Chemical and Kinematic Properties of Bright Metal
Poor Stars
ARCHNEM
MASSACHUSETTS INSTITUTE
OFTECHNOLOLGy
by
Weishuang Linda Xu
AUG 10 2015
Submitted to the Department of Physics
in partial fulfillment of the requirements for the degree of
LIBRARIES
Bachelor of Science in Physics
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
June 2015
Massachusetts Institute of Technology 2015. All rights reserved.
A uthor ..................................
C ertified by ..........................
Signature redacted
.-...-.
. . . . . ...
Department of Physics
May 9, 2015
Signature
redacted
...............
....
/
-
Anna Frebel
Assistant Professor of Physics
Thesis Supervisor
A ccepted by ...........................
Signature redacted
-
-
Nergis Mavalvala
Physics Senior Thesis Coordinator
2
Chemical and Kinematic Properties of Bright Metal Poor
Stars
by
Weishuang Linda Xu
Submitted to the Department of Physics
on May 9, 2015 , in partial fulfillment of the
requirements for the degree of
Bachelor of Science in Physics
Abstract
In this work, I analyze the high-resolution spectra of 20 stars, chosen for their low
metallicity rFe/HI < -2.5 and proximity to the sun. Using these spectra I model the
atmospheres of these stars by determing stellar parameters {Teff, log(g), p, [Fe/H]}
and obtain also their chemical abundances for 17 elements including Fe, C, Sr, and
Ba. Three of these stars are found to possess an overabundance of Carbon relative
to Iron. Combining these chemical abundances with those from previously analyzed
spectra from the same bright metal-poor star sample, I perform orbit determination
and integration on a total of 59 metal-poor stars and extract their kinematic parameters. I also explore how these results depend on the assumed mass of the Milky Way.
These chemical and kinematic results are then combined and compared with comparatively metal-rich (-2.5 < [Fe/H] < 0) samples; a conal distribution of velocity
components with respect to metallicity is observed, as well as two distinct populations
in eccentricity. The 59 bright metal-poor stars were identified as residing in the inner
halo of the Milky Way.
Thesis Supervisor: Anna Frebel
Title: Assistant Professor of Physics
3
4
Acknowledgments
I am infinitely grateful to my wonderful supervisors and mentors Anna Frebel and
Heather Jacobson. They provided more support, advice, direction, help, and timely
prodding than I can quantify and made this entire project a very fun process in the
end. I most definitely would not have a thesis without either of them, and at this
point I owe them a whole of physics and astronomy and life advice. Not to mention
that they bore not a little amount of undergraduate flakiness and confusion with
incredible patience.
I am thankful as well to the 6th floor of Kavli and all its inhabitants in general,
particularly those who stopped by my annexed hallway desk in the middle of their
busy grad student lives to chat. They made my work a lot more pleasant and I always
felt welcome there. Thank you especially to Alex Ji who tolerated my biweekly usage
of him as post-choir keycard access.
Thank you to my lovely family and friends whose smiles, company, and conversation carried me through college and the brilliant and brutal process that was MIT. I
look to seeing most all of you back in sunny California.
I extend my thanks finally to Fr, Qg, Sl, Al, Rz and all the others for all their love,
support, patience, solidarity, and warmth that has been unequivocally critical to my
happiness for the past years, in ways academic or otherwise. I genuinely wouldn't be
myself without them. And of course thank you 0 for apples and bump functions and
everything in between; there is very little I can justify with words here.
5
6
This doctoral thesis has been examined by a Committee of the
Department of Physics as follows:
Professor Nergis Mavalvala..................................
Senior Thesis Coordinator
Professor of Physics
Professor Anna Frebel................
Signature redacted
Thesis Supervisor
Assistant Professor of Physics
8
Contents
1
Introduction
17
2
Analysis of Spectral Data
19
Initial Processing: Normalization and Doppler Correction
. . . .
19
2.2
Measuring Equivalent Widths of Spectral Features . . .
. . . .
21
2.3
Modeling Stellar Parameters from Equivalent Widths .
. . . .
25
2.3.1
From Equivalent Widths to Abundances
. . . .
. . . .
26
2.3.2
Stellar Parameter Fitting . . . . . . . . . . . . .
. . . .
27
2.3.3
Stellar Parameters of 20 Metal-Poor Stars . . .
. . . .
29
Abundances of Non-Iron Elements . . . . . . . . . . . .
. . . .
29
2.4.1
Abundances of General non-Iron Metals
. . . .
. . . .
31
2.4.2
Synthetic fitting of Sr, Ba, and C Lines . . . . .
. . . .
32
2.4.3
Chemical Abundances of 20 Metal Poor Stars
. . . .
38
.
.
.
.
.
.
Analysis of Kinematic Data
3.3
3.1.1
Astrometric Data: RA, Dec, and Proper Motions
42
3.1.2
Heliocentric Velocities
. . . . . . . . . . . . . .
42
3.1.3
Heliocentric Distances
. . . . . . . . . . . . . .
42
Orbits of Metal-Poor Stars . . . . . . . . . . . . . . . .
44
3.2.1
Uncertainty of Orbits . . . . . . . . . . . . . . .
45
. .
45
.
.
.
41
.
3.2
. . . . . . .
Parameters for Galactic Orbit Integration
.
3.1
41
Orbital Potentials with Various Milky Way Masses
9
.
3
.
2.4
.
2.1
4
5
51
Interpretation of Results
4.1
The Bidelman-McConnell Sample . . . . . . . . . . . . . . . . . . . .
51
4.2
Correlations between Metallicity and Orbital Kinematics . . . . . . .
52
4.3
Identification of Halo Stars . . . . . . . . . . . . . . . . . . . . . . . .
53
4.4
Carbon Abundances
. . . . . . . . . . . . . . . . . . . . . . . . . . .
55
-
Conclusions and Future Work
59
61
A Tables
10
List of Figures
2-1
Full spectrum of HE 2208-1239 after normalization and Doppler correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2-2
Signal-to-Noise ratio as a function of wavelength for the spectrum of
HE 2201-4043 ........
2-3
..............................
20
Normalization of the 3600-1670 aperture of the spectra of HE 2208-1239
with a 4th-order spline function and 30A knot spacing. . . . . . . . .
2-4
20
21
The spectra of HE 2208-1239 (in black), before [left] and after [right]
corrections for Doppler shifting to match the template spectra of HD140283
(show n in blue) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2-5
2-6
Gaussian fits to various spectral features for HE 2235 -5058.
The
equivalent width of each fit is calculated automatically by SMH.
. .
.
23
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
The approximate curve of growth for a Sun-like star. Image taken from
[5 1
2-7
22
The Ha and Ho features of HE 2201-4043 compared with those of well
known metal-poor stars. The reference stars used here are HD122563
(blue), HE 1523-0901 (red), CS22892-52 (green), HD140283 (magenta), G64-12 (cyan). One can infer that Teff of this star is roughly
~ 4700K .
2-8
2-9
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Before temperature correction parameters for HE 2201-4043.
The
blue regression lines are not meaningful and can be ignored.
. . . . .
After temperature correction parameters for HE 2201 -4043.
The blue
regression lines are not meaningful and can be ignored.
11
28
. . . . . . . .
31
32
2-10 20 metal-poor stars plotted on an isochrone (left).
The log(g) - A
plot (right) confirms that the input microturbulence factor is within
expectation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
2-11 59 stars from the bright metal-poor sample plotted on an isochrone. .
33
2-12 Determination of Ti II abundance for HE 1317-0407. The red points
indicate lines that were discarded due to excess noise or blending.
. .
34
2-13 Determination of Mg I abundance for HE 1317-0407. There are significantly fewer lines than Ti or Fe. . . . . . . . . . . . . . . . . . . .
34
2-14 Fitting to Sr lines of HE 1311-0131 with synthetic spectra; the upper
panels show the residuals of the fits in the lower panels. . . . . . . . .
36
2-15 Fitting to Ba lines of HE 1311-0131 with synthetic spectra; the upper
panels show the residuals of the fits in the lower panels. . . . . . . . .
37
2-16 Fitting to CH forests of HE 1311-0131 with synthetic spectra; the
upper panels show the residuals of the fits in the lower panels. ....
2-17 Chemical Abundances of 20 stars against previous literature
3-1
39
. . . . .
40
Reading of Mv values from an isochrone plot with [Fe/H]z=-3.0. The
Teff and log(g) parameters determine the star's position on the isochrone. 43
3-2
Plot of energy loss over time for HE 0201-3142. The energy error grows
with time but is still very small (AE/E < 10-7).
3-3
. . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . .
47
Changes in eccentricity, orbital apsis, and maximum orbital height
(both in kpc) with varying assumptions on Milky Way mass.
The
abscissa shows the stars indexed 1-59. . . . . . . . . . . . . . . . . . .
4-1
46
Integrated orbit of HE 1216-1554 under error perturbations of pmRA,
pm Dec, and distance.
3-5
44
Integrated galactic orbits for a few stars in X--Y (left), R-Z (middle),
and R - VR (right) spaces.
3-4
. . . . . . . . . . .
49
Plots of Iron abundance [Fe/H] against eccentricity and U, V, W velocities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
52
4-2
Plots of Iron abundance [Fe/H] and U, V, W velocities for both this
project and the B+Mc sample . . . . . . . . . . . . . . . . . . . . . .
4-3
Plots of Iron abundance IFe/HI against eccentricity for both this project
and the B+Mc sample. . . . . . . . . . . . . . . . . . . . . . . . . . .
4-4
54
55
Plot of orbital energies (tangential against radial velocity) for both
these and the B+Mc stars. The dotted arcs are equipotential and the
metal-rich disk is confined inside the 100 km/s arc.
4-5
Plot of Carbon abundance as
[C/Fe
. . . . . . . . . .
56
against [Fe/HJ, eccentricity, sur-
face gravity, maximum height, and orbital apsis. A star is Carbonenhanced at [C/Fe]>1. . . . . . . . . . . . . . . . . . . . . . . . . . .
4-6
Plots of Carbon abundance as
[C/Fe]
containing both the stars in this
work and the Bidelman-McConnell stars. . . . . . . . . . . . . . . . .
13
56
57
14
List of Tables
2.1
Stellar Parameters before and after temperature correction . . . . . .
30
2.2
Chemical Abundances for 20 metal-poor stars . . . . . . . . . . . . .
40
A .1
A strom etry
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
62
A.2 Astrometry (cont) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
A.3 Heliocentric Distances
. . . . . . . . . . . . . . . . . . . . . . . . . .
64
A.4 Heliocentric Distances (cont) . . . . . . . . . . . . . . . . . . . . . . .
65
A.5 Stellar Parameters
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
A.6 Stellar Parameters (cont) . . . . . . . . . . . . . . . . . . . . . . . . .
