SDSS spectroscopic survey of stars

Ivezić, Ž.; Schlegel, David J.; Uomoto, Alan; Bond, Nicholas A.; Prieto, C. Allende; Wilhelm, Ronald; Lee, Y. Sun; Jurić, Mario; Lupton, R.; Rockosi, C. M.; Knapp, G.R.; Gunn, J. E.; Yanny, B.; Jester, Sebastian; Kent, S.; Pier, Jeff; Munn, J. A.; Richards, G.; Newberg, Heidi Jo; Blanton, Michael R.; Eisenstein, Daniel; Hawley, Suzanne L.; Anderson, S. B.; Harris, Hugh C.; Kiuchi, Furea; Chen, A.; Bushong, James; Sohi, Harkirat; Haggard, Daryl; Kimball, Amy; Barentine, John C.; Brewington, H.; Harvanek, M.; Kleinman, S. J.; Krzesinski, J.; Long, Douglas; Nitta, A.; Snedden, Stephanie A.

doi:10.48550/arxiv.astro-ph/0701509

Cited by 1 publication

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“…(1) Converting wavelength provided by LAMOST to rest frame. This will eliminate the effect of the radial velocity on spectra, as shown in Equation (1).…”

Section: Pre-processingmentioning

confidence: 99%

“…With the advancement of spectroscopic surveys, a vast amount of stellar spectra have been acquired. The SEGYE plan by SDSS involved sampling stars from different distances to capture spatial substructures in the Galactic stellar halo [1]. The Anglo-Australian Observatory utilized the Schmidt telescope to determine the radial velocity and parameters of stars [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ESNet: Estimating Stellar Parameters from LAMOST Low-Resolution Stellar Spectra

Wang,

Qiu,

Luo

et al. 2023

Universe

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Stellar parameters are estimated through spectra and are crucial in studying both stellar evolution and the history of the galaxy. To extract features from the spectra efficiently, we present ESNet (encoder selection network for spectra), a novel architecture that incorporates three essential modules: a feature encoder (FE), feature selection (FS), and feature mapping (FM). FE is responsible for extracting advanced spectral features through encoding. The role of FS, on the other hand, is to acquire compressed features by reducing the spectral dimension and eliminating redundant information. FM comes into play by fusing the advanced and compressed features, establishing a nonlinear mapping between spectra and stellar parameters. The stellar spectra used for training and testing are obtained through crossing LAMOST and SDSS. The experimental results demonstrate that for low signal-to-noise spectra (0–10), ESNet achieves excellent performance on the test set, with mean absolute error (MAE) values of 82 K for Teff (effective temperature), 0.20 dex for logg (logarithm of the gravity), and 0.10 dex for [Fe/H] (metallicity). The results indeed indicate that ESNet has an excellent ability to extract spectral features. Furthermore, this paper validates the consistency between ESNet predictions and the SDSS catalog. The experimental results prove that the model can be employed for the evaluation of stellar parameters.

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“…(1) Converting wavelength provided by LAMOST to rest frame. This will eliminate the effect of the radial velocity on spectra, as shown in Equation (1).…”

Section: Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%