Related papers: Estimating Stellar Parameters from LAMOST Low-reso…
We present a data-driven method to estimate absolute magnitudes for O- and B-type stars from the LAMOST spectra, which we combine with {\it Gaia} parallaxes to infer distance and binarity. The method applies a neural network model trained…
Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution…
We present integrated optical spectrophotometry for a sample of 417 nearby galaxies. Our observations consist of spatially integrated, S/N=10-100 spectroscopy between 3600 and 6900 Angstroms at ~8 Angstroms FWHM resolution. In addition, we…
With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the…
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), also known as the Guoshoujing Telescope, is a major national scientific facility for astronomical research located in Xinglong, China. Beginning with a pilot survey in…
High-precision stellar mass and radius measured directly from binaries can effectively calibrate the stellar models. However, such a database containing full spectral types and large range of metallicity is still not fully established. A…
The plethora of spectra of OB-type stars in observatory archives and the much larger numbers to come from the WEAVE and 4MOST spectroscopic facilities require efficient, but also accurate and precise methods for (semi)automatic quantitative…
We present a novel approach to analyzing astronomical spectral survey data using our non-linear extension of an online dictionary learning algorithm. Current and upcoming surveys such as SPHEREx will use spectral data to build a 3D map of…
We measure carbon and nitrogen abundances to $\lesssim$ 0.1 dex for 450,000 giant stars from their low-resolution (R$\sim$1800) LAMOST DR2 survey spectra. We use these [C/M] and [N/M] measurements, together with empirical relations based on…
We present a database of 45,000 atmospheric models (which will become 80,000 models by the end of the project) with stellar masses between 9 and 120 M$_{\odot}$, covering the region of the OB main sequence and W-R stars in the H-R diagram.…
Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…
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…
$Aims.$ We present a database of 43,340 atmospheric models ($\sim$80,000 models at the conclusion of the project) for stars with stellar masses between 9 and 120 $M_{\odot}$, covering the region of the OB main-sequence and Wolf-Rayet (W-R)…
We present a novel approach to deriving stellar labels for stars observed in MUSE fields making use of data-driven machine learning methods. Taking advantage of the comparable spectral properties (resolution, wavelength coverage) of the…
We present a method estimating the atmospheric parameters Teff, log g, [Fe/H] for stars observed, even at low signal to noise ratio, with the echelle spectrograph ELODIE on the 193cm telescope at Observatoire de Haute-Provence. The method…
Asteroseismology is one of the most accurate approaches to estimate the surface gravity of a star. However, most of the data from the current spectroscopic surveys do not have asteroseismic measurements, which is very expensive and time…
We collect a sample of stars observed both in LAMOST and Gaia which have colors implying a temperature hotter than 7000 K. We train a machine learning algorithm on LAMOST spectroscopic data which has been tagged with stellar classifications…
Lithium is a fragile but crucial chemical element in the universe, exhibits interesting and complex behaviors. Thanks to the massive spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)…
Large sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science…
A simple, fully connected neural network with a single hidden layer is used to estimate stellar masses for star-forming galaxies. The model is trained on broad-band photometry - from far-ultraviolet to mid-infrared wavelengths - generated…