Related papers: The Cannon: A data-driven approach to stellar labe…

We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 million stars using the methodology developed in the first…

In this work we apply and expand on a recently introduced outlier detection algorithm that is based on an unsupervised random forest. We use the algorithm to calculate a similarity measure for stellar spectra from the Apache Point…

Deriving stellar atmospheric parameters and chemical abundances from stellar spectra is crucial for understanding the evolution of the Milky Way. By performing a fitting with MARCS model atmospheric theoretical synthetic spectra combined…

Upcoming next-generation sky surveys will detect large number of faint objects with magnitudes larger than 25. When objects are crowded within a limited a field of view, blending becomes unavoidable. Blending leads to the omission of many…

The chemical composition of a star's atmosphere reflects the chemical composition of its birth environment. Therefore, it should be feasible to recognize stars born together that have scattered throughout the galaxy, solely based on their…

Stellar parameters and abundances provide crucial insights into stellar and Galactic evolution studies. In this work, we developed a convolutional neural network (CNN) to estimate stellar parameters: effective temperature…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

In order to develop a pipeline for automated classification of stars to be observed by the TAUVEX ultraviolet space Telescope, we employ an artificial neural network (ANN) technique for classifying stars by using synthetic spectra in the UV…

Learning from noisy labels (LNL) is crucial in deep learning, in which one of the approaches is to identify clean-label samples from poorly-annotated datasets. Such an identification is challenging because the conventional LNL problem,…

Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify…

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…

In this work, we present the new catalog of carbon stars from the LAMOST DR2 catalog. In total, 894 carbon stars are identified from multiple line indices measured from the stellar spectra. Combining the CN bands in the red end with \ctwo\…

We present the identification of potential members of nearby Galactic globular clusters using radial velocities from the RAdial Velocity Experiment Data Release 4 (RAVE-DR4) survey database. Our identifications are based on three globular…

To calculate the model accuracy on a computer vision task, e.g., object recognition, we usually require a test set composing of test samples and their ground truth labels. Whilst standard usage cases satisfy this requirement, many…

Context. In the advent of new infrared, high-resolution spectrometers, accurate and precise atomic data in the infrared is urgently needed. Identifications, wavelengths, strengths, broadening and hyper-fine splitting parameters of stellar…

Aims: We developed a new method of estimating the stellar parameters Teff, log g, [M/H], and elemental abundances. This method was implemented in a new code, SP_Ace (Stellar Parameters And Chemical abundances Estimator). This is a highly…

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) has built the largest moderately high-resolution (R=22, 500) spectroscopic map of the stars across the Milky Way, and including dust-obscured areas. The APOGEE Stellar…

Interstellar molecules, which play an important role in astrochemistry, are identified using observed spectral lines. Despite the advent of spectral analysis tools in the past decade, the identification of spectral lines remains a tedious…

The possibility of identifying co-natal stars that have dispersed into the Galactic disc based on chemistry only is called strong chemical tagging. Its feasibility has been debated for a long time, with the promise of reconstructing the…

The RAdial Velocity Experiment (RAVE) is a spectroscopic survey of the Milky Way. We use the subsample of spectra with spectroscopically determined values of stellar parameters to determine the distances to these stars. The list currently…