J/MNRAS/512/2890 APOGEE-DR16/APOKASC high/low-α disc stars (Lu+, 2022)
Similarities behind the high- and low-α disc small intrinsic abundance
scatter and migrating stars.
Lu Y., Ness M.K., Buck T., Zinn J.C., Johnston K.V.
<Mon. Not. R. Astron. Soc. 512, 2890-2910 (2022)>
=2022MNRAS.512.2890L 2022MNRAS.512.2890L (SIMBAD/NED BibCode)
ADC_Keywords: Milky Way ; Stars, K-type ; Stars, giant ; Stars, late-type ;
Spectroscopy ; Asteroseismology ; Models ; Infrared ;
Effective temperatures ; Stars, ages ; Abundances ;
Positional data
Keywords: stars: abundances
Abstract:
The detailed age-chemical abundance relations of stars measure
time-dependent chemical evolution. These trends offer strong empirical
constraints on nucleosynthetic processes, as well as the homogeneity
of star-forming gas. Characterizing chemical abundances of stars
across the Milky Way over time has been made possible very recently,
thanks to surveys like Gaia, APOGEE, and Kepler. Studies of the
low-α disc have shown that individual elements have unique
age-abundance trends and the intrinsic dispersion around these
relations is small. In this study, we examine and compare the age
distribution of stars across both the high and low-α disc and
quantify the intrinsic dispersion of 16 elements around their
age-abundance relations at [Fe/H] = 0 using APOGEE DR16. We examine
the age-metallicity relation and visualize the temporal and spatial
distribution of disc stars in small chemical cells. We find: (1) the
high-α disc has shallower age-abundance relations compared to
the low-α disc, but similar median intrinsic dispersions of
∼0.03 dex; (2) turnover points in the age-[Fe/H] relations across
radius for both the high- and low-α disc. The former constrains
the mechanisms that set similar intrinsic dispersions, regardless of
differences in the enrichment history, for stars in both disc, and the
latter indicates the presence of radial migration in both disc. Our
study is accompanied by an age catalogue for 64317 stars in APOGEE
derived using THE CANNON with a median uncertainty of 1.5 Gyr (26 per
cent; APO-CAN stars), and a red clump catalogue of 22031 stars with a
contamination rate of 2.7 per cent.
Description:
In this study, we examine separately the high- and low-α disc,
gives us insight as to which properties are shared and which are
distinct across this chemical plane. This gets towards understanding
the relationship between the two sequences, and if the high-α
disc could be an ancestor of the low-α disc, or if its formation
channel must be entirely distinct. In detail, we compare the
age-abundance relations and the intrinsic dispersions around these
relations. We highlight which elements are most similar between the
two discs and which are different. We examine the mean age
distributions, both spatially and chemically, and showcase the
signatures of radial migration.
To do so we use a catalogue of from the spectroscopic APOGEE DR16
containing spectra and abundances of red clump stars. We combine it to
APOKASC catalogue that contains ages and asteroseismic parameters. In
order to create the age and the red clump catalogue, we used the
CANNON algorithm. The CANNON is a data driven approach to derive
stellar parameters from stellar spectra. Here, we use a quadratic
combination of the labels to predict each pixel of the spectrum, which
is consistent with previous implementations. It is a tool that
determines the relationship between the spectral flux and labels.
Therefore, it is important to include labels that describe most of the
spectral flux, i.e. Teff, logg, [Fe/H], [Mg/Fe], Δν, ΔP
and 16 individual element abundances.
The CANNON can not extrapolates beyond the training sample regime.
Therefore, we first discarded stars outside of the training parameter
range. We only included stars with Teff between 4400-5200 K, logg
between 2.2-3.5 dex, and [Fe/H] between -0.8 and 0.5 dex. We
also excluded stars with abnormal element abundances (absolute
abundance values > 1) for the 16 elements we are interested in. This
left us with 64317 stars. After training sessions, we thus obtained
the CANNON inferred results as presented in the table.dat.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table.dat 236 64317 APO-CAN stars with inferred parameters
from the CANNON algorithm
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See also:
J/ApJ/858/L7 : Red clump stars selected from LAMOST and APOGEE (Ting+, 2018)
III/284 : APOGEE-2 data from DR16 (Johnsson+, 2020)
J/A+A/588/A87 : Seismic global parameters of 6111 KIC (Vrard+, 2016)
V/154 : Sloan Digital Sky Surveys (SDSS), Release 16 (Ahumada+, 2020)
J/ApJ/823/114 : The Cannon: a new approach to determine masses (Ness+, 2016)
J/ApJ/836/5 : Abundances of LAMOST giants from APOGEE DR12 (Ho+, 2017)
J/AJ/151/144 : ASPCAP weights for the 15 APOGEE chemical elements
(Garcia+, 2016)
J/ApJS/239/32 : APOKASC-2 catalog of Kepler evolved stars
(Pinsonneault+, 2018)
J/AJ/158/147 : Spectrophotometric parallaxes with linear models (Hogg+,2019)
J/MNRAS/489/176 : Dynamical heating across the Milky Way disc (Mackereth+,2019)
J/A+A/639/A127 : Age-chemical-clocks-metallicity relations (Casali+, 2020)
Byte-by-byte Description of file: table.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 18 A18 --- APOGEE 2MASS-style object name designation
(2MHHMMSSss±DDMMSSs) (APOGEE_ID)
20- 24 A5 --- f_APOGEE [True False] Indicates the star is a red
clump star using ΔP > 230 s, 22031 True
and 42286 False cases (RCflag)
26- 38 E13.10 [Sun] [Fe/H] Iron to hydrogen abundance ratio inferred
using the CANNON algorithm ([Fe/H])
40- 54 F15.10 K Teff Effective temperature inferred using the
CANNON algorithm (Teff)
56- 67 F12.10 [cm/s2] logg Surfave gravity inferred using the CANNON
algorithm (Logg)
69- 81 E13.10 [Sun] [Mg/Fe] Magnesium to iron abundance ratio inferred
using the CANNON algorithm ([Mg/Fe])
83- 96 F14.10 s DP Period spacing of the mixed g and p modes
ΔP inferred using the CANNON algorithm
(DP)
98- 110 F13.10 s Dnu Frequency spacing between p-modes
Δν inferred using the CANNON
algorithm (Dnu)
112- 123 F12.10 [Gyr] logAge Star age inferred using the CANNON algorithm
(LogAge)
125- 136 F12.10 [Sun] e_[Fe/H] Mean uncertainty of [Fe/H] ([Fe/H]err)
138- 149 F12.10 K e_Teff Mean uncertainty of Teff (Tefferr)
151- 162 F12.10 [cm/s2] e_logg Mean uncertainty of logg (Loggerr)
164- 175 F12.10 [Sun] e_[Mg/Fe] Mean uncertainty of [Mg/Fe] ([Mg/Fe]err)
177- 188 F12.10 s e_DP Mean uncertainty of DP (DP_err)
190- 201 F12.10 s e_Dnu Mean uncertainty of Dnu (Dnu_err)
203- 214 F12.10 [Gyr] e_logAge Mean uncertainty of logAge (LogAge_err)
216- 225 F10.6 deg RAdeg Right ascension from Gaia DR2 (J2000) (RA)
227- 236 F10.6 deg DEdeg Declination from Gaia DR2 (J2000) (DEC)
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History:
From electronic version of the journal
(End) Luc Trabelsi [CDS] 12-Mar-2025