Chemical and Kinematic Properties of Bright Poor ARCHNEM MASSACHUSETTS INSTITUTE OFTECHNOLOLGy by Weishuang Linda Xu AUG 10 2015 Submitted to the Department of Physics LIBRARIES in partial fulfillment of the requirements for the degree of Bachelor of Science in Physics at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2015 Massachusetts Institute of Technology 2015. All rights reserved.

Signature redacted A uthor ...... -...-...... Department of Physics May 9, 2015 Signature redacted C ertified by ...... Anna Frebel Assistant Professor of Physics Thesis Supervisor

Signature redacted

/ - A ccepted by ...... - - 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 rFe/HI < -2.5 and proximity to the sun. Using these spectra I model the 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 relative to . Combining these chemical abundances with those from previously analyzed spectra from the same bright metal-poor sample, I perform determination and integration on a total of 59 metal-poor stars and extract their kinematic parame- ters. I also explore how these results depend on the assumed mass of the . These chemical and kinematic results are then combined and compared with com- paratively 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 and 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 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 conversa- tion 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

Signature redacted Professor Anna Frebel...... Thesis Supervisor Assistant Professor of Physics 8 Contents

1 Introduction 17

2 Analysis of Spectral Data 19 2.1 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 2.4 Abundances of Non-Iron Elements ...... 29 2.4.1 Abundances of General non-Iron ...... 31

2.4.2 Synthetic fitting of Sr, Ba, and C Lines ...... 32 2.4.3 Chemical Abundances of 20 Metal Poor Stars . . .. 38

3 Analysis of Kinematic Data 41 3.1 Parameters for Galactic Orbit Integration ...... 41 3.1.1 Astrometric Data: RA, Dec, and Proper Motions 42 3.1.2 Heliocentric Velocities ...... 42 3.1.3 Heliocentric Distances ...... 42 3.2 of Metal-Poor Stars ...... 44 3.2.1 Uncertainty of Orbits ...... 45 3.3 Orbital Potentials with Various Milky Way Masses . . . 45

9 4 Interpretation of Results 51 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

5 Conclusions and Future Work - 59

A Tables 61

10 List of Figures

2-1 Full spectrum of HE 2208-1239 after normalization and Doppler cor- rection ...... 20

2-2 Signal-to-Noise ratio as a function of wavelength for the spectrum of HE 2201-4043 ...... 20

2-3 Normalization of the 3600-1670 aperture of the spectra of HE 2208-1239 with a 4th-order spline function and 30A knot spacing...... 21 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 (show n in blue) ...... 22

2-5 Gaussian fits to various spectral features for HE 2235 -5058. The

equivalent width of each fit is calculated automatically by SMH. . . . 23

2-6 The approximate curve of growth for a -like star. Image taken from [5 1 ...... 2 7 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 (ma- genta), G64-12 (cyan). One can infer that Teff of this star is roughly ~ 4700K ...... 28 2-8 Before temperature correction parameters for HE 2201-4043. The blue regression lines are not meaningful and can be ignored...... 31 2-9 After temperature correction parameters for HE 2201 -4043. The blue regression lines are not meaningful and can be ignored...... 32

11 2-10 20 metal-poor stars plotted on an isochrone (left). The log(g) - A plot (right) confirms that the input 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 sig- nificantly 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. .... 39 2-17 Chemical Abundances of 20 stars against previous literature ..... 40

3-1 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 loss over time for HE 0201-3142. The energy error grows

with time but is still very small (AE/E < 10-7)...... 44

3-3 Integrated galactic orbits for a few stars in X--Y (left), R-Z (middle), and R - VR (right) spaces...... 46

3-4 Integrated orbit of HE 1216-1554 under error perturbations of pmRA, pm Dec, and distance...... 47

3-5 Changes in eccentricity, orbital , and maximum orbital height (both in kpc) with varying assumptions on Milky Way mass. The abscissa shows the stars indexed 1-59...... 49