67
15
16
Chapter 1
Introduction
Stellar Archaeology is concerned with gleaning cosmological insight through observation and analysis of old stars. Along with high-redshift objects and the cosmic
microwave background, the comparatively nearby old stars provide an additional
probe towards understanding the early Universe. Since the synthesis of successively
heavier elements occurred as the universe evolved, a deficiency of metallic elements
in stars can be indicative of an especially early formation date.
The discovery of extremely metal-poor stars can then potentially provide temporal
and thermodynamic constraints on the formation of the Universe beyond the range
of the highest redshift observable objects. Locally, information on the spatial and
kinematic distribution of metal-poor stars within the Milky Way can also lend insight
on the formation process of our galaxy.
In addition, the pursuit of metal-poor stars often leads to the discovery of individually astrophysically interesting objects. A star with strong r-process enhancement,
that is exhibiting overabundance of neutron-heavy elements, for instance could be the
site of rare and exotic nucleosynthesis processes.
Most of all, metal-poor stars have the potential of containing within their atmospheres relics of the chemically primitive environment in which they were formed.
The chemical signatures of these metal-poor stars then provide information on the
process and timeline of stellar nucleosynthesis- the process responsible for essentially
the entire periodic table which remains yet poorly understood. Iron abundances can
17
be particularly interesting since
56
Fe is the heaviest element produced at the end of
the fusion processes in stellar nucleosynthesis (elements heavier than Fe are generally
produced via r- and s- process neutron capture) and is the most energy-stable heavy
element currently known
[5]. Carbon abundances, a key part of the CNO cycle, is
likewise important in that in provides information on the Carbon-richness of the gas
from which the star was formed.
This project investigates a total of 59 Milky Way stars chosen from a sample of
1777 candidates in the Hamburg/ESO Survey. This sample is particularly selected
for its brightness - with 9 < mB < 14 - and low metallicity -with [Fe/H < -2.0;
the specific selection process is detailed in
[7].
These 59 stars were observed at the
Magellan-Clay Telescope and high resolution spectra were obtained, allowing a much
more accurate model of their stellar atmospheres. 39 of these spectra were previously
processed and analyzed and this project directly undertakes the remaining 20 which
are then combined with the quoted parameters for analysis.
The purpose of this project is to analyze the chemical and kinematic properties of
these bright metal-poor stars in the Milky Way, and to relate their stellar metallicities
to orbital characteristics in order to understand the current distribution of metals in
the Milky Way and the kinematic state of the galaxy at earlier times. The structure
of this work is split into two main sections: first, the chemical abundances, metallicities, and atmospheric models of these stars are extracted from its spectra; secondly,
the orbits of these metal-poor stars are integrated and characterized with catalogued
astrometrical and other kinematic parameters. This project seeks to classify these
stars as disk or halo populations and will attempt to recover correlations between
stellar metalicities and orbital energies, eccentricities, and chemical abundances of
other elements, particularly Carbon. Previous work has suggested a trend of increas-
ing Carbon enhancement with lower metallicity and orbital height above the disk [131,
and this project will seek to reproduce these results.
18
Chapter 2
Analysis of Spectral Data
This chapter details the methods I used to extract information on the evolutionary
and chemical state of a star from its raw spectra, from normalization and line-fitting to
determining element abundances and stellar parameters. In this project, the spectra
and stellar parameters of a total of 59 bright metal-poor stars were used; of these, I
personally processed and analyzed 20 and quote the remaining values from previous
work and published literature.
The spectra collected for these stars had a wavelength range of 3000 - 9000 A (Fig
2-1) and were observed with the MIKE spectrograph at the Magellan-Clay Telescope.
Since this metal-poor star sample was selected particularly for its brightness, the
spectra obtained are of high resolution with R
=
A/AA > 40, 000 and a very high
signal-to-noise ratio, generally above 100-150 (Fig 2-2). The spectral analysis for
this project is conducted using SMH, a custom software framework named rather
ominously Spectroscopy Made Harder 161.
2.1
Initial Processing: Normalization and Doppler
Correction
Some processing of the metal-poor star spectra was necessary before analysis to correct for bias effects, specifically variations in continuum flux and Doppler shifting
19
1.0
JWLLI IL
-
I .I
nrr}
0.8
0.6
0.01
5600
4800
4000
6400
oo
7200
8800
Figure 2-1: Full spectrum of HE 2208-1239 after normalization and Doppler correction
250
-
200
150
I
100
J-1'I'
50
I
~II
4000
5000
6000
Wavelength, A (A)
7000
rP
8000
9000
Figure 2-2: Signal-to-Noise ratio as a function of wavelength for the spectrum of
HE 2201-4043
of lines. The continuum emission flux level varies with respect to wavelength as a
function of the radiation spectrum of the star, extinction effects such as interstellar
reddening, and also the regional CCD sensitivity of the spectrograph. The spectrum
is normalized by fitting each of 70 divided segments or "orders" with a spline function
of order 3-5 and an approximate knot spacing of 20 A (Fig 2-3). These orders then
have the spline fit divided from them and are stitched together to form the complete
normalized spectrum. A normalized spectrum is necessary for the following analysis.
Doppler shifting of lines occurs since the star generally will have non-zero line-ofsight velocity relative to the Earth in its orbit around the Sun. Since this sample of
bright metal-poor stars are expected to predominantly be giants, their spectra were
20
File: 1 / 2, Aperture: 10 / 70 (70 apertures have continuum fits)
2000
spline (order 4)
30.0 A knot spacing
Sigma clipping: (5.0, 1.0)
8 iterations
Scale: 1.015
1500
1000
500
0
0.8
3600
3610
3620
3640
3630
Wavelength,
3650
3660
3670
A (A)
Figure 2-3: Normalization of the 3600-1670 aperture of the spectra of HE 2208-1239
with a 4th-order spline function and 30
A knot spacing.
compared with the known at-rest "template" spectrum of HD140283 (Fig 2-4) and
through cross-correlation the wavelength offset and thus the geocentric radial velocity
of the star is determined.
This parameter, generally determined to within 0.1km
with this technique, will be later used in the kinematic analysis and its heliocentric
corrections will be discussed in the next chapter. This velocity offset is then applied
to the spectrum to place it at rest. The accuracy of these Doppler corrections are
more than sufficient for the equivalent width analysis following.
2.2
Measuring Equivalent Widths of Spectral Features
The chemical abundances of a given element in a star can be determined through
observing spectral features (either absorption or emission) at fixed wavelengths which
21
1.0
hA
A
1.0
0.8
0.8
0.6
0.6-
0.4
0.4
0.2
0.2
0.0 8460
8490
8520
8550
8580
8610
8640
8670
0.0 8460
8700
8490
Wavelength. A (A)
8520
8550
8580
8610
Wavelength, A (A)
8640
8670
8700
Figure 2-4: The spectra of HE 2208-1239 (in black), before [left] and after [right]
corrections for Doppler shifting to match the template spectra of HD140283 (shown
in blue)
are, modulo Doppler shifting, dependent on the element in question. The strengths
of these spectral features, correlated with the abundance of the element in the star,
are quantified by their equivalent widths: the equivalent width of an absorption line
is the width of an "equivalent" feature with equal total flux deficit while dropping
emission intensity to zero. Formally,
Weq
/
Fcont -F(A)dA
=Jd
where Wgq is the equivalent width, Fcont is continuum flux, and the integral is taken
over the wavelength range of the feature of interest.
Equivalent widths are generally a good measure of feature strength and thus chemical abundances because unlike maximum emission/absorption intensities they are
insensitive to broadening effects that "flatten" spectral peaks. In practice, equivalent
widths were determined by applying a curve-fit to absorption lines of interest. While
ideal spectral features occur at a singular energy or wavelength, various features
contribute to the broadening and shape of the spectral line-for instance, Doppler
broadening from the Maxwell distribution of atom velocities in a star gives a char22
--
-..
----..--.
-..
-
1.0
0.8
-
0.60.4
0.2-
CO I
4118
4120
(a) Fitting of a Ca I line at 4121
careful fit by hand.
A.
4124
4122
4126
The poorly normalized continuum necessitates a more
0.4
0.2-M
I
4348
4350
4352
4354
4356
(b) Attempted fitting of a Mg I line at 4351 A. Due to the heavily blended feature it is
impossible to perform a clean line measurement; this line was discarded.
-
-..
-.
-..--..--.--..--.
1 .0 -.
0.8
0.60.40.2
0.014424
F
W4430
4
44.32
(c) Fitting of a Fe I line at 4427 A. There are evidently smaller lines blended into the wings
of this line, but a measurement can be made since the desired line is much stronger.
Figure 2-5: Gaussian fits to various spectral features for HE 2235 -5058.
alent width of each fit is calculated automatically by SMH.
The equiv-
acteristic Gaussian line-shape, while pressure broadening due to atomic collisions in
gaseous media form a Lorentzian line-shape. Thus, spectral lines are often fit with
Voigt profiles, which are convolutions of the Gaussian and Lorentzian curves. How-
ever, in large stellar bodies the added "macroscopic" Doppler effect from the rotation
of the star dominates the broadening effect- that is, stellar rotation introduces a much
larger line-of sight velocity dispersion from atoms on either side of the star than can
be induced thermally or from collisions. This is especially true in metal-poor stars,
since weak lines lie in the non-saturated optically thin regime which has minimal
pressure broadening, and are well fit by Gaussians.
23
For the spectra analyzed in this project, equivalent widths were determined by
fitting absorption features with Gaussian curves inside the SMH framework. For each
of the spectral lines, the continuum level on either side of the feature was manually
determined by inspection and input into the software; SMH then output a best-fit
curve to the line and determined the equivalent width by integrating the curve area
(Fig 2-5). The manual input of continuum level was then adjusted slightly on either
side to determine the sensitivity and uncertainty of this equivalent width measurement.
After normalization and Doppler correction for each of the 20 spectra, an
average of 600 spectral features were identified and by the SMH software based on an
input linelist of known absorption wavelengths and equivalent width measurements
were carried out in this fashion for most of these lines. In certain cases, insufficient
signal to resolve a given line or significant blending of two or more lines made fitting
a single Gaussian impossible and these lines were discarded. Other than the geocentric line-of-sight velocity from Doppler corrections, the equivalent widths were the
only information directly recovered from the raw spectra and thus it was important
that these measurements be completed carefully. These form the basis of chemical
abundance analysis.
