Related papers: A Machine Learning Method to Infer Fundamental Ste…
High-resolution spectroscopic measurements of OB stars are important for understanding processes like stellar evolution, but require labor-intensive observations. In contrast, photometric missions like the Transiting Exoplanet Survey…
The accuracy of the estimated stellar atmospheric parameter decreases evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there are a huge amount of this kind observations, especially in case of SNR$<$30. Therefore, it…
Extremely metal-poor (EMP) stars ([Fe/H] < -3.0 dex) provide a unique window into understanding the first generation of stars and early chemical enrichment of the Universe. EMP stars are exceptionally rare, however, and the relatively small…
In the era of exploding survey volumes, traditional methods of spectroscopic analysis are being pushed to their limits. In response, we develop deep-REMAP, a novel deep learning framework that utilizes a regularized, multi-task approach to…
Aims. This paper introduces LRPayne, a novel algorithm designed for the efficient determination of stellar parameters and chemical abundances from low-resolution optical spectra, with a primary focus on data from large-scale galactic…
The increasing number of spectra gathered by spectroscopic sky surveys and transiting exoplanet follow-up has pushed the community to develop automated tools for atmospheric stellar parameters determination. Here we present a novel approach…
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…
Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…
Star formation rates (SFRs) are a crucial observational tracer of galaxy formation and evolution. Spectroscopy, which is expensive, is traditionally used to estimate SFRs. This study tests the possibility of inferring SFRs of large samples…
Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and brightness limits restrain the efficiency of spectral…
Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to…
The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of…
This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural…
Studies of Galactic chemical and dynamical evolution in the solar neighborhood depend on the availability of precise atmospheric parameters (Teff, [Fe/H] and log g) for solar-type stars. Many large-scale spectroscopic surveys operate at low…
In the current panorama of large surveys, the vast amount of data obtained with different methods, data types, formats, and stellar samples, is making an efficient use of the available information difficult. The Survey of Surveys is a…
We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on…
As a typical data-driven method, deep learning becomes a natural choice for analysing astronomical data nowadays. In this study, we built a deep convolutional neural network to estimate basic stellar parameters $T\rm{_{eff}}$, log g,…
We describe a scheme to extract linearly supporting (LSU) features from stellar spectra to automatically estimate the atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H]. "Linearly supporting" means that the atmospheric parameters can be…
The asteroseismic and planetary studies, like all research related to stars, need precise and accurate stellar atmospheric parameters as input. We aim at deriving the effective temperature (Teff), the surface gravity (log g), the…
This article investigates the problem of estimating stellar atmospheric parameters from spectra. Feature extraction is a key procedure in estimating stellar parameters automatically. We propose a scheme for spectral feature extraction and…