4-1 Plots of Iron abundance [Fe/H] against eccentricity and U, V, W veloc- ities...... 5 2

12 4-2 Plots of Iron abundance [Fe/H] and U, V, W velocities for both this project and the B+Mc sample ...... 54 4-3 Plots of Iron abundance IFe/HI against eccentricity for both this project and the B+Mc sample...... 55 4-4 Plot of orbital (tangential against ) 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...... 56 4-5 Plot of Carbon abundance as [C/Fe against [Fe/HJ, eccentricity, sur- face , maximum height, and orbital apsis. A star is Carbon- enhanced at [C/Fe]>1...... 56 4-6 Plots of Carbon abundance as [C/Fe] containing both the stars in this work and the Bidelman-McConnell stars...... 57

13 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 obser- vation and analysis of old stars. Along with high- objects and the cosmic microwave background, the comparatively nearby old stars provide an additional probe towards understanding the early . 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 . In addition, the pursuit of metal-poor stars often leads to the discovery of individ- ually astrophysically interesting objects. A star with strong r-process enhancement, that is exhibiting overabundance of -heavy elements, for instance could be the site of rare and exotic processes. Most of all, metal-poor stars have the potential of containing within their atmo- spheres 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 - the process responsible for essentially the entire periodic table which remains yet poorly understood. Iron abundances can

17 be particularly interesting since 5 6 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 ) 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 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, metallic-

ities, 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 Made Harder 161.

2.1 Initial Processing: Normalization and Doppler Correction

Some processing of the metal-poor star spectra was necessary before analysis to cor- rect for bias effects, specifically variations in continuum flux and Doppler shifting

19 I .I JWLLI IL 1.0 -

0.8 0.6 nrr} 0.01 4000 4800 5600 6400 7200 oo 8800 Figure 2-1: Full spectrum of HE 2208-1239 after normalization and Doppler correc- tion

250

200 -

150 I

100

50 J-1'I' I

~II rP 4000 5000 6000 7000 8000 9000 Wavelength, A (A)

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, 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-of- sight velocity relative to the 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 1500 Scale: 1.015

1000

500

0

0.8 3600 3610 3620 3630 3640 3650 3660 3670 Wavelength, 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 Fea- tures

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 A hA 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 8700 0.0 8460 8490 8520 8550 8580 8610 8640 8670 8700 Wavelength. A (A) Wavelength, A (A)

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,

/ Fcont -F(A)dA Weq =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 chem- ical 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 -for instance, Doppler broadening from the Maxwell distribution of velocities in a star gives a char-

22 1.0 -- -.. -..----..--.- 0.8 - 0.6- 0.4

0.2- CO I

4118 4120 4122 4124 4126 (a) Fitting of a Ca I line at 4121 A. The poorly normalized continuum necessitates a more careful fit by hand.

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 - 0.6- 0.4-

0.2 F

0.014424 4 W4430 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. The equiv- alent width of each fit is calculated automatically by SMH.

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" from the rotation of the star dominates the broadening effect- that is, introduces a much larger line-of sight from 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 measure- ment. 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 geocen- tric 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 and , 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 predomi- nantly 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 fine- structure 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 - 25 Fe II

24 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 via determina- tion of four key stellar parameters:

" Teff: The effective temperature of a star is the tem- perature of a black body with equivalent radiative flux, expressed in .

Alternatively, it is the temperature of the star at Rosseland optical depthr = 1.

" Surface Gravity log(g): The surface gravity is the 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 and the place on a given isochrone.