Since stars are overwhelmingly mostly Hydrogen and Helium, and this project
is particularly interested in measuring stellar metallicities, only spectral features of
relevant metal (that is, non H or He) elements were identified and measured. In this
selection of 20 spectra, the elements associated with identified lines were predominantly Na, Mg, Al, Si, Ca, Sc, Ti, Cr, Mn, Fe, Co, Ni, Sr, Ba with occasional lines
due to 0, K, V, Zn. In particular, Sr, Ba, and C due to effects like isotopic and finestructure splitting cannot be acceptably measured with the aforementioned technique
and recovery of abundances of these elements will be addressed separately. Of these,
Fe I and II are of particular interest and primary importance to the understanding
of the metal-poor star, since it serves as a proxy for the overall metal content of the
star. As such, and because its spectral features are easily found in the optical range,
Iron peaks-especially Fe I lines-are most commonly found in the spectra; in general
every spectrum in this sample yielded ~ 250 measurable Fe I lines and
24
-
25 Fe II
ones. These Fe lines form the basis for the later modeling of stellar parameters and
they are entirely based on Fe equivalent width measurements. This will be addressed
in more detail in the next section.
2.3
Modeling Stellar Parameters from Equivalent Widths
After equivalent widths were measured for as many identified spectral lines as possible,
the Iron lines were isolated and used to model the stellar atmosphere via determination of four key stellar parameters:
" Effective Temperature Teff: The effective temperature of a star is the temperature of a black body with equivalent radiative flux, expressed in Kelvin.
Alternatively, it is the temperature of the star at Rosseland optical depthr
"
=
1.
Surface Gravity log(g): The surface gravity is the acceleration experienced on
the surface of the star due to gravity, expressed in cgs and log (base 10). The
surface gravity and temperature of a star together specify its state of stellar
evolution and the place on a given isochrone.
" Microturbulence Velocity p: The microturbulence of a star's atmosphere, although carrying units of km/s, is best thought of as a noise parameter and does
not physically exist in a star; it alters the expectation for line-shape and is used
in 1D plane parallel stellar models to account for the discrepancy between Iron
abundances obtained from different line strengths-i.e. it makes the abundances
of strong Iron lines agree with those of the weaker ones.
* Iron Abundance [Fe/H]: The Iron, or other element, abundance of a star indicates the number density of atoms of that element in the star. This is generally
presented in one of two notations containing equivalent information
For any element A, NA denotes the number of A atoms, and
[A/B] - log(NA/NB)* - log(NA/NB)o
25
191:
that is, [A/H] for a given star expresses the log ratio of A atoms to H atoms,
normalized to solar conditions. A negative value of [Fe/H] means the star has
less metals than the Sun, and a star is considered metal-poor at [Fe/H] <-2.0.
Alternatively and equivalently,
log,(A) = log(NA/NH) + 12.0
which gives the log number of A atoms in the star if NH were normalized to
solar conditions: NHO
2.3.1
1012.
-
From Equivalent Widths to Abundances
Intuitively, the equivalent width of every individual spectral line is related directly to
the abundance of its element-that is, the amount of absorbed flux depends directly
on the number of absorbers. The particular relation varies with the optical depth
of the absorption and the particular contributions to its line broadening. It can be
very crudely approximated into three different regimes: in the optically thin regime,
the abundance is very small and Weq
to saturate, Weq ~
/log
-
NA; as the Doppler-broadened wings begin
NA; finally, in the very optically thick regime, pressure
broadening effects dominate and Weq ~ VNA
15.
The specific dependence between a spectral line's reduced equivalent width Wred
log(Weq/A) and its element abundance is given by the star's curve of growth (Fig 2-
6) but the specific shape of a star's curve of growth depends in turn on its stellar
parameters and the element under consideration
For metal-poor stars on the red giant branch, which is the expected dominant
constituent of this sample, lines with Wred <-4.5 have an approximately constant
relation with its corresponding log, abundance. To maintain this linearity, lines with
a reduced equivalent width beyond -4.5 were not used in determining the element
abundance.
26
-4-
1I2
13
log NI 0
14
15
16
/50001)
Figure 2-6: The approximate curve of growth for a Sun-like star. Image taken from
[51
2.3.2
Stellar Parameter Fitting
The temperature, surface gravity, and microturbulence of the stellar atmosphere determine the curve of growth which relates the strength of a spectral line to the amount
of that element present in the star; in turn, the element abundances inferred from
measured spectral lines only make sense if the model applied to the stellar atmosphere
has the correct parameters.
The guiding principle is that all Iron lines, regardless of line strength, wavelength,
or whether the line comes from neutral Fe I or ionized Fe II, should reflect the same
abundance - all lines should point to the same amount of Iron since they come from
the same star. Thus, if the Fe I and Fe II abundances don't match, or there is a nonzero trend between excitation potential (the energy E = hc/A at which the absorption
occurs) and inferred abundance, or a trend between reduced equivalent widths and
inferred abundance, then the stellar parameters used in the atmosphere model are
incorrect and need to be adjusted 110].
The process of determining stellar abundances then becomes a process of maximizing agreement between Fe I and Fe II abundances and minimizing abundance
trends in excitation potential (Fig 2-8a) and reduced equivalent width (Fig 2-8b). In
practice, this is done iteratively, since there are multiple free parameters going into
27
1.0
1.0
0.8
0.8
0.6
0.6
4350
4370
4600
30
0.2
0.0
0.4
To
0.4
02
4000
H-i
5650
4859
4861
462002
0.2
555o
0.0
4863
H-
6560
6562
6564
Figure 2-7: The Ha and Ho features of HE 2201-4043 compared with those of
well known metal-poor stars. The reference stars used here are HD122563 (blue),
HE 1523-0901 (red), CS22892-52 (green), HD140283 (magenta), G64-12 (cyan).
One can infer that Teff of this star is roughly
-
4700K.
the model and each affects the abundance trends in different ways: Fe I lines tend to
be sensitive to temperature; in turn, Fe II lines tend to be sensitive to surface gravity.
Microturbulence affects primarily the Fe I lines of high reduced equivalent width.
To shrink the parameter space, a first estimate of the effective temperature of the
star can be made by comparing its Hydrogen Ha and Ho features with the spectra
of other, well-known, metal-poor stars (Fig 2-7). For stars of a similar metallicity,
a higher temperature and surface gravity imply a more compressed atmosphere; the
pressure then contributes to broader wings on the curve [15].
Using this temperature estimate, surface gravity and microturbulence are altered
until Fe I and II agree, and abundances maximally agree over excitation potential
and reduced equivalent width (Fig 2-8). This is taken to mean that the set of stellar
parameters input to the model are correct for the given star, and we have found its
isochronal position. However, it has been recorded in previous literature that effective
temperature determined with this method using Iron abundances is systematically low
compared to the temperatures determined using photometry methods. To correct for
this effect, after finding the set of Teff, log(g), p and [Fe/H] that minimizes these
trends, an empirical correction 16, 101 is applied to Teff as
T'ff
0.9Teff + 670
28
Using this new temperature, surface gravity, iron abundance and microturbulence are
altered until Fe I and II agree once again and there is no trend between abundance
and reduced equivalent width, but note that a small trend for excitation potential is
now expected (Fig 2-9). This has the overall effect of pushing a star down its specific
isochrone, and this corrected set of stellar parameters is quoted for the star (Table
2.1).
2.3.3
Stellar Parameters of 20 Metal-Poor Stars
Using this method, the stellar parameters determined for the 20 bright metal-poor
stars I personally analyzed are quoted below, before and after temperature correction
(Table 2.1). They are also shown plotted on an isochrone (Fig 2-10), and as expected
they fall on or close to their respective isochrones, indicating that the determined
stellar parameters for these stars are plausible solutions.
Combination with previous work
39 other bright metal-poor stars from this sample were analyzed using the SMH
framework in previously done work and will be quoted for analysis in future chapters.
Fig. 2-11 plots this total of 59 stars on an isochrone, and a table of their stellar
parameters can be found in the appendix. In general, stars in this sample are relatively
cool giants, with a temperature of ~ 4500K and a metallicity [Fe/H] of ~-2.5 to -4.5.
2.4
Abundances of Non-Iron Elements
The determination of stellar parameters fixes the atmospheric model and curve of
growth for a given star. This model can then be used to directly determine the
chemical abundances of other elements in the star.
29
Star Name
HE 2159-0551
HE 2220-4840
HE 2208-1239
HE 2201-4043
HE 2226-1529
HE 2234-4757
HE 2235-5058
HE 2250-4229
HE 2243-0244
HE 2322-6125
HE 0013-0522
HE 1116-0634
HE 0015+0048
HE 2123-0329
HE 1311-0131
HE 1317-0407
HE 2319-5228
HE 2324-0215
HE 0247-0533
HE 2340-6036
Star Name
HE 2159-0551
HE 2220-4840
HE 2208-1239
HE 2201-4043
HE 2226-1529
HE 2234-4757
HE 2235-5058
HE 2250-4229
HE 2243-0244
HE 2322-6125
HE 0013-0522
HE 1116-0634
HE 0015+0048
HE 2123-0329
HE 1311-0131
HE 1317-0407
HE 2319-5228
HE 2324-0215
HE 0247-0533
HE 2340-6036
Teff
4420
4750
4800
4900
4500
4400
5200
4590
5127
5115
4715
4100
4600
4600
4720
4500
4600
4300
4800
4400
P
2.55
1.90
2.40
1.60
2.50
2.80
1.70
2.30
1.60
1.75
2.00
2.95
2.00
1.90
2.10
2.40
2.30
2.00
1.75
2.60
Teff
P
4650
4945
4990
5080
4720
4630
5350
4801
5285
5273
4913
4360
4810
4810
4918
4720
4815
4540
4990
4630
2.30
1.90
2.00
1.75
2.35
2.50
1.69
2.10
1.50
1.70
1.75
2.50
1.90
1.80
2.10
2.40
2.15
2.60
1.75
2.69
Uncorrected
log(g)
0.72
1.30
1.20
1.80
0.40
0.35
2.50
0.75
2.44
2.00
1.10
0.30
1.00
0.90
1.00
0.40
0.40
1.00
1.45
0.69
Corrected
log(g)
0.80
1.60
1.80
2.10
0.90
0.85
2.85
1.30
2.69
2.35
1.70
0.50
1.49
1.40
1.40
0.85
0.90
1.00
1.85
0.65
[M/H]
-2.80
-1.23
-2.77
-2.50
-2.90
-2.78
-1.89
-2.87
-2.22
-2.30
-1.18
-1.29
-2.80
-1.04
-2.98
-2.82
-1.20
-2.75
-2.43
-1.47
[Fe/H]
-1.05
-1.48
-1.02
-2.75
-1.15
-1.03
-2.14
-1.12
-2.47
-2.55
-1.43
-1.54
-1.05
-1.29
-1.23
-1.07
-1.45
-1.00
-2.68
-1.72
[M/H]
[Fe/H]
-2.62
-1.07
-2.57
-2.38
-2.68
-2.60
-1.75
-2.72
-2.00
-2.15
-2.97
-1.07
-2.63
-2.89
-2.84
-2.67
-1.00
-2.70
-2.27
-1.33
-2.87
-1.32
-2.82
-2.63
-2.93
-2.85
-2.00
-2.97
-2.25
-2.40
-1.22
-1.32
-2.88
-1.14
-1.09
-2.92
-1.25
-2.95
-2.52
-1.58
Table 2.1: Stellar Parameters before and after temperature correction
30
(Fe I/HJ = -2.78 1 0.12 (N: 254),[Fe 1l/NM
5.4
5.2
i; 5.0
s~ 4.8
-
-2.76 - 0.11 (N: 29)
-0.008+40.007 dex evl (rm--.070, p-0.262).
