J/ApJS/261/36 LAMOST DR7 stellar param. from RRNet model (Xiong+, 2022)
A model RRNet for spectral information exploitation and LAMOST Medium-Resolution
Spectrum parameter estimation.
Xiong S., Li X., Liao C.
<Astrophys. J. Suppl. Ser., 261, 36 (2022)>
=2022ApJS..261...36X 2022ApJS..261...36X
ADC_Keywords: Models; Spectra, optical; Abundances
Keywords: Astronomy data analysis ; Spectroscopy ; Stellar atmospheres ;
Stellar abundances
Abstract:
This work proposes a residual recurrent neural network (RRNet) for
synthetically extracting spectral information and estimating stellar
atmospheric parameters together with 15 chemical element abundances
for medium-resolution spectra from the Large Sky Area Multi-Object
Fiber Spectroscopic Telescope (LAMOST). The RRNet consists of two
fundamental modules: a residual module and a recurrent module. The
residual module extracts spectral features based on the longitudinally
driving power from parameters, while the recurrent module recovers
spectral information and restrains the negative influences from noises
based on Cross-band Belief Enhancement. RRNet is trained by the
spectra from common stars between LAMOST DR7 and the APOGEE-Payne
catalog. The 17 stellar parameters and their uncertainties for 2.37
million medium-resolution spectra from LAMOST DR7 are predicted. For
spectra with a signal-to-noise ratio ≥10, the precision of
estimations (Teff and logg) are 88K and 0.13dex, respectively,
elements C, Mg, Al, Si, Ca, Fe, and Ni are 0.05-0.08dex, and N, O, S,
K, Ti, Cr, and Mn are 0.09-0.14dex, while that of Cu is 0.19dex.
Compared with StarNet and SPCANet, RRNet shows higher accuracy and
robustness. In comparison to Apache Point Observatory Galactic
Evolution Experiment and Galactic Archaeology with HERMES surveys,
RRNet manifests good consistency within a reasonable range of bias.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
rrnetcat.dat 856 2377510 The LAMOST-RRNet catalog
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See also:
V/146 : LAMOST DR1 catalogs (Luo+, 2015)
V/156 : LAMOST DR7 catalogs (Luo+, 2019)
J/AJ/137/4377 : List of SEGUE plate pairs (Yanny+, 2009)
J/other/RAA/11.924 : Atmospheric parameters for 771 stars (Wu+, 2011)
J/AJ/151/144 : ASPCAP weights for the APOGEE elements (Garcia+, 2016)
J/AJ/156/126 : Stellar parameters from BACCHUS analysis (Jonsson+, 2018)
J/ApJS/245/34 : Abundances for 6 million stars from LAMOST DR5 (Xiang+, 2019)
J/ApJ/891/23 : Stellar abundances from LAMOST MRS (SPCAnet) (Wang+, 2020)
J/A+A/640/A127 : Open cluster properties with Gaia and LAMOST (Zhong+, 2020)
J/MNRAS/506/150 : The GALAH+ Survey DR3 (Buder+, 2021)
http://www.lamost.org/dr7/ : LAMOST DR7 home page
http://github.com/Chan-0312/RRNet : RRNet code and trained models
Byte-by-byte Description of file: rrnetcat.dat
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Bytes Format Units Label Explanations
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1- 9 I9 --- obsID [588902003/747816250] Identification of
LAMOST spectrum
11- 55 A45 --- FileName Name of the FITS file LAMOST spectrum
57- 76 F20.16 deg RAdeg Right ascension (J2000)
78- 96 F19.16 deg DEdeg [-9.15/79.1] Declination (J2000)
98- 107 A10 --- Nameb Spectrum extension name of blue part
109- 118 A10 --- Namer Spectrum extension name of red part
120- 125 F6.2 --- SNRb [10/999] Signal-to-noise ratio of blue part
127- 132 F6.2 --- SNRr [10/999] Signal-to-noise ratio of red part
134- 151 F18.13 K Teff [3779/6597] Effective temperature
153- 171 F19.15 K e_Teff [33/484] Teff uncertainty
173- 190 F18.16 [cm/s2] logg [0.95/4.8] Log of surface gravity
192- 209 F18.16 [cm/s2] e_logg [0.08/0.8] logg uncertainty
211- 233 E23.17 [-] CH [-2.38/0.34] [C/H] abundance
235- 252 F18.16 [-] e_CH [0.029/0.5] CH uncertainty
254- 276 E23.17 [-] [N/H] [-1.94/0.9] [N/H] abundance
278- 295 F18.16 [-] e_[N/H] [0.038/0.52] [N/H] uncertainty
297- 319 E23.17 [-] OH [-1.44/0.44] [O/H] abundance
321- 338 F18.16 [-] e_OH [0.028/0.5] OH uncertainty
340- 362 E23.17 [-] MgH [-1.9/0.6] [Mg/H] abundance
364- 381 F18.16 [-] e_MgH [0.02/0.4] MgH uncertainty
383- 405 E23.17 [-] AlH [-2.7/0.8] [Al/H] abundance
407- 424 F18.16 [-] e_AlH [0.027/0.6] AlH uncertainty
426- 448 E23.17 [-] SiH [-1.81/0.61] [Si/H] abundance
450- 467 F18.16 [-] e_SiH [0.019/0.31] SiH uncertainty
469- 491 E23.17 [-] SH [-1.91/0.5] [S/H] abundance
493- 510 F18.16 [-] e_SH [0.039/0.42] SH uncertainty
512- 534 E23.17 [-] KH [-2.1/0.5] [K/H] abundance
536- 553 F18.16 [-] e_KH [0.04/0.61] KH uncertainty
555- 577 E23.17 [-] CaH [-1.97/0.52] [Ca/H] abundance
579- 596 F18.16 [-] e_CaH [0.02/0.4] CaH uncertainty
598- 620 E23.17 [-] TiH [-1.75/0.6] [Ti/H] abundance
622- 639 F18.16 [-] e_TiH [0.026/0.6] TiH uncertainty
641- 663 E23.17 [-] CrH [-2.25/0.64] [Cr/H] abundance
665- 682 F18.16 [-] e_CrH [0.028/0.5] CrH uncertainty
684- 706 E23.17 [-] MnH [-2.1/0.5] [Mn/H] abundance
708- 725 F18.16 [-] e_MnH [0.029/0.6] MnH uncertainty
727- 749 E23.17 [-] [Fe/H] [-1.88/0.61] [Fe/H] abundance
751- 768 F18.16 [-] e_[Fe/H] [0.025/0.4] [Fe/H] uncertainty
770- 792 E23.17 [-] NiH [-2.55/0.72] [Ni/H] abundance
794- 811 F18.16 [-] e_NiH [0.02/0.5] NiH uncertainty
813- 835 E23.17 [-] CuH [-2.6/0.6] [Cu/H] abundance
837- 854 F18.16 [-] e_CuH [0.05/0.7] CuH uncertainty
856 I1 --- Flag [0/1] Recommended flag: 1=good
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History:
Downloaded from http://nadc.china-vo.org/res/r101112/
(End) Emmanuelle Perret [CDS] 30-Sep-2022