" Microturbulence Velocity p: The microturbulence of a star's atmosphere, al- though 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 indi- cates the number of atoms of that element in the star. This is generally presented in one of two notations containing equivalent information 191:

For any element A, NA denotes the number of A atoms, and

[A/B] - log(NA/NB)* - log(NA/NB)o

25 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 1012. -

2.3.1 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 - NA; as the Doppler-broadened wings begin to saturate, Weq ~ /log 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 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 14 15 16 log NI 0 /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 de- termine 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 non- zero 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 max- imizing 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

0.4 To 0.4 4600 4350 30 4370 02 4000 462002 0.2 5650 H-i 555o 0.2 H-

0.0 4859 4861 4863 0.0 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 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 Uncorrected Star Name Teff P log(g) [M/H] [Fe/H] HE 2159-0551 4420 2.55 0.72 -2.80 -1.05 HE 2220-4840 4750 1.90 1.30 -1.23 -1.48 HE 2208-1239 4800 2.40 1.20 -2.77 -1.02 HE 2201-4043 4900 1.60 1.80 -2.50 -2.75 HE 2226-1529 4500 2.50 0.40 -2.90 -1.15 HE 2234-4757 4400 2.80 0.35 -2.78 -1.03 HE 2235-5058 5200 1.70 2.50 -1.89 -2.14 HE 2250-4229 4590 2.30 0.75 -2.87 -1.12 HE 2243-0244 5127 1.60 2.44 -2.22 -2.47 HE 2322-6125 5115 1.75 2.00 -2.30 -2.55 HE 0013-0522 4715 2.00 1.10 -1.18 -1.43 HE 1116-0634 4100 2.95 0.30 -1.29 -1.54 HE 0015+0048 4600 2.00 1.00 -2.80 -1.05 HE 2123-0329 4600 1.90 0.90 -1.04 -1.29 HE 1311-0131 4720 2.10 1.00 -2.98 -1.23 HE 1317-0407 4500 2.40 0.40 -2.82 -1.07 HE 2319-5228 4600 2.30 0.40 -1.20 -1.45 HE 2324-0215 4300 2.00 1.00 -2.75 -1.00 HE 0247-0533 4800 1.75 1.45 -2.43 -2.68 HE 2340-6036 4400 2.60 0.69 -1.47 -1.72 Corrected Star Name Teff P log(g) [M/H] [Fe/H] HE 2159-0551 4650 2.30 0.80 -2.62 -2.87 HE 2220-4840 4945 1.90 1.60 -1.07 -1.32 HE 2208-1239 4990 2.00 1.80 -2.57 -2.82 HE 2201-4043 5080 1.75 2.10 -2.38 -2.63 HE 2226-1529 4720 2.35 0.90 -2.68 -2.93 HE 2234-4757 4630 2.50 0.85 -2.60 -2.85 HE 2235-5058 5350 1.69 2.85 -1.75 -2.00 HE 2250-4229 4801 2.10 1.30 -2.72 -2.97 HE 2243-0244 5285 1.50 2.69 -2.00 -2.25 HE 2322-6125 5273 1.70 2.35 -2.15 -2.40 HE 0013-0522 4913 1.75 1.70 -2.97 -1.22 HE 1116-0634 4360 2.50 0.50 -1.07 -1.32 HE 0015+0048 4810 1.90 1.49 -2.63 -2.88 HE 2123-0329 4810 1.80 1.40 -2.89 -1.14 HE 1311-0131 4918 2.10 1.40 -2.84 -1.09 HE 1317-0407 4720 2.40 0.85 -2.67 -2.92 HE 2319-5228 4815 2.15 0.90 -1.00 -1.25 HE 2324-0215 4540 2.60 1.00 -2.70 -2.95 HE 0247-0533 4990 1.75 1.85 -2.27 -2.52 HE 2340-6036 4630 2.69 0.65 -1.33 -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 - -2.76 - 0.11 (N: 29) 5.4 - -0.008+40.007 dex evl (rm--.070, p-0.262). 5.2 . +0.080 +A067 dex *V (r- +0.223, p-0.244). i; 5.0 s~ 4.8

-~4.6 4.4 4.2 0 1 2 3 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.

5.4 +0.018 +0.029 4ex (r-+0.062, p-0.323) 5.2 + -0.02sA:0.049 dex (r--0.09e, p-0.624) ~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. The blue regression lines are not meaningful and can be ignored.