+0.080 +A067 dex *V
(r- +0.223, p-0.244).
.
-~4.6
4.4
4.2
0
3
2
1
4
Excitation Potential, - (eV)
(a) A bundances v. Excitation Potential before temperature coi
rectio n. Note there is no significant trend and the Fe I and II
abunc ances agree.
(r-+0.062, p-0.323)
dex (r--0.09e, p-0.624)
+0.018 +0.029 4ex
5.4
5.2
-0.02sA:0.049
+
~5.0
S4.8
4.6
4.4
4.2
-6.5
-6.0
-5.5
-5.0
-4.5
Reduced Equivalent Width, kg1 (T)
(b) Abundances v. Reduced Equivalent Width before temperature
correction. Note there is no significant trend.
Figure 2-8: Before temperature correction parameters for HE 2201 -4043.
regression lines are not meaningful and can be ignored.
2.4.1
The blue
Abundances of General non-Iron Metals
For elements without significant hyperfine splitting or significant isotopes, it is sufficient to simply read off the abundances from the now determined curve of growth.
We use only the measured lines above a reduced equivalent width of -4.5
since the
log, chemical abundance in this regime is approximately constant across excitation
potential and reduced equivalent width (Fig 2-12). Therefore, it is sufficient to take
the arithmetic mean of abundances inferred by all measured lines for an element
[10].
This method of determining chemical abundances yields a large uncertainties for
many elements who have very few absorption lines in the optical range (Fig 2-13). For
certain elements such as 0, K, Sc, only one or two measurable lines are available in
the optical wavelength regime and the inferred abundance for these elements cannot
be quoted with much confidence.
31
[Fe U/H] - -2.62 A 0.13 (N: 254),[Fe II/H) - -2.63 1 0.11 (N: 29)
-. 034+0.007 dex eV' (r-O.291, p.O.000)
+0.076Ad*067 dex *V (r-+O.213, p=0.26)
5.6
5.4
5.2
+
S
4.4-
0
34
2
1
Excitation Potential, y (eV)
(a) Abundances v. Excitation Potential after temperature correction. Note the Fe I and II abundances agree but there is now a
trend.
ex (r--O.Oqvn,
+
-0.0s2
.048 dex
++
-6.5
-6.0
-5.5
-4.5
-5.0
Reduced Equivalent Width, iogl,(t
p-0.287)
)
4.6
4.4
pWd..g)
(r--0.209t
+
R0.003+0.020
5.6
5.4
(b) Abundances v. Reduced Equivalent Width after temperature
correction. Note there is no significant trend.
Figure 2-9: After temperature correction parameters for HE 2201 -4043.
regression lines are not meaningful and can be ignored.
2.4.2
The blue
Synthetic fitting of Sr, Ba, and C Lines
For certain spectral features with non-resolved hyperfine or isotopic splitting, or with
otherwise non-Gaussian line shapes, the equivalent width method of abundance determination becomes invalid.
In these cases, it becomes more useful to generate
synthetic spectra of an input abundance and determine the element abundance by
matching to the observed spectrum 161. The synthetically generated spectrum for
a given feature is based on a line list 181 of excitation potentials and expected oscillator strengths including information on hyperfine and isotopic splitting. Then,
pre-imposing an abundance and full-width-half-max smoothing parameter as determined from the previously determined stellar abundances, a synthetic spectrum is
generated.
Synthesized spectra for various abundances are then compared with the
observed feature to accurately characterize the element abundance of the star in ques-
tion. For this project, 6 features yielding abundances for 3 elements were analyzed in
32
Figure 2-10: 20 metal-poor stars plotted on an isochrone (left). The log(g) - P plot
(right) confirms that the input microturbulence factor is within expectation.
* sm
Ki 1413
.1.1 U1.
]
3.5
2-1.5
0
3.0
1
*.n
Kim
A
mA
.4.
2_w.s
42226-I4.
U
.*I. a
*Q
W
M-0sM
411-1ne
K
y
4
14m
2.5
2.0
Wi n
Ki 1 .,
T 4,0%1
1.5
*0
4
-
*231*-5232
",
om ais"o
.1452
4.22
42
-~zo
wimo,
2
A
*
..
*.*
IsI
1.0
-
0
.14
5
7000
6500
6000
5500
Teff (K)
5000
1
4500
3
2
4
5
log g
Figure 2-11: 59 stars from the bright metal-poor sample plotted on an isochrone.
33
3.0
2.7
KX
2.4
K
K
2.1
x
x
1.8
Excitation Potential,
1.2
0.6
3.0
2.4
y (eV)
3.0
x
x
x
xX
2.7
xKx
X
x
K
x
x
-
K
x-
2.1
K
K
K
K
1.8
-5.6
-6.0
x
K
-
2.4
"
1.8
-4.
-4.4
-4.8
-5.2
Reduced Equivalent Width, kog,( (LiU)
Figure 2-12: Determination of Ti II abundance for HE 1317-0407. The red points
indicate lines that were discarded due to excess noise or blending.
K
5.50
5.25
5.00
4.75
--
I---.-- - - - - - - - - - - -
---------~-----
4.50
1
0
3
2
Excitation Potential,
4
y (eV)
x
5.50
5.25
-" xXx
bo
5.00
---
- - - - - - - - - - - - .. .. - .
X
X
X
x
4.75
XK
Kx
4.50
-5.6
-4.8
-5.2
Reduced Equivalent Width, kog, 0(!L')
-4x
-4.4
Figure 2-13: Determination of Mg I abundance for HE 1317-0407. There are significantly fewer lines than Ti or Fe.
34
this way for each of the 20 metal-poor stars: the 4077 A and 4215 Alines of Strontium
(Fig.. 2-14), the 4554 A and 4934
A lines
of Barium (Fig. 2-15), and the 4313 -4323
A
forests of CH molecular lines (Fig 2-16). The lower panel in each of these plots shows
the synthetic spectra with different abundances plotted against the observed lines;
the upper panel shows the residual plot.
Strontium is not actually subject to isotopic and hyperfine splitting and has an
abundance entirely recoverable through equivalent width measurements. Rather, it is
used to calibrate the synthesis process and estimate the synthetic smoothing FWHM
parameter in the blue wavelength range. Since the resolution degrades towards redder
wavelengths, the FWHM becomes larger. This process also calibrates for residual
Doppler shifts.
Using the chemical abundance determined from equivalent width
measurements described above, the FWHM parameter is constrained by matching
the synthetic Sr lines to the observed ones (Fig. 2-14). Then, measurements of Ba
and C abundances can be performed.
Barium is subject to heavy hyperfine splitting and has strong isotopes; as such its
lines in the optical region are actually heavily blended multiple-excitation features,
making accurate equivalent width determination entirely infeasible. For this project,
we use the isotope ratios of Barium assuming only r-process neutron capture. Using
synthetic spectra to determine its abundances can take this effect into account, and
Ba abundances of each of the 20 metal-poor stars are constrained to within 0.1 dex.
Both Barium and Strontium are interesting elements because they are heavy
neutron-capture elements and therefore come from either the r- or s- process. Within
metal-poor stars, they are particularly easy to measure since their lines are strong
and lie within the optical range.
Carbon, in the form of molecular CH bands, is much more difficult to characterize since its spectral features are not singly resolved lines but have a clustered and
more complex structure (Fig. 2-16). It is clear in this case why equivalent width
measurements are insufficient. The synthetically generated spectra is also unable to
completely fit to the observed bands and often the two bands at 4313 A and 4323 A
yield slightly different abundances and even within the same bands the stronger and
35
0.50
0.25
4'
0.00
-
-0.25
-0.50
...... -.
1 .0
....... .. .
-..
.......
0.8
0.6
0.45gf(Sr)
= -0.60
- 05(S
0.2 4076.5
4077
4M7
4077.5
Wavelength, x (A)
4078.5
= -0.50
logc(Sr) = -0.40
-
4079
4019.5
0.50
0.25
S0.00
x-0.25
-0.50
1.0
0.8
0.6
0.40.24214.4
4214.8
4215.2
4215.6
Wavelength, x (A)
4216
-
toge(Sr) = -0-80
-
koge(Sr) = -0.70
--
toge(Sr) = -0.60
4216.4
4216.8
Figure 2-14: Fitting to Sr lines of HE 1311-0131 with synthetic spectra; the upper
panels show the residuals of the fits in the lower panels.
36
0.50
,j
0.25
a;
U
C
i-0.00,
-0.25
-0.50
LO
0.8
0.6
0.4
loge(Ba)
-
4552.6
log(Ba)
-
0.2 F
4553.2
4553.8
4554.4
Wavelength,
4555
.
= -1.96
oge(Ba) =
-
-1-86
= -1.76
4555.6
4556.2
(A)
0.50
0.25
0.00
-0.25
-0.50
-
0.8
--...-.. -.
....-..--
0.6
-
0.4
-
0.2
0.2-
4933.4
4933.6
4933.8
4934
Wavelength,
4934.2
4934.4
Iogc(Ba)
=
-1.80
Iogd Ba)
=
-1.70
logf(Ba) = -1.60
4934.6
A (A)
Figure 2-15: Fitting to Ba lines of HE 1311-0131 with synthetic spectra; the upper
panels show the residuals of the fits in the lower panels.
37
weaker lines do not agree in inferred abundance. This is due to uncertainties in the
line list as well as usage of the microturbulence parameter.
Nonetheless, the syn-
thetic spectra approach is able to effectively constrain the carbon abundance within
an accuracy of 0.3 dex.
Carbon abundance is particularly interesting in metal-poor stars since there has
been previous work indicating an increase of carbon enhancement for stars of lower
metallicity. A possible explanation for this is that carbon is an efficient cooling agent
for gas clouds, allowing Carbon-enhanced clouds to collapse and begin star formation
faster than otherwise
141.
In addition, Carbon abundances have been found to be
correlated with kinematic parameters such as maximum orbital galactic height for
metal-poor stars [13, 14].
