SIMBAD references

2018MNRAS.475.2978F - Mon. Not. R. Astron. Soc., 475, 2978-2993 (2018/April-2)

An application of deep learning in the analysis of stellar spectra.

FABBRO S., VENN K.A., O'BRIAIN T., BIALEK S., KIELTY C.L., JAHANDAR F. and MONTY S.

Abstract (from CDS):

Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here, we apply a deep neural network architecture to analyse both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same data sets; however, StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys.

Abstract Copyright: © 2017 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Journal keyword(s): methods: numerical - techniques: spectroscopic - surveys - stars: fundamental parameters - infrared: stars

Simbad objects: 116

goto Full paper

goto View the reference in ADS

To bookmark this query, right click on this link: simbad:2018MNRAS.475.2978F and select 'bookmark this link' or equivalent in the popup menu


2020.09.22-14:41:32

© Université de Strasbourg/CNRS

    • Contact