J/MNRAS/489/176 Dynamical heating across the Milky Way disc (Mackereth+, 2019)
Dynamical heating across the Milky Way disc using APOGEE and Gaia.
Mackereth J.T., Bovy J., Leung H.W., Schiavon R.P., Trick W.H.,
Chaplin W.J., Cunha K., Feuillet D.K., Majewski S.R., Martig M., Miglio A.,
Nidever D., Pinsonneault M.H., Silva Aguirre V., Sobeck J., Tayar J.,
Zasowski G.
<Mon. Not. R. Astron. Soc., 489, 176-195 (2019)>
=2019MNRAS.489..176M 2019MNRAS.489..176M (SIMBAD/NED BibCode)
ADC_Keywords: Stars, giant ; Stars, ages ; Milky Way ; Models ; Optical
Keywords: Galaxy: disc - Galaxy: evolution - Galaxy: formation
Galaxy: kinematics and dynamics - Galaxy: stellar content
Abstract:
The kinematics of the Milky Way disc as a function of age are well
measured at the solar radius, but have not been studied over a wider
range of Galactocentric radii. Here, we measure the kinematics of
mono-age, mono-[Fe/H] populations in the low and high [α/Fe]
discs between 4~<R~<13kpc and |z|~<2kpc using 65719 stars in common
between APOGEE DR14 and Gaia DR2 for which we estimate ages using a
Bayesian neural network model trained on asteroseismic ages. We
determine the vertical and radial velocity dispersions, finding that
the low and high [α/Fe] discs display markedly different
age-velocity dispersion relations (AVRs) and shapes
σz/σR. The high [α/Fe] disc has roughly flat
AVRs and constant σz/σR=0.64±0.04, whereas the low
[α/Fe] disc has large variations in this ratio that positively
correlate with the mean orbital radius of the population at fixed age.
The high [α/Fe] disc component's flat AVRs and constant
σz/σR clearly indicate an entirely different heating
history. Outer disc populations also have flatter radial AVRs than
those in the inner disc, likely due to the waning effect of spiral
arms. Our detailed measurements of AVRs and σz/σR
across the disc indicate that low [α/Fe], inner disc (R~<10kpc)
stellar populations are likely dynamically heated by both giant
molecular clouds and spiral arms, while the observed trends for outer
disc populations require a significant contribution from another
heating mechanism such as satellite perturbations. We also find that
outer disc populations have slightly positive mean vertical and radial
velocities likely because they are part of the warped disc.
Description:
We use a catalogue of stellar positions, velocities, element
abundances, and estimated ages, comprising a cross-match of the
catalogues from the 14th data release (DR14; Abolfathi et al.
2018ApJS..235...42A 2018ApJS..235...42A) of the SDSS-IV APOGEE-2 survey, and the second
data release (DR2; Gaia Collaboration 2018A&A...616A...1G 2018A&A...616A...1G, Cat. I/345)
of the ESA-Gaia mission. Ages are estimated from a neural network
based model trained on data from the APOKASC catalogue, which contains
stars observed both spectroscopically by APOGEE and asteroseismically
by the Kepler mission.
Our method uses a BCNN, implemented in the astroNNpython package
(Leung & Bovy 2019MNRAS.483.3255L 2019MNRAS.483.3255L), which wraps the Keras and
TensorFlow machine learning architectures. BCNNs treat the more
commonly used convolutional neural network (CNN) as a Bayesian
regression problem, inferring the probability distributions over the
model weights.
The training data is compiled and loaded using functions in astroNN.
Individual APOGEE visit spectra are recombined and continuum
normalized using a method similar to that used in the Cannon (Ness et
al. 2015ApJ...808...16N 2015ApJ...808...16N, Cat. J/ApJ/808/16), which makes a Chebyshev
polynomial fit to specifically selected pixels separately between the
different CCD chips in APOGEE. This procedure results in an improved
normalization, which is preferable when training the BCNN model, over
the normalization used in the standard APOGEE data reduction.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
dr14ages.dat 100 74748 Estimating ages for red giant stars using
bayesian convolutional neural networks
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See also:
I/345 : Gaia DR2 (Gaia Collaboration, 2018)
Byte-by-byte Description of file: dr14ages.dat
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Bytes Format Units Label Explanations
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1- 18 A18 --- Name Star name (2MHHMMSSss+DDMMSSs)
20- 41 F22.19 Gyr aNNage Star age from astroNN model
43- 60 F18.16 Gyr e_aNNage Error on aNNage (1)
62- 80 F19.17 Gyr predstd astroNN predictive error on age
82-100 F19.17 Gyr modelstd astroNN model error on age
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Note (1): Total error e_NNage=sqrt(predstd2+modelstd2)
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History:
From electronic version of the journal
(End) Ana Fiallos [CDS] 02-Jan-2023