2.4.3
Chemical Abundances of 20 Metal Poor Stars
The chemical abundances of the 20 metal-poor stars for a select few elements (C,
Fe, Zn, Ti, Sr, Ba) are tabulated below (Table 2.2). A comparison of the various
determined abundances together with published literature data is shown in Fig 2-17
as a function of [Fe/H]. As shown, the measured abundances for these 20 metal-poor
stars agree with literature data distributions. Metals such as Si, Ca, and Ti tended
to have a slight overabundance relative to Fe when compared with solar values, while
metals such as Al, Cr, Mn appear to be slightly deficient for its Fe abundance when
compared to Sun-like stars. This may be due to different heavy-element production
or neutron capture processes in these metal-poor stars compared to those of the
Sun. Looking at table 2.2, Many of these stars appear to be carbon enhanced, with
HE 2235-5058 and HE 2319-5228 being especially Carbon rich.s
38
0.50
41
I I 1111
4,
-0.25
AJ
wA
V - "
A-
ddb-
VT, V
'V
,
0.25
F
-0.50
1.0
0.8
0.6
0.4
-
0.2
logC(C)
=
5.50
logd(C)
=
5.60
goa(C) = 510
4306
4312
4310
Wavelength, A (A)
4308
4316
4314
0.50
0.25
43
U
C
4)
LM
0.00
^ ^^-
0
-0.25
-0.50
U
'
1.0
0.8
0.6
0.4
-
kogf (C) = 5.8 5
logf(C) = 5.95
-
logc(C)
-
0.2
4319.5
4321
4322.5
Wavelength,
4324
4325.5
= 6.05
4327
A(A)
Figure 2-16: Fitting to CH forests of HE 1311-0131 with synthetic spectra; the upper
panels show the residuals of the fits in the lower panels.
39
Figure 2-17: Chemical Abundances of 20 stars against previous literature
Star Name
HE 2159-0551
HE 2220-4840
HE 2208-1239
HE 2201-4043
HE 2226-1529
HE 2234-4757
HE 2235-5058
HE 2250-4229
HE 2243-0244
HE 2322-6125
HE 0013-0522
HE 1116-0634
HE 0015+0048
HE 2123-0329
HE 1311-0131
HE 1317-0407
HE 2319-5228
HE 2324-0215
HE 0247-0533
HE 2340-6036
[Fe/H]
-2.87
-1.33
-2.82
-2.76
-2.92
-2.87
-1.99
-2.97
-2.25
-2.40
-1.23
-1.32
-2.88
-1.13
-1.09
-2.92
-1.25
-2.95
-2.51
-1.59
[C/H]
-1.11
-2.75
-1.61
-2.17
-1.18
-2.75
-0.08
-2.18
-2.93
-1.93
-2.77
-4.02
-2.38
-2.78
-2.65
-1.53
-0.76
-1.35
-2.23
-1.44
[Sr/H]
-1.09
-4.79
-2.47
-2.80
-2.82
-2.87
-1.47
-1.57
-1.82
-2.67
-1.21
-5.97
-1.63
-1.32
-1.47
-1.07
-5.58
-2.97
-2.71
-5.39
[Ba/H]
-4.34
-1.76
-0.91
-1.23
-1.11
-2.98
-0.03
-1.98
-1.88
-1.00
-4.28
-5.50
-4.08
-4.22
-1.96
-1.42
-5.29
-2.88
-1.09
-4.94
[Ti/H]
-2.64
-1.67
-2.74
-2.38
-2.87
-1.14
-2.26
-1.12
-1.02
-2.97
-1.41
-4.21
-1.00
-1.27
-1.24
-2.59
-1.14
-2.78
-2.28
-1.30
[Zn/H]
-2.54
-1.21
-2.87
-2.58
-2.76
-2.44
-2.43
-2.79
-2.82
-2.45
-2.73
-2.76
-2.89
-2.73
-2.79
-2.60
-2.91
-2.71
-2.62
-1.16
Table 2.2: Chemical Abundances for 20 metal-poor stars
40
Chapter 3
Analysis of Kinematic Data
This chapter details the extraction of kinematic data for the sample of metal-poor
stars. The main tool for this segment of the analysis is galpy, a python library developed to model galactic dynamics [3]. It provides a built-in model of the Milky
Way potential and supports orbit integration. After obtaining astrometric and kinematic information on the phase-space position of the 59 bright metal-poor stars, their
galactic orbits were determined and integrated over a period of
3.1
-
10Gyr.
Parameters for Galactic Orbit Integration
In general, the 3 dimensional position and velocity vectors of a point-like massive
body at one given time are necessary and sufficient to determine its orbit in a known
potential such as the Milky Way. In the language of observational parameters, spatial
information is given by the Right Ascension and Declination of the star along with
its distance, and the velocity information is recovered with the combination of its
proper motion and heliocentric radial velocities. Together with some assumptions on
solar motion, taking its distance to the galactic center to be 8 kpc and its rotational
velocity roughly 220 km/s, these parameters completely determine the orbit of an
observed star and are used by galpy in its orbit integration routine. This section will
describe these parameters and how they were determined for the stars in this sample.
41
3.1.1
Astrometric Data: RA, Dec, and Proper Motions
The right ascension and declination of a star map out its angular position on the
celestial sphere-i.e. in a heliocentric frame. Together with the star's heliocentric distance, these completely specify the heliocentric position of the star. Proper motions,
typically units of milliarcseconds/ year, specify the angular direction and magnitude
of stellar movement.
The astrometric data for the stars in this sample were taken from the UCAC 4.0
catalogue [?]. Since these particular stars, chosen for their brightness, happen to
be close to the Sun, the recorded proper motions for these stars tended to be relatively large in magnitude. Nevertheless, they have correspondingly large uncertainty
ranges, especially for the more distant giants. In fact, these errors were sometimes
comparable in magnitude to the proper motions quoted, and ultimately dominated
the uncertainties of the integrated stellar orbits.
3.1.2
Heliocentric Velocities
The geocentric line-of-sight velocity of the star is recovered through measuring the
Doppler shift of the spectrum by cross-correlation with a template-this is described
in the previous chapter. Using information on the date and time of observation and
assumptions on the geocentric solar position (1 Au) and velocity
(~
27r Au/yr), one
can convert the radial velocity from a geocentric frame to a heliocentric one.
3.1.3
Heliocentric Distances
The determination of heliocentric distance is done by comparing the absolute and
apparent V magnitudes of the star. The luminosity distance is then given by
log10 d =
1
(mv - MV) + 1
5
-
The apparent magnitude of the star is taken also from UCAC 4.0. The absolute
magnitude, however, is derived based on the evolutionary status of the star and thus
42
W1216-1554
W1243-2408
W1313-1916
W1321-1750
W*1327-2116
IFitflA.243
0
*
-
*
Im1348+0135
*1431-1227
W0012-5643
10033-2141
HED037-4341
W00390216
IE0048-1109
fE2340-6036
10054-2542
HE0217-2819
-4.0
I
3
If0147-4926
-2.0
-2137-1240
*
10032-4056
12303-5756
1E0201-3142
HE0220-5947
_
.
0239323
-
E0231-2101
*
0242-5211
f11005-0739
!
00
W21590551
1 0
*
1052-2139
2
HE1052-2548
S1*2201-4043
12208-1239
12220-4840
*
322504229 2.0
I
W2226-1529
2234-4757
1*2235-5058
12243-244
2322-6125
.0
WOOI-3-522
*
U
A
4
W0015-0048
1*1116-0634
12123-0329
1*1311-0131
11317-0407
E1*2319-5228
*
11327-2326
I1225+0155
11523-0901
11320-1339
W*0223-2814
5
4.0
5.0
6.0
J.
0.U
HE1401-0010
*
LE
6500
6000
5500
Teff (K)
5000
4500
10102-5655
117-0201
4000
Figure 3-1: Reading of MV values from an isochrone plot with [Fe/H--3.0. The Teff
and log(g) parameters determine the star's position on the isochrone.
dependent on the stellar parameters described in the previous chapter. The metallicity
of a star specifies a particular isochrone, with its temperature the expected absolute
brightness can be derived. For this sample, the bright metal-poor stars were placed on
to isochrones with metallicity of the nearest 0.5 dex, and then absolute magnitudes
were read off based on its pre-determined Teff and log(g) parameters (Fig. 3-1).
The uncertainties of these distances are driven by the uncertainties of the absolute
magnitudes which come from the characteristic 0.3 dex error range to the surface
gravity.
Thus far, all the kinematic parameters for these metal-poor stars have been com43
le-7+9.999998e-1
1.8
1.6
'Z'
1.4
1.2
1.0
5
10
20
15
25
30
35
t (Gyr)
Figure 3-2: Plot of energy loss over time for HE 0201-3142. The energy error grows
with time but is still very small (AE/E < 10-').
puted or quoted from relevant catalogues, a full table of which and their associated
uncertainties can be found in the appendix.
3.2
Orbits of Metal-Poor Stars
With all the necessary ingredients for orbit determination now assembled, these kinematic parameters were input into galpy and the stellar orbits were integrated for a
timescale of 10Gyr. The orbital potential built into the galpy system and named
MWPotential2014 is a weighted combination of the power spherical potential and
the Miyamoto-Nagai and Navarro-Frenk-White profiles. Most of the resulting orbits
for this sample had an average radius of
-
10 - 20 kpc, but a small number of these
orbits appeared unbounded. The integration method is not sympletic, so the error of
orbit increases with integrated time, but energy is still approximately conserved (Fig.
4-4) so this is not a concern.
Fig. ?? shows the integrated orbits for a few of the metal poor stars. The first
44
column shows the orbit in X - Y position space in the disk plane, the second shows
galactic distance R with galactic height z, and the third shows R against radial
velocity
yR.
Since they are metal-poor, the expectation is that they reside mostly
outside the metal-rich thick disk. However, their current brightness implies at least
a temporary proximity to the Sun. We therefore expect these orbits to be fairly
eccentric, and indeed the eccentricity across all of these orbits was found to be
-
0.6
on average.
3.2.1
Uncertainty of Orbits
To ascertain the sensitivity of these orbits to the aforementioned uncertainties in
proper motions, as well as the significantly smaller uncertainties in RA/Dec, velocity, and distance, the orbit integration procedure for every star was repeated several
times while one or some of its parameters were perturbed within their quoted uncertainties (Fig 3-4). Since the largest magnitude of uncertainty stemmed from pmRa,
pmDec, and distance, these are the perturbed parameters in the orbits shown below:
the notation'+-+' indicates the orbit with input pmRA increased by its uncertainty,
pmDec decreased by its uncertainty, and distance increased by its uncertainty.
HE 1216-1554 had relatively small (<10%) parameter uncertainties, but it is clear
that its orbital macrostructure changes significantly under these perturbations-this
shows that these integrated orbits are quite sensitive to changes in kinematic parameters. In certain cases, the extremity of orbit variation under perturbation made it
impossible to ascertain the accurate shape of the star's orbit and it was ultimately
discarded from further analysis.
