J/MNRAS/485/4906 GC number density profiles using Gaia DR2 (de Boer+, 2019)
Globular cluster number density profiles using Gaia DR2.
de Boer T.J.L., Gieles M., Balbinot E., Henault-Brunet V., Sollima A.,
Watkins L.L., Claydon I.
<Mon. Not. R. Astron. Soc., 485, 4906-4935 (2019)>
=2019MNRAS.485.4906D 2019MNRAS.485.4906D (SIMBAD/NED BibCode)
ADC_Keywords: Models ; Clusters, globular ; Optical
Keywords: methods: numerical - stars: kinematics and dynamics -
globular clusters: general - galaxies: star clusters
Abstract:
Using data from Gaia DR2, we study the radial number density profiles
of the Galactic globular cluster sample. Proper motions are used for
accurate membership selection, especially crucial in the cluster
outskirts. Due to the severe crowding in the centres, the Gaia data
are supplemented by literature data from HST and surface brightness
measurements, where available. This results in 81 clusters with a
complete density profile covering the full tidal radius (and beyond)
for each cluster. We model the density profiles using a set of
single-mass models ranging from King and Wilson models to generalized
lowered isothermal LIMEPY models and the recently introduced SPES
models, which allow for the inclusion of potential escapers. We find
that both King and Wilson models are too simple to fully reproduce the
density profiles, with King (Wilson) models on average underestimating
(overestimating) the radial extent of the clusters. The truncation
radii derived from the LIMEPY models are similar to estimates for the
Jacobi radii based on the cluster masses and their orbits. We show
clear correlations between structural and environmental parameters, as
a function of Galactocentric radius and integrated luminosity.
Notably, the recovered fraction of potential escapers correlates with
cluster pericentre radius, luminosity, and cluster concentration. The
ratio of half mass over Jacobi radius also correlates with both
truncation parameter and PE fraction, showing the effect of Roche lobe
filling.
Description:
To study the density profiles of Globular Clusters (GCs) we will use
data from the Gaia mission (Gaia Collaboration et al.
2018A&A...616A...1G 2018A&A...616A...1G, Cat. I/345), which contains exquisite data for
about 1.6 billion sources covering the full sky.
We use the extensive catalogue of GCs from Harris
(1996AJ....112.1487H 1996AJ....112.1487H, Cat. VII/202) for our input list of targets. To
avoid regions of excessive crowding where Gaia measurements become
less reliable, we limit our sample to |b|>5deg, leaving 113 GCs. Each
of these targets is queried in the Gaia data archive
(https://gea.esac.esa.int/archive/) using a cone search out to a
radius of 2.5 times the Jacobi radius (rJ) determined by Balbinot &
Gieles (2018MNRAS.474.2479B 2018MNRAS.474.2479B, Cat. J/MNRAS/474/2479).
A crucial step in the study of GC density profiles is a reliable
membership selection. In this work, we first employ a fixed parallax
cut to remove nearby stars, followed by a selection in
colour-magnitude space and proper motion space. Following these
selections, we use the Gaia proper motions to compute the membership
probability of each star. The proper motion cloud is fit using a
Gaussian mixture model consisting of one Gaussian for the cluster
distribution and another for the MW foreground distribution.
We have fit the combined density profiles using a variety of
single-mass models, including often-used King (1966AJ.....71...64K 1966AJ.....71...64K)
and Wilson (1975AJ.....80..175W 1975AJ.....80..175W) models, as well as the recently
introduced LIMEPY models (https://github.com/mgieles/limepy). Finally,
we also utilize the recently developed SPES models, which include a
prescription for the presence of PE stars, essential for reproducing
the outskirts of GCs.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
tableb1.dat 186 81 Best-fitting parameters of LIMEPY and SPES
models fit to 81 GCs
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See also:
I/345 : Gaia DR2 (Gaia Collaboration, 2018)
Byte-by-byte Description of file: tableb1.dat
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Bytes Format Units Label Explanations
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1- 7 A7 --- Name Globular cluster name
9- 12 F4.2 --- WKing Dimensionless central potential from the King
model fit
14- 17 F4.2 --- e_WKing Error on WKing
19- 24 F6.2 pc rtKing Tidal radius from the King model fit
26- 30 F5.2 pc e_rtKing Error on rtKing
32- 36 F5.2 --- WWil Dimensionless central potential from the
Wilson model fit
38- 41 F4.2 --- e_WWil Error on WWil
43- 49 F7.2 pc rtWil Tidal radius from the Wilson model fit
51- 56 F6.2 pc e_rtWil Error on rtWil
58- 61 F4.2 --- WLime Dimensionless central potential from the
Limepy model fit
63- 66 F4.2 --- e_WLime Error on WLime
68- 71 F4.2 --- gLime Truncation parameter from the Limepy model
fit
73- 76 F4.2 --- e_gLime Error on gLime
78- 82 F5.2 pc rhLime Half-mass radius from the Limepy model fit
84- 87 F4.2 pc e_rhLime Error on rhLime
89- 94 F6.2 pc rtLime Tidal radius from the Limepy model fit
96-101 F6.2 pc e_rtLime Error on rtLime
103-107 F5.2 --- WSPES Dimensionless central potential from the SPES
model fit
109-112 F4.2 --- e_WSPES Error on WSPES
114-117 F4.2 --- etaSPES η parameter from the spherical potential
escapers (SPES) stitched models fit (1)
119-122 F4.2 --- e_etaSPES Error on etaSPES
124-128 F5.2 --- log1B Logarithm of 1-B, with parameter B coming
from the SPES model fit (2)
130-133 F4.2 --- e_log1B Error on log1B
135-139 F5.2 pc rhSPES Half-mass radius from the SPES model fit
141-144 F4.2 pc e_rhSPES Error on rhSPES
146-151 F6.2 pc rtSPES Tidal radius from the SPES model fit
153-158 F6.2 pc e_rtSPES Error on rtSPES
160-164 F5.2 --- logfPE Logarithm of the fraction of potential
escapers (PEs) recovered in the SPES best
fit
166-169 F4.2 --- e_logfPE Error on logfPE
171-175 F5.2 arcmin rtie Innermost usable Gaia radius
177-181 F5.2 arcmin-2 BGlev Background level estimated from the outer
regions of the Gaia data
183-186 F4.2 Msun Mlow Low-mass value
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Note (1): The parameter η is the ratio of the velocity dispersion of the
potential escapers (PEs) over the velocity scale s, and it can have
values 0=<η=<1. For η=0 there are no PEs, and (for fixed B)
the fraction of PEs correlates with η. For a fixed η, the
fraction of PEs anticorrelates with B for B close to 1. For smaller B,
the fraction of PEs is approximately constant or correlates slightly
with B (for constant η).
Note (2): The value of B can be 0=<B=<1, where for B=1 there are no PEs (i.e.
the distribution function (DF) is the same as the King model) and for
0=<B=<1, the model contains PEs.
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History:
From electronic version of the journal
(End) Ana Fiallos [CDS] 07-Oct-2022