J/A+A/698/A300 SLAM-payne for hot stars in LAMOST (Sun, 2025)
A hybrid SLAM-Payne framework for atmospheric parameter and abundance
determination of early-type stars from LAMOST DR9 low-resolution spectra.
Sun W.
<Astron. Astrophys. 698, A300 (2025)>
=2025A&A...698A.300S 2025A&A...698A.300S (SIMBAD/NED BibCode)
ADC_Keywords: Stars, B-type ; Stars, A-type ; Stars, fundamental
Keywords: Stars: abundances - stars: early-type - Galaxy: abundances -
Galaxy: steller content
Abstract:
Early-type stars are key drivers of Galactic chemical evolution,
enriching the interstellar medium with alpha elements through powerful
stellar winds and core-collapse supernovae, fuelled by their short
lifetimes and high masses. However, their spectra remain challenging
to analyse due to rapid rotation, weak metal lines, and non-local
thermodynamic equilibrium (NLTE) effects. While large spectroscopic
surveys provide extensive low-resolution data, extracting reliable
parameters remains difficult due to methodological limitations for hot
stars.
Our goal is to develop a unified framework that combines data-driven
and synthetic spectral approaches to determine atmospheric parameters
and abundances for hot stars using low-resolution spectra, thereby
addressing limitations in current methodologies while retaining
critical spectral information.
We present a hybrid approach integrating the Stellar LAbel Machine
(SLAM) and the Payne frameworks, for low-resolution (R∼1800) spectra
from LAMOST DR9. Our method preserves full spectral information
including Balmer series and metal-line blends, employing
neural-network interpolation for efficient parameter estimation (Teff,
logg, and v sin i) and abundance determination for O, Mg, Si, and Fe,
across 8000K-20000K.
We derive stellar parameters and abundances for 315822 stars with
S/N∼10 in the r-band. Among these, we identify 3564 blue horizontal
branch candidates, over 90% of which align with stellar evolutionary
models of horizontal branch stars. Additionally, we detect abundance
trends ([alpha/Fe]-[Fe/H]) that exhibit temperature-dependent
systematics and a distinct alpha-poor stellar population within
0.0≤[Fe/H]≤0.5dex. The radial abundance gradients are negative and
consistent with that derived from Cepheids, with a slope of
-0.070±0.007 in [Fe/H] in the region 6≤RGC≤15kpc.
Description:
We present a hybrid approach integrating the Stellar LAbel Machine
(SLAM) and the Payne frameworks, for low-resolution (R∼1800) spectra
from LAMOST DR9. Our method preserves full spectral information
including Balmer series and metal-line blends, employing
neural-network interpolation for efficient parameter estimation (Teff,
logg, and vsini) and abundance determination for O, Mg, Si, and Fe
across 8000-20000K. We derive stellar parameters and abundances for
315822 stars with SNR>10 in the r-band.
Our stellar parameter catalog contains Teff, logg, vsini, [Fe/H],
[O/Fe], [Mg/Fe] and [Si/Fe] for 315822 unique stars with Teff>7000K
and S/N>10 from LAMOST LRS DS9.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
catalog.dat 419 315822 Stellar parameter catalog
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See also:
I/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)
Byte-by-byte Description of file: catalog.dat
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Bytes Format Units Label Explanations
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1- 9 I9 --- ObsID LAMOST LRS unique spectrum ID
11- 21 F11.7 deg RAdeg Fiber pointing right ascension (J2000)
23- 33 E11.3 deg DEdeg Fiber pointing declination (J2000)
35- 53 I19 --- GaiaDR3 Gaia DR3 Source id
55- 73 F19.13 K Teff Effective temperature
75- 98 F24.16 K e_Teff Uncertainty of effective temperature
100-117 F18.16 [cm/s2] logg Surface gravity
119-142 F24.19 [cm/s2] e_logg Uncertainty of surface gravity
144-167 E24.16 [-] [Fe/H] Iron abundance
169-191 F23.19 [-] e_[Fe/H] Uncertainty of Iron abundance
193-212 F20.16 km/s vsini Rotational velocity
214-237 F24.17 km/s e_vsini Uncertainty of rotational velocity
239-262 E24.16 --- [O/Fe] Oxygen abundance
264-289 F26.19 --- e_[O/Fe] Uncertainty of Oxygen abundance
291-314 E24.16 --- [Mg/Fe] Magnesium abundance
316-341 F26.19 --- e_[Mg/Fe] Uncertainty of Magnesium abundance
343-366 E24.16 --- [Si/Fe] Silicon abundance
368-393 F26.19 --- e_[Si/Fe] Uncertainty of Silicon abundance
395-419 F25.17 --- chi2 ? chi-square value
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Acknowledgements:
Weijia Sun, wsun(at)aip.de
(End) Patricia Vannier [CDS] 15-May-2025