3.3
Orbital Potentials with Various Milky Way Masses
As previously mentioned, the Milky Way potential used in this analysis is a sum of
the power spherical potential, Miyamoto-Nagai, and Navarro-Frenk-White (NFW)
profiles with weights 0.05, 0.6, and 0.35 respectively.
The first is a spherically symmetric potential derived from power law density
45
201-3142Rz
(b)HE
(a)HE0201-3142XY
1E06-19
20
to
-o0
(c) HE 0201-3142-RvR
HE00481109
-10
-
-15
-20
--0
o
3
-a)
0201 14
--
-HE
1
2-XY0
20
(b) HE 0048-3119-Rz
(d)HE0048-119XY
-100-
HE0054--110
HE3D13
5
30
2
to
20
15
25
3D
35
(f) HE 0048-1109-RvRc
1
10
HE115&-2313
2
HE32-026
&
0
-20 H
0
10
10 20
50 10
is
20
25
0
(g) HE 1158-2313-XY
(e
HE1327--2326
E
E043-1109
35
30
z
(k(Ekw7-36-
(i) HE 1158-2313-RvR
R
WD
3 00
MO0
-200
-100
0
too
M0
10 10
E1327-2326
20
0
25
5
0
No0
(j) HE 1327-2326-XY
HE1143-0114
(h) HE 1143-3114-Rz
c orbits
or aHfewstars32n
-o
(i)
(o) HE 1143-0114-RvR
HE 1143-0114-XY
X
Figure 3-3: Integrated galacti
and R - VR (right) spaces.
46
-
Y (left), R
-
Z (middle),
HE1216-1554+++.
HE1215.1554+--
HE1216-1554+-+
30
.
40
10
10
.
0
-
-20
-10
-0
-20--
O -2o
-60
20
40
-60
(a) HE 1216-1554+++
-4W
0
-- 20
to
.0
O
w0
-30
-20
a
-to
10
2
400
(c) HE 1216-1554+-
(b) HE 1216-1554+-+
HE1216-1554
HE1216-2554.++
40
40
0
-20-
-20
-40
-40-4o
20
-20
-4a
40
0
-20
0
40
(f) HE 12654+
(e) HE 1216-1554
(d) HE 1216-1554++-
(f)
EE1216-1554
KE1216-1554--
HE1216-1554-+
30
30-
20-
10
00
-4D
-20
0
0
4to
(g) HE 1216-1554-+
-
-20
e010 20 30
-0
-
-20
-10
-
-20
-10
0
to
2
3D
(h) HE 1216-1554-+-
-3
2
(i) HE 1216-1554-
Figure 3-4: Integrated orbit of HE 1216-1554 under error perturbations of pmRA,
pmDec, and distance.
47
models with an exponential cut-off
1
-exp(-(r/rc)
2
)
4bi(r)
where a = 1.8 and r, = 1.9 kpc for the Milky Way. This factor includes a spherical
bulge in the center of the Milky way indicated by observed data but unaccounted for
by combinations of the Miyamoto-Nagai and NFW potentials.
The Miyamoto-Nagai profile is a famous "flattened" system defined by
4D2 (R, z) = -(R
2
+ (a + /z 2 +b2))-1/2
for the Milky Way a = 3 kpc and denotes a radial disk scale, and b = 0.28 kpc denotes
a characteristic disk height. This profile is used to describe the matter-dominated disk
portion of the galaxy.
The NFW profile is a well-known spherical model for dark matter distributions
defined as
2 1
4D3 (r) = (47rr(a + r) )-
and a = 16kpc is the characteristic halo length scale for the Milky Way with a total
mass of 8 x 10 1 1Mo. This potential describes the dark matter halo surrounding the
galaxy.
The Milky Way potential used in the preceding analysis assumed a Milky Way
mass of 8 x 10 11 MO. To determine the sensitivity of the above results to variations
in the Milky Way mass, the above analysis was repeated with Milky Way masses
of 10 12 MO and 2 x 1012 M 0 . The Milky Way mass was varied by changing the halo
mass-this was varied without changing the shape of potential and retaining the same
dynamical constraints on the Milky Way disk by perturbing the scale length of the
NFW portion of the potential while enforcing a 220 km/s radial velocity at Solar
distances. A galaxy mass of 8 x 10 11 MO corresponds to a NFW halo scale of 16 kpc,
while galaxy masses of 10 12 MO and 2 x 10 12 M® imply a halo scale of 19 and 31 kpc
respectively 13, 5].
48
1
3
0
.2 -0
0,.6.80
00s 000
10
0 .0
g0
MW Mass = 2e12
MW Mass = 1e12
MW Mass = 8ell
30
2
0
g0
4
0
0
UC0
0
4 00
30
20
10
0
0
50
40
0
3 50
2 00-
S
0
00
30
20
10
0
003
2
2
50
5000
0a
40
50
000
,
.
0
0
oo-
0
0
6'
500
0
3
10
0
3*
**0
,
00 00G oG
30
20
40
40
50
50
Star index
Figure 3-5: Changes in eccentricity, orbital apsis, and maximum orbital height (both
in kpc) with varying assumptions on Milky Way mass. The abscissa shows the stars
indexed 1-59.
49
As shown in Fig. 3-5 a smaller galaxy mass corresponds to a generally larger
orbit, further apsis, and higher eccentricity for the stellar orbit on for the same initial
parameters. The dispersion is significantly greater for orbits further from the galactic
center. However, since the dispersion is not very large and in fact contributes towards
a lesser source of uncertainty than the astrometry parameters, this effect can be safely
neglected in the subsequent analysis.
50
Chapter 4
Interpretation of Results
In this chapter we combine the kinematic and chemical results obtained for these 59
metal poor stars and interpret them in an attempt to gain insight into the early Milky
Way. These results are then combined and compared with similar kinematic and
chemical parameters for a thick-disk-centric metal-weak sample and the classification
of the bright metal-poor stars as true halo or disk stars is established.
4.1
The Bidelman-McConnell Sample
Since relatively few stars were analyzed from the bright metal-poor star sample in this
project, it is often unclear how the conclusions drawn from the data fit into a bigger
picture. It is thus useful to compare our results with previously published numbers
in order to correctly identify any trends, agreements or discrepancies.
The Bidelman-McConnell star sample is a selection of 302 "weak metal stars"
with metallicity -2.5
< [Fe/HJ < 0 [2]. These are more metal-rich compared to
our sample, are well-populated in both in the disk and the inner halo and serve as
a good standard against which to compare our results. We will quote the chemical
and kinematic parameters obtained by Beers et al. for the Bidelman-McConnell for
the remainder of this chapter, and a complete table of the parameters quoted can be
found in [1]
51
1
*
03I.
.
Z 0.6[
c 0.4
.
I0.2
00-3.5
-3.0
200.,
0-
-2.5
-2.0
-1.5
-2.5
-;.0
-1.5
-2.0
-1.5
*
,*..
3 -200
.
*..
: -400-
-3.0
-3.5
500 -0-
00A
04
*
-500-1000-
-1500-.
-2.5
-3.0
-3.5
-200
s
600
. -1000
*
*e-i
-3.5
-2.5
-3.0
-2.0
-1.5
[Fe/H]
Figure 4-1: Plots of Iron abundance
4.2
[Fe/HI
against eccentricity and U, V, W velocities.
Correlations between Metallicity and Orbital Kinematics
Since the most metal-poor stars are often the oldest and earliest population stars,
their orbital kinematics often reflect an earlier kinematic state of the galaxy. Thus,
a trend in certain orbital elements as a function of metallicity may indicate that
these orbital elements evolved along this trend over time. In addition to providing
insight to early Milky Way kinematics, the different orbital characteristics of different
metallicity stars also gives information on the current distribution of metals within
the Milky Way. The key parameters of interest are eccentricity of orbit and the three
space velocity components: radial velocity V, and tangential velocities U (in the disk
plane) and W (out of disk plane).
Fig. 4-1plots Iron abundance against these orbital parameters. There appears to
be no discernible trend in eccentricity or the out-of-disk W velocity, but the U and
V velocities take on a distinctly conal shape with respect to Iron abundance, with
tighter groupings towards higher metallicity and exhibiting increased scatter with
lower abundance. This suggests that extremely metal-poor stars have a much larger
range of phase-space distribution, while relatively metal-rich stars are kinematically
52
closer together. This is consistent with the model that metal-rich stars are predominantly confined to the thick disk and metal-poor stars are scattered around the much
larger halo.
Combining these plots with the analysis ?? on the Bidelman and McConnell sample lends more context since this star sample is of comparatively high metallicity and
resides predominantly in the metal-rich disk. Fig 4-2 shows the velocity plots with
both our 59 stars and the B+Mc sample. The conal structure is enhanced here and
very clear for all three velocity components, showing the tight grouping of the thick
disk and the large dispersion of the halo stars.
In turn, Fig 4-3 shows the abundance plotted against eccentricity and appears
to indicate two distinct populations in the [Fe/H] -e
plane. One of the clusters is
relatively metal-rich and of low eccentricity-presumably populated by disk stars. The
other population is looser and more ambiguous but has distinctly higher eccentricity
and lower metallicity. These are likely to be halo stars, and the 59 stars in this work
reside entirely in this population.
4.3
Identification of Halo Stars
The energy of a star's galactic orbit is tied to its U, V, W velocity components. Its
classification as a halo or disk star follows from its position in an energy diagram [1].
Stars confined to the thick disk tend to be low energy and stars in the halo tend to
be of much higher energy. Thus halo stars and disk stars can be distinguished by
looking at the orbital energy of the star. The Toomre diagram in Fig 4-4 shows the
stars in this project and the Bidelman-McConnell stars overlayed with equipotential
lines, which are concentric rings in velocity space. There is a clear clustering of stars
within the 100km/s ring, within which the disk is confined. Outside of this ring,
the density of stars in velocity space drops sharply as the halo is much larger, much
sparser, and allows for a much bigger range of energy dispersion. The metal-weak
Bidelman and McConnell stars are shown to populate both the disk and the halo,
but the much more metal-poor sample analyzed in this project is shown to consist of
53
This work
Bidelman-MacConnell Sample
ST
600
-0
400
. *
200
o
00
.
0
.4* 6
.1
,0.
0
.
-200
~
00
e
4%
.0
-400
-600
-800
-2
.
.
.
Sample
.
.
i
SBidelman-MacConnell
.
*
0
%
0
0
-10
.S . [Fe/H]
.@.i.
. 00
.
-3
%
-4
This work
0.0
. 00
*
-200
0
[Fe/HI
E
> -40(
0o 0 0
.
0
0
.
