Related papers: Estimating Stellar Parameters from LAMOST Low-reso…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
We present a technique which employs artificial neural networks to produce physical parameters for stellar spectra. A neural network is trained on a set of synthetic optical stellar spectra to give physical parameters (e.g. T_eff, log g,…
Understanding the evolution of the Milky Way calls for the precise abundance determination of many elements in many stars. A common perception is that deriving more than a few elemental abundances ([Fe/H], [$\alpha$/Fe], perhaps [C/H],…
The Planck mission provided all-sky dust emission maps in the submm to mm range at an angular resolution of 5'. In addition, some specific sources can be observed at long wavelengths and higher resolution using ground-based telescopes.…
Accurate determination of the stellar atmospheric parameters of RR Lyrae stars (RRLs) requires short individual exposures of the spectra to mitigate pulsation effects. We present improved template matching methods to determine the stellar…
A small fraction of giants possess photospheric lithium(Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In…
Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal intricate internal structures of these objects, allowing researchers to…
The Large sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution spectral survey of Galactic Nebulae (MRS-N) has conducted for three years since Sep. 2018 and observed more than 190 thousands nebular spectra and 20…
The LAMOST spectroscopic survey for Galactic structure and evolution has been in operation since October 2012, following a one-year pilot survey. The pilot survey produced a data release containing over 600,000 stellar spectra. By…
Since the release of LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope) catalog, we have the opportunity to use the LAMOST DR2 stellar catalog and \emph{WISE All-sky catalog} to search for 22 $\mu$m excess candidates. In…
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…
The nearly continuous light curves with micromagnitude precision provided by the space mission Kepler are revolutionising our view of pulsating stars. They have revealed a vast sea of low-amplitude pulsation modes that were undetectable…
Understanding star formation in resolved low mass systems requires the integration of information obtained from observations at different wavelengths. We have combined new and archival multi-wavelength observations on a set of 20 nearby…
We report an interpretation method for deep learning models that allows us to handle high-dimensional spectral data in materials science. The proposed method uses feature extraction and clustering analysis to categorize materials into…
The stellar spectra from LAMOST Medium Resolution Survey can be used to search for compact objects in binaries. The LAMOST DR10 catalog includes > 980, 000 targets with multiple medium resolution spectra. We select the targets with large or…
In the era of vast spectroscopic surveys focusing on Galactic stellar populations, astronomers want to exploit the large quantity and good quality of data to derive their atmospheric parameters without losing precision from automatic…
Estimating redshifts from broadband photometry is often limited by how accurately we can map the colors of galaxies to an underlying spectral template. Current techniques utilize spectrophotometric samples of galaxies or spectra derived…
Ultra metal-poor stars ([Fe/H] < -4.0) are very rare, and finding them is a challenging task. Both narrow-band photometry and low-resolution spectroscopy have been useful tools for identifying candidates, and in this work we combine both…
In this work, we select the high signal-to-noise ratio spectra of stars from the LAMOST data andmap theirMK classes to the spectral features. The equivalentwidths of the prominent spectral lines, playing the similar role as the multi-color…
Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys. In response, we develop deep-Regularized Ensemble-based Multi-task Learning with Asymmetric…