lined feature can be seen from the GTC/HORuS high-resolution spectra, thus the radial velocity variation indicates an invisible object hiding in the binary. The system's optical light curves show clear ellipsoidal variability, suggesting that the visible companion is tidal distorted. By fitting the multi-band light curves with the ELC and WD codes, we constrain the mass of the invisible star to be 1.1--1.3 M\\(_{\\odot}\\). Spectral disentangling shows no additional component with optical absorption spectra, supporting the system contains one compact object. No X-ray or UV emission are detected in the ROSAT archive observations. Therefore, we suspect the invisible object is more likely a neutron star rather than a white dwarf. Our finding suggests the ability of LAMOST spectroscopic survey to discover X-ray quiescent compact objects.