-4
0
-1
-2
-3
Bidelman-MacConnell Sampe
illi
0
This work
00
0S
-60(
-80
0
-3
o
0
0000
0
0
*
0
0
20
*0
-1
0
0
0
t
0
0
0
0
to0
00
0
*
b 00
0%
00
0
00
00
0
00 0
0e
0
0
30
-0 0
.
%
a 0Ot0
0
0
00
10(
E
-10
-20
-30
-40
-4
-3
-2
-1
0
[Fe/H]
Figure 4-2: Plots of Iron abundance [Fe/Hi and U, V, W velocities for both this project
and the B+Mc sample.
I
This work
Bidelman-MacConnell Sample
S
0
*
0
0
0
0
0
0
see*
S
0
-1
0
I
G)
0
0
0
0
00
%
0
%0
0
0
0
0
000
0
be
*
0
I
00
00
*
-3 1-
13.
*
%
%
*
U-
-2
S
*
~*
@0
,~
0
S
*D
0
-4
0.0
0.2
Figure 4-3: Plots of Iron abundance
and the B+Mc sample.
0.4
Eccentricity
[Fe/H]
0.6
0.8
1.0
against eccentricity for both this project
exclusively halo stars.
4.4
Carbon Abundances
Previous literature has indicated a trend between low metallicity in stars and Carbon
enhancement (the property of IC/Fe]>I1, or having a Carbon-to-Iron ratio more than
an order of magnitude larger than that of the Sun). Fig 4-5 shows these . Since
Carbon abundances were only available for the 20 stars I analyzed, data points are
rather scarce and it is difficult to discern any type of trend. However, three carbon
enhanced stars were identified from this sample of 20: HE 2208-1239, HE 2235-5058,
and HE 2319-5228. There appears to be a small cluster around IC/Fe]=0.5, but the
evidence is inconclusive due to the scarcity of data points, and will be re-examined
in future work.
As with Iron abundances, we combine our measurements with those from the
Bidelman-McConnell sample (Fig 4-6) in order to recover any correlations between
This work
Bidelman-MacConnell Sample
800
-
0-
600
~~0
E
-
S-
500
- - -
-
-
700
00
/
-
/
/
/0
/
300
-
0
0
0
-
,
I
-
400
O'0
00
q
-
*
.*
-
200
0
..
0
%4
0
/0
10(
l1
1
-600
-800
-1000
-400
V [km/si
-200
0
-1.5
-2.0
F -2.5
-
Figure 4-4: Plot of orbital energies (tangential against radial velocity) for both these
and the B+Mc stars. The dotted arcs are equipotential and the metal-rich disk is
confined inside the 100 km/s arc.
-3.0
-3.534.031
110
-0.5
0.0
0.5
1.0
1.5
2.0
25
3.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
.0
S0.4:
S0.2
-1.0
000
400
1.0
0
-
- 10
600
-0.5
5
E
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1 -..
-50
0
-0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
[CIFeI
Figure 4-5: Plot of Carbon abundance as IC/Fel against [Fe/HI, eccentricity, surface
gravity, maximum height, and orbital apsis. A star is Carbon-enhanced at IC/Fe]>1.
56
This work
Bidelman-MacConnell Sample
-2.
.5
0
-10
.O0
.
W05
0.
0
0
1o~e.0
205
5
3
0
00
4-
*.
vrm-.
yg
* .-.
:
0.
01
0
1.0
5
-0.5
-1.0
0.0
0.5
0.0
0.5
1.5
2.0
2.5
1.0
1.5
2.0
2.5
3.0
2.0
2.5
3.0
2.0
2.5
S0.8
0.6
0.4
S0.2
-~0.0
-0.2
0 1.5 2.0 2.5 3.0
-. 05 0.0 0.5 1.-0.5
-1.0
.5 .5
[C/
-0.5
S-1
A
30
20
0e
0
-*o**
1.5
1.0
0.5
0.0
-0.5
-1.0
s.
.
c
3.0
1.0
100
CL
80
0
~60
S40
CL
Lq0Op
20
0
-1.0
0
6
0
-05
00
q*
1.0
05
0
00
1.5
0
0
3.0
[C/Fe]
Figure 4-6: Plots of Carbon abundance as IC/Fej containing both the stars in this
work and the Bidelman-McConnell stars.
carbon abundances and other kinematic or atmospheric parameters. Although the
bright metal-poor distribution of [C/Fe] values appear to agree well with the more
metal-rich sample, no particular trends are apparent. However, the clustered population around [C/Fe]=0-0.5 is more heavily emphasized.
57
58
.
'..
'.......
4|')1biOa-'
e g s-e..g
n. ~gy-.p
t| ~ y-ae sss--,y
e e lyi1m M
39.9 y y
'
"'s:- .
.''4
-,r.. .n
.0.-
- -
.
-..
.;y-is
..- ,.
...
-
s
, .L
.
.
-
, -g .g
-
.-
y - : .-
-;.-,0-0
.-
mg
1
,:
L1B
a aiJ ?IeaJ
i)lM
Ile
i~
llWlkii
uIl
ns
oolLr
rm
an~ali
siljiiaa
im
mm
-a
:
Chapter 5
Conclusions and Future Work
This project concerned itself with a total of 59 stars from a sample of 1777 bright
metal-poor candidates in the Milky Way; these were observed at the Magellan-Clay
telescope and high resolution spectra were obtained.
From each spectrum, equivalent width measurements of numerous Iron lines were
obtained (- 250 for Fe I and - 20 for Fe II) and these were used to determine a model
of the stellar atmosphere, including effective temperature Teff, surface gravity log(g),
microturbulece velocity y and Iron abundance [Fe/H]. This was done by imposing
agreement on abundance values for every measured line for both neutral and ionized
Fe, and eliminating trends between abundance, excitation energies and equivalent
width. Using this stellar model and equivalent width measurements of lines from
other elements, the chemical abundances of many non-Iron metals were obtained.
Finally, the chemical abundances of Sr, Ba, and C for each star were determined by
fitting observed spectra with synthetically generated spectra accounting for hyperfine
and isotopic splitting. As part of this work, I personally analyzed 20 of these spectra
and quoted the remaining results from previous work.
For each of these 59 stars, astrometry parameters RA, Dec, pmRa, pmDec and
their associated errors were taken from the UCAC4.0 catalogue.
The heliocentric
velocity of each star was determined by measuring Doppler shift of spectral lines and
the luminosity distance was determined by comparing photometric measurements of
apparent magnitude with isochronal expectations of absolute magnitude. Using these
59
as input, the galactic orbits of these bright metal-poor stars were determined, integrated on a time scale of
-
10 Gyr. This process was repeated for several Milky Way
potential models with differing masses. These orbits yielded kinematic information
including maximum orbital height, energy, distance of apsis, and eccentricity.
The chemical and kinematic characterizations of these stars were then combined
to identify trends between stellar metallicity and orbital velocities and eccentricities.
Here, a previously published analysis of the Bidelman and McConnell sample was
used for comparison and context as it contained a sizeable amount of both disk
and halo stars. A conal structure was observed for orbital velocity dispersion across
metallicity as velocity scatter increased significantly towards the metal-poor end. Two
overlapping populations in eccentricity were observed and thought to represent disk
and halo populations respectively. Carbon abundances of each star were compared to
its orbital and chemical parameters but no significant structure was observed. Using
their orbital energies and the Bidelman-McConnell sample as context, all 59 bright
stars were identified to be true halo stars.
Future work will include the continued observations and analysis of remaining stars
in the bright metal-poor star sample, which will provide better understanding of the
distribution of low-metallicity stars in the galaxy and illuminate any substructure in
metallicity-kinematic trends. An understanding of the Carbon abundances of more
very metal-poor stars will also make clear any potential correlations between Carbon
enhancement. metallicity, and orbital kinematics, and increase our understanding of
Carbon distributions in our galaxy.
