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

Context. The study of the oldest and most metal-poor stars in our Galaxy promotes our understanding of the Galactic chemical evolution and the beginning of Galaxy and star formation. However, they are notoriously difficult to find, with…

Estimating a distance by inverting a parallax is only valid in the absence of noise. As most stars in the Gaia catalogue will have non-negligible fractional parallax errors, we must treat distance estimation as a constrained inference…

There are many pertinent open issues in the area of star and planet formation. Large statistical samples of young stars across star-forming regions are needed to trigger a breakthrough in our understanding, but most optical studies are…

The nearest stars provide a fundamental constraint for our understanding of stellar physics and the Galaxy. The nearby sample serves as an anchor where all objects can be studied and understood with precise data. This work is an update of…

Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian…

The Gaia Data Release 2 (DR2): we summarise the processing and results of the identification of variable source candidates of RR Lyrae stars, Cepheids, long period variables (LPVs), rotation modulation (BY Dra-type) stars, delta Scuti & SX…

Atmospheric retrieval of exoplanets from spectroscopic observations requires an extensive exploration of a highly degenerate and high-dimensional parameter space to accurately constrain atmospheric parameters. Retrieval methods commonly…

We present a survey of the extinction properties of ten lensing galaxies, in the redshift range z = 0.04 - 1.01, using multiply lensed quasars imaged with the ESO VLT in the optical and near infrared. The multiple images act as 'standard…

(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…

The Galaxy and the stars in it form a hierarchical system, such that the properties of individual stars are influenced by those of the Galaxy. Here, an approach is described which uses hierarchical Bayesian models to simultaneously and…

We present a study on the determination of rotation periods (P) of solar-like stars from the photometric irregular time-sampling of the ESA Gaia mission, currently scheduled for launch in 2013, taking into account its dependence on ecliptic…

The Transiting Exoplanet Survey Satellite (TESS) is a wide-field all-sky survey mission designed to detect Earth-sized exoplanets. After over four years photometric surveys, data from sectors 1-57, including approximately 1,050,000 light…

Statistical studies of exoplanets and the properties of their host stars have been critical to informing models of planet formation. Numerous trends have arisen in particular from the rich Kepler dataset, including that exoplanets are more…

The second data release of the Gaia mission includes an advance catalog of variable stars. The classification of these stars are based on sparse photometry from the first 22 months of the mission. We set out to investigate the purity and…

Estimating stellar masses and radii is a challenge for most of the stars but their knowledge is critical for many different astrophysical fields. One of the most extended techniques for estimating these variables are the so-called empirical…

Estimating stellar masses for billions of galaxies in upcoming surveys requires methods that are both accurate and computationally efficient. We present a new approach using symbolic regression trained on a simulation to derive simple,…

We report the distances of molecular clouds at high Galactic latitudes (|b|>10$^\circ$) derived from parallax and G band extinction (A$_{\rm G}$) measurements in the second Gaia data release, Gaia DR2. Aided by Bayesian analyses, we…

We determine the local galactic extinction towards the field of gamma-ray burst GRB970228 using a variety of methods. We develop a maximum likelihood method for measuring the extinction by comparing galaxy counts in the field of interest to…

This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…

We develop a data-driven model to map stellar parameters (effective temperature, surface gravity and metallicity) accurately and precisely to broad-band stellar photometry. This model must, and does, simultaneously constrain the…