Directly Deriving Parameters from SDSS Photometric Images
Abstract Stellar atmospheric parameters (effective temperature, surface gravity, and metallicity) are fundamental for understanding the formation and evolution of stars and galaxies. Photometric data can provide a low-cost way to estimate these parameters, but traditional methods based on photometri...
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Published in | The Astronomical journal Vol. 166; no. 3; pp. 88 - 102 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Madison
The American Astronomical Society
01.09.2023
IOP Publishing |
Subjects | |
Online Access | Get full text |
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Summary: | Abstract
Stellar atmospheric parameters (effective temperature, surface gravity, and metallicity) are fundamental for understanding the formation and evolution of stars and galaxies. Photometric data can provide a low-cost way to estimate these parameters, but traditional methods based on photometric magnitudes have many limitations. In this paper, we propose a novel model called Bayesian Convit, which combines an approximate Bayesian framework with a deep-learning method, namely Convit, to derive stellar atmospheric parameters from Sloan Digital Sky Survey images of stars and effectively provide corresponding confidence levels for all the predictions. We achieve high accuracy for
T
eff
and [Fe/H], with
σ
(
T
eff
) = 172.37 K and
σ
([Fe/H]) = 0.23 dex. For
log
g
, which is more challenging to estimate from image data, we propose a two-stage approach: (1) classify stars into two categories based on their
log
g
values (>4 dex or <4 dex) and (2) regress separately these two subsets. We improve the estimation accuracy of stars with
log
g
>
4
dex significantly to
σ
(
log
g
>
4
)
=
0.052
dex, which are comparable to those based on spectral data. The final joint result is
σ
(
log
g
)
=
0.41
dex. Our method can be applied to large photometric surveys like Chinese Space Station Telescope and Large Synoptic Survey Telescope. |
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Bibliography: | Stars and Stellar Physics AAS46022 |
ISSN: | 0004-6256 1538-3881 |
DOI: | 10.3847/1538-3881/acdcfb |