相关论文: Machine Learning Regression of extinction in the s…

High-contrast imaging data analysis depends on removing residual starlight from the host star to reveal planets and disks. Most observers do this with principal components analysis (i.e. KLIP) using modes computed from the science images…

The majority of massive stars are born in binaries, and most unbind upon the first supernova. With precise proper motion surveys such as Gaia, it is possible to trace back the motion of stars in the vicinity of young remnants to search for…

Gaia is a fully-approved all-sky astrometric and photometric survey due for launch in 2011. It will measure accurate parallaxes and proper motions for everything brighter than G=20 (ca. 10^9 stars). Its primary objective is to study the…

Membership studies characterising open clusters with Gaia data, most using DR2, are so far limited at magnitude G = 18 due to astrometric uncertainties at the faint end. Our goal is to extend current open cluster membership lists with faint…

We have obtained accurate dust reddening from far-ultraviolet (UV) to the mid-infrared (IR) for up to 5 million stars by the star-pair algorithm based on LAMOST stellar parameters along with GALEX, Pan-STARRS 1, Gaia, SDSS, 2MASS, and WISE…

The Extreme Deconvolution method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting…

The second Gaia data release is based on 22 months of mission data with an average of 0.9 billion individual CCD observations per day. A data volume of this size and granularity requires a robust and reliable but still flexible system to…

We present a new atmospheric extinction curve for Mauna Kea spanning 3200--9700 \AA. It is the most comprehensive to date, being based on some 4285 standard star spectra obtained on 478 nights spread over a period of 7 years obtained by the…

We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit…

Gaia mission will offer an exceptional opportunity to perform variability studies. The data homogeneity, its optimised photometric systems, composed of 11 medium and 4-5 broad bands, the high photometric precision in G band of one milli-mag…

We study a set of solar neighborhood ($d < 3$ kpc) stars from Gaia Data Release 2 to determine azimuthal star count differences, i.e., left and right of the line from the Galactic center through the sun - and compare these differences north…

Gaia Data Release 2 (Gaia DR2) contains results for 1693 million sources in the magnitude range 3 to 21 based on observations collected by the European Space Agency Gaia satellite during the first 22 months of its operational phase. We…

We present a method to simultaneously infer the interstellar extinction parameters $A_0$ and $R_0$, stellar effective temperature $T_{\rm eff}$, and distance modulus $\mu$ in a Bayesian framework. Using multi-band photometry from SDSS and…

We investigate whether Gaia can specify the binary fractions of massive stellar populations in the Galactic disk through astrometric microlensing. Furthermore, we study if some information about their mass distributions can be inferred via…

Aims: We explore the wealth of high quality photometric data provided by data release 2 of the Gaia mission for long period variables (LPVs) in the Large Magellanic Cloud. Our goal is to identify stars of various types and masses along the…

We develop a new machine learning algorithm, Via Machinae, to identify cold stellar streams in data from the Gaia telescope. Via Machinae is based on ANODE, a general method that uses conditional density estimation and sideband…

In this paper we generalize the flux ratio method Bailey et al. (2009) to the case of two luminosity indicators and search the optimal luminosity-flux ratio relations on a set of spectra whose phases are around not only the date of bright…

Achieving maximum scientific results from the overwhelming volume of astronomical data to be acquired over the next few decades will demand novel, fully automatic methods of data analysis. Artificial intelligence approaches hold great…

Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine…

We develop statistical methods within a Bayesian framework to infer the star formation history from photometric surveys of pre-main sequence populations. Our procedures include correcting for biases due to extinction in magnitude-limited…