2.4.1 Abundances of General non-Iron Metals

For elements without significant hyperfine splitting or significant , it is suf- ficient 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) 5.6 -. 034+0.007 dex eV' (r-O.291, p.O.000) 5.4 +0.076Ad*067 dex *V (r-+O.213, p=0.26) S 5.2 +

4.4- 0 1 2 34 Excitation Potential, y (eV)

(a) Abundances v. Excitation Potential after temperature correc- tion. Note the Fe I and II abundances agree but there is now a trend.

5.6 R0.003+0.020 ex (r--O.Oqvn, pWd..g) 5.4 + -0.0s2 .048 dex (r--0.209t p-0.287)

4.6 + + + 4.4 -6.5 -6.0 -5.5 -5.0 -4.5 Reduced Equivalent Width, iogl,(t )

(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. The blue regression lines are not meaningful and can be ignored.

2.4.2 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 de- termination 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 os- cillator strengths including information on hyperfine and isotopic splitting. Then, pre-imposing an abundance and full-width-half-max smoothing parameter as deter- mined 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.

.1.1 U1. * Kism 1413 ] 3.5 2-1.5 0

A ", s"o 3.0 A om ai 1 *.n mA .1452 14m Kim 4.22 2.5

2_w.s 2 wimo,42 -~zo .4. 42226-I4. 2.0 U I. *231*-5232 K .* a *QWM-0s M y 411-1ne Wi n *0 1.5 Ki 1 4 - ., * .. *.* .14 4 T 4,0%1

1.0 0 - IsI

5

7000 6500 6000 5500 5000 4500 1 2 3 4 5 Teff (K) 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 0.6 1.2 1.8 2.4 3.0 Excitation Potential, y (eV)

3.0 x " x x x xX xKx X 2.7 x K K- x x 2.4 x- - K K

K 2.1 K K -4. 1.8 -6.0 -5.6 -5.2 -4.8 -4.4 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.

5.50 K 5.25

5.00 --I---.------4.75 ------~----- 4.50 0 1 2 3 4 Excitation Potential, y (eV)

5.50 x 5.25 -" xXx 5.00 ------.. .. - . X X bo x X 4.75 KxXK 4.50 -4x -5.6 -5.2 -4.8 -4.4

Reduced Equivalent Width, kog, 0(!L')

Figure 2-13: Determination of Mg I abundance for HE 1317-0407. There are signif- icantly 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 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 character- ize 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 = -0.50 0.2 - - logc(Sr) = -0.40

4076.5 4077 4077.5 4M7 4078.5 4079 4019.5 Wavelength, x (A)

0.50

0.25

S0.00

x-0.25

-0.50

1.0

0.8

0.6

0.4- - toge(Sr) = -0-80 0.2- - koge(Sr) = -0.70 -- toge(Sr) = -0.60

4214.4 4214.8 4215.2 4215.6 4216 4216.4 4216.8 Wavelength, x (A)

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) = -1.96 - oge(Ba) = -1-86 0.2 F - log(Ba) = -1.76 4552.6 4553.2 4553.8 4554.4 4555 4555.6 4556.2

Wavelength, . (A)

0.50

0.25

0.00

-0.25

-0.50

0.8 - ..--..-- ..- .- .. -.--..-

0.6

0.4 - - Iogc(Ba) = -1.80 0.2 0.2- Iogd Ba) = -1.70 - logf(Ba) = -1.60

4933.4 4933.6 4933.8 4934 4934.2 4934.4 4934.6 Wavelength, 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 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

0.25 A-

I I 1111 ddb- 41 AJV - " VT, V , 'V 4, wA -0.25 F

-0.50

1.0

0.8

0.6

0.4 - logC(C) = 5.50 = 5.60 0.2 logd(C) goa(C) = 510

4306 4308 4310 4312 4314 4316 Wavelength, A (A)