60
Appendix A
Tables
61
Table A.1: Astrometry
Star Name
HE1143-0114
HE1158-2313
HE1210-2729
HE1214-2704
HE1216-1554
HE1243-2408
HE1313-1916
HE1321-1750
HE1327-2116
HE1340-2343
HE1348+0135
HE1431-1227
HE0012-5643
HE0033-2141
HE0037-4341
HE0039-0216
HE0048-1109
HE2340-6036
HE0054-2542
HE0217-2819
HE2137-1240
HE0032-4056
HE2303-5756
HE0147-4926
HE0201-3142
HE0220-5947
HE0231-2101
HE0239-3236
HE0242-5211
HE1005-0739
HE1051-1331
HE1052-1852
HE1052-2139
HE1052-2548
HE2159-0551
HE2201-4043
HE2208-1239
HE2220-4840
HE2250-4229
HE2226-1529
RA
11.775
12.020
12.219
12.286
12.313
12.765
13.263
13.404
13.505
13.722
13.843
14.566
0.255
0.595
0.669
0.698
0.857
23.728
0.955
2.336
21.673
0.576
23.115
1.819
2.069
2.364
2.562
2.690
2.737
10.137
10.903
10.918
10.920
10.922
22.038
22.068
22.181
22.390
22.894
22.488
Dec
0.958
-23.510
-27.764
-27.351
-16.181
-24.417
-19.543
-18.106
-21.534
-23.970
1.339
-12.677
-56.441
-21.416
-43.422
-2.009
-10.887
-60.323
-25.436
-28.095
-12.451
-40.658
-57.676
-49.195
-31.466
-59.559
-20.801
-32.403
-51.973
-7.902
-13.798
-19.143
-21.931
-26.080
-5.613
-40.489
-12.408
-48.414
-42.218
-15.231
vhel
133.7
352.9
-83.4
316.2
333.5
271.5
256.8
-39
176.8
6.5
70.3
106
-268.3
-183.8
53.2
221.1
220.3
213.6
-232.9
51.3
-124.8
9999
9999
94.1
89.7
11.7
34.8
-51
29.8
48.2
190.2
23.1
295.3
234.7
-99.6
-31.1
-9.8
-100.6
-25
-139.9
62
pmRA
0
-21.3
-1.2
-14.3
-10.6
-52.1
9.1
-21.3
-34.2
-24
-29.9
-7
100.4
-6.1
1.7
16.9
-29.2
1.2
8.2
14.2
-5.4
80.6
48.3
9
5.2
22.1
4.9
3.1
14.3
-68.4
-4.8
-8.8
9.6
-24
-0.9
55.7
22.2
16.6
30.4
0.8
Err pmRA
50
1.6
0.7
1.1
1.6
1.6
2.4
1.1
0.9
1.7
1.1
0.9
0.9
0.9
0.9
1.3
1.1
1.1
2.4
1.7
0.7
1.5
1.1
1
1.5
1.7
1.1
1.2
0.9
1.5
1.2
1.3
1
1.1
1.6
1.5
0.8
1.3
0.8
1.6
pmDec
0
-10.3
-12.4
-9.2
-0.9
-55.2
-28.9
-1.6
-19.9
-28.1
4.4
-8
-59.9
-6.9
-9.3
-85.7
-180.4
-9.7
0.5
-58.3
-28.8
-69.1
-83.7
1.1
1.4
-34
-5.3
-2
-51.3
-35.4
-8.4
-9.2
-10.2
-37.4
-5.1
-6.2
-23.1
-38.4
-6.6
-8.3
Err pmDec
50
0.8
2.1
2
1.5
1
0.8
1.5
1
1.5
2.8
1.3
0.9
1
1.7
1.6
1.3
1.1
2
0.9
1.3
1.2
1
1
1.1
1.3
1.7
1.8
1
1.5
1.1
1
1.5
2
2.6
0.9
1.2
1
1.4
1.2
Table A.2: Astrometry (cont)
Star Name
HE2234-4757
HE2235-5058
HE2243-0244
HE2322-6125
HE0013-0522
HE0015+0048
HE1116-0634
HE2123-0329
HE1311-0131
HE1317-0407
HE2319-5228
HE1327-2326
HE1225+0155
HE1523-0901
HE1320-1339
HE0223-2814
HE1401-0010
HE0102-5655
HE0117-0201
RA
22.622
22.636
22.772
23.426
0.274
0.300
11.310
13.228
13.228
13.330
23.366
13.502
12.468
15.434
13.379
2.421
14.068
1.078
1.341
Dec
-47.694
-50.712
-2.483
-61.153
-5.098
1.086
-6.846
-1.788
-1.788
-4.386
-52.195
-23.698
-1.642
-9.194
-13.925
-28.013
-0.407
-56.662
-1.771
vhel
-102.4
52.5
48.9
329.6
-175.5
-48.8
115.5
-219.4
124.7
124.7
292.7
63.6
99.4
-221.1
172.1
149.5
387.4
269.7
-2
63
pmRA
-1.4
15.7
-3.5
20.8
27.3
-5
-8.1
-34.4
-34.4
-10.4
1.4
-51.6
-14.9
-24.8
-22.2
6.8
-14.5
6.7
22.8
Err pmRA
1.3
1.2
1.1
1.3
1.8
2.9
2.3
1.4
1.4
2
1.2
1.2
5.6
2.1
1.3
1
2
1.4
1.3
pmDec
0.6
-10.9
-23.5
-10.8
-12
-13.5
2.6
-2.8
-2.8
1.9
-1.8
47.1
-13.9
-31.3
4.6
-30.6
-36.8
-7.5
-9.1
Err pmDec
1.6
1.3
1.9
1.3
2.3
1.9
1.9
1.7
1.7
1.3
1.1
1.9
5.6
2.6
1.3
1.2
2.6
1.4
2.1
Table A.3: Heliocentric Distances
Star Name
HE1143-0114
H E1158-2313
HE1210-2729
HE1214-2704
HE1216-1554
HE1243-2408
HE1313-1916
HE1321-1750
HE1327-2116
HE1340-2343
HE1348+0135
HE1431-1227
HE0012-5643
HE0033-2141
HE0037-4341
HE0039-0216
HE0048-1109
HE2340-6036
HE0054-2542
HE0217-2819
HE2137-1240
HE0032-4056
HE2303-5756
HE0147-4926
HE0201-3142
HE0220-5947
HE0231-2101
HE0239-3236
HE0242-5211
HE1005-0739
HE1051-1331
HE1052-1852
HE1052-2139
HE1052-2548
HE2159-0551
HE2201-4043
HE2208-1239
HE2220-4840
HE2250-4229
mB
12.973
11.59
13.37
12.95
12.8
10.76
12.3
12.34
12.65
12.43
13.026
12.911
12.07
12.92
13.72
13.75
11.64
13.68
13.51
13.69
11.78
13.27
13.78
12.11
13.37
12.97
13.57
12.88
12.41
13.48
13.56
13.74
13.57
13.492
13.32
11.93
12.48
11.78
13.22
Mv
3.155
-2.182
-2.432
-1.049
-2.679
-1.255
-2.182
-1.333
0.858
-1.660
1.301
-2.979
3.005
0.816
-0.931
3.558
3.269
-2.517
3.242
4.141
-2.562
2.912
2.959
-1.979
-2.432
-0.201
-0.939
-1.156
0.559
3.138
-2.517
-2.486
-1.760
2.913
-2.432
-0.002
-0.621
-1.050
-1.677
64
. ..
. .....
B-V
0.426
0.900
0.938
0.755
0.972
0.778
0.900
0.838
0.680
0.892
0.624
1.012
0.424
0.645
0.782
0.373
0.389
0.942
0.480
0.369
0.960
0.458
0.441
0.870
0.938
0.682
0.744
0.767
0.658
0.432
0.942
0.936
0.840
0.459
0.938
0.691
0.713
0.745
0.829
R[kpc]
0.802
3.753
9.393
4.455
7.969
1.768
5.205
3.690
1.669
4.360
1.660
9.454
0.535
1.958
5.939
0.920
0.395
11.249
0.907
0.685
4.746
0.955
1.191
4.402
9.393
3.146
5.663
4.506
1.732
0.959
10.644
11.428
7.906
1.057
9.179
1.771
3.003
2.612
6.510
Rerr[kpc]
0.491
1.007
1.671
1.008
0.279
0.425
1.396
0.992
0.251
1.228
0.271
0.218
0.020
0.236
1.443
0.124
0.019
0.965
0.042
0.063
0.526
0.044
0.014
1.181
1.671
0.358
1.296
1.007
0.284
0.086
0.913
1.165
1.998
0.032
1.633
0.244
0.598
0.604
1.554
Table A.4: Heliocentric Distances (cont)
Star Name
HE2226-1529
HE2234-4757
HE2235-5058
HE2243-0244
HE2322-6125
HE0013-0522
HE0015+0048
HE1116-0634
HE2123-0329
HE1311-0131
HE1317-0407
HE2319-5228
HE1327-2326
HE1225+0155
HE1523-0901
HE1320-1339
HE0223-2814
HE1401-0010
HE0102-5655
HE0117-0201
Mv
12.95 -2.345
13.33 -2.380
13.81
1.635
11.46
1.056
13.1
0.687
13.71 -0.856
14.04 -1.282
12.65 -2.679
14.63 -1.473
13.446 -1.384
12.97 -2.380
14.31 -2.189
13.9
2.718
13.62 -0.621
12.25 -2.253
11.28 -0.856
13.17 3.062
13.9
3.062
13.93 -2.432
13.28 -1.269
mB
65
B- V
0.925
0.930
0.640
0.635
0.652
0.735
0.781
0.972
0.804
0.793
0.930
0.888
0.387
0.713
0.911
0.735
0.400
0.400
0.938
0.778
R[kpc]
7.482
9.038
2.027
0.899
2.250
5.836
8.094
7.437
11.479
6.420
7.657
13.252
1.442
5.076
5.227
1.906
0.874
1.223
12.157
5.676
Rerr[kpc]
1.642
1.824
0.316
0.150
0.350
1.285
1.857
0.274
2.734
1.314
1.545
1.852
0.100
1.011
1.390
0.420
0.074
0.103
2.163
0.900
Table A.5: Stellar Parameters
Star Name
HE1143-0114
HE1158-2313
HE1210-2729
HE1214-2704
HE1216-1554
HE1243-2408
HE1313-1916
HE1321-1750
HE1327-2116
HE1340-2343
HE1348+0135
HE1431-1227
HE0012-5643
HE0033-2141
HE0037-4341
HE0039-0216
HE0048-1109
HE2340-6036
HE0054-2542
HE0217-2819
HE2137-1240
HE0032-4056
HE2303-5756
HE0147-4926
HE0201-3142
HE0220-5947
HE0231-2101
HE0239-3236
HE0242-5211
HE1005-0739
HE1051-1331
HE1052-1852
HE1052-2139
HE1052-2548
HE2159-0551
HE2201-4043
HE2208-1239
HE2220-4840
HE2250-4229
Teff
5917
4693
4702
4880
4470
4873
4630
4837
5251
4783
5340
4410
6280
5320
4860
6430
6430
4630
5760
6650
4660
5880
6120
4765
4603
5008
4900
4846
5134
6016
4530
4576
4750
5971
4650
5090
4990
4945
4801
log(g)
3.95
1.05
0.8
1.6
0.4
1.5
1.05
1.45
2.45
1.3
2.7
0.35
3.4
2.4
1.65
4
3.65
0.65
3.05
4.4
0.65
3.1
3.35
1.15
0.8
2
1.65
1.55
2.35
3.6
0.65
0.7
1.25
3.3
0.8
2.1
1.8
1.6
1.3
66
[Fe/H]
-2.51
-2.83
-2.97
-2.9
-3.64
-2.92
-2.75
-2.49
-1.96
-2.7
-2.5
-3.19
-3.05
-2.56
-2.54
-2.52
-2.55
-3.58
-2
-2.37
-3.16
-2.98
-3.09
-2.94
-3.11
-2.76
-2.9
-2.9
-2.72
-2.7
-3.31
-3.6
-3.16
-2.75
-2.87
-2.63
-2.8
-3.32
-2.97
p
1.3
2.25
2.45
1.85
3.05
1.9
2.45
2.15
2.15
2.15
1.8
3.8
1.5
1.9
2.3
1.55
1.55
2.6
2
1.3
2.4
1.4
1.45
2.05
2.6
1.9
1.9
2.1
2.05
1.55
2.65
2.4
2.2
1.55
2.3
1.75
2
1.9
2.1
Table A.6: Stellar Parameters (cont)
Star Name
HE2226-1529
HE2234-4757
HE2235-5058
HE2243-0244
HE2322-6125
HE0013-0522
HE0015+0048
HE1116-0634
HE2123-0329
HE1311-0131
HE1317-0407
HE2319-5228
HE1327-2326
HE1225+0155
HE1523-0901
HE1320-1339
HE0223-2814
HE1401-0010
HE0102-5655
HE0117-0201
Teff
4720
4630
5350
5285
5273
4913
4810
4360
4810
4918
4720
4815
6180
4842
4630
4935
6232
6214
4729
4954
log(g)
0.9
0.85
2.85
2.6
2.4
1.7
1.49
0.5
1.4
1.4
0.85
0.9
3.7
1.8
1
1.69
3.7
3.75
0.8
1.4
67
[Fe/H]
-2.92
-2.85
-2
-2.25
-2.4
-3.22
-2.88
-3.32
-3.14
-3.09
-2.92
-3.25
-5.6
-2.75
-2.95
-2.78
-2.77
-2.83
-3.24
-2.84
p
2.35
2.5
1.6
1.5
1.7
1.75
1.9
2.5
1.8
2.1
2.4
2.15
1.7
1.85
2.6
1.97
1.65
1.7
2.55
2.15
68
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