0.50

0.25 43 U C 0.00 LM ^ ^^- 4) 0 -0.25

-0.50

U ' 1.0

0.8

0.6

0.4 - kogf (C) = 5.8 5 - logf(C) = 5.95 0.2 - logc(C) = 6.05

4319.5 4321 4322.5 4324 4325.5 4327 Wavelength, 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 [Fe/H] [C/H] [Sr/H] [Ba/H] [Ti/H] [Zn/H] HE 2159-0551 -2.87 -1.11 -1.09 -4.34 -2.64 -2.54 HE 2220-4840 -1.33 -2.75 -4.79 -1.76 -1.67 -1.21 HE 2208-1239 -2.82 -1.61 -2.47 -0.91 -2.74 -2.87 HE 2201-4043 -2.76 -2.17 -2.80 -1.23 -2.38 -2.58 HE 2226-1529 -2.92 -1.18 -2.82 -1.11 -2.87 -2.76 HE 2234-4757 -2.87 -2.75 -2.87 -2.98 -1.14 -2.44 HE 2235-5058 -1.99 -0.08 -1.47 -0.03 -2.26 -2.43 HE 2250-4229 -2.97 -2.18 -1.57 -1.98 -1.12 -2.79 HE 2243-0244 -2.25 -2.93 -1.82 -1.88 -1.02 -2.82 HE 2322-6125 -2.40 -1.93 -2.67 -1.00 -2.97 -2.45 HE 0013-0522 -1.23 -2.77 -1.21 -4.28 -1.41 -2.73 HE 1116-0634 -1.32 -4.02 -5.97 -5.50 -4.21 -2.76 HE 0015+0048 -2.88 -2.38 -1.63 -4.08 -1.00 -2.89 HE 2123-0329 -1.13 -2.78 -1.32 -4.22 -1.27 -2.73 HE 1311-0131 -1.09 -2.65 -1.47 -1.96 -1.24 -2.79 HE 1317-0407 -2.92 -1.53 -1.07 -1.42 -2.59 -2.60 HE 2319-5228 -1.25 -0.76 -5.58 -5.29 -1.14 -2.91 HE 2324-0215 -2.95 -1.35 -2.97 -2.88 -2.78 -2.71 HE 0247-0533 -2.51 -2.23 -2.71 -1.09 -2.28 -2.62 HE 2340-6036 -1.59 -1.44 -5.39 -4.94 -1.30 -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 de- veloped to model galactic dynamics [3]. It provides a built-in model of the Milky Way potential and supports orbit integration. After obtaining astrometric and kine- matic information on the phase-space position of the 59 bright metal-poor stars, their galactic orbits were determined and integrated over a period of - 10Gyr.

3.1 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 and heliocentric radial velocities. Together with some assumptions on solar motion, taking its distance to the 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 dis- tance, these completely specify the heliocentric position of the star. Proper motions, typically units of milliarcseconds/ year, specify the angular direction and 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 rela- tively 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 distance is then given by

1 log d = - (mv - MV) + 1 10 5

The of the star is taken also from UCAC 4.0. The , however, is derived based on the evolutionary status of the star and thus

42 W1216-1554 W1313-1916W1243-2408 W1321-1750 W*1327-2116 IFitflA.243 Im1348+0135 *1431-1227 W0012-5643 * 10033-2141 0 - HE-D037-4341 -4.0 * W00390216 IE0048-1109 fE2340-6036 10054-2542 3 HE0217-2819 I-2137-1240 * 10032-4056 If0147-492612303-5756 -2.0 1E0201-3142 HE0220-5947 E0231-2101 _ . * 0239323 - 0242-5211 f11005-0739 2 * 00 !1052-2139 HE1052-2548 W21590551 1 0 S1*2201-4043 12208-1239 12220-4840 322504229 2.0 * W2226-1529 I 2234-4757 1*2235-5058 12243-244 2322-6125 WOOI-3-522 .0 * W0015-0048 4 U 1*1116-0634 A 12123-0329 1*1311-0131 4.0 11317-0407 E1*2319-5228 5.0 * 11327-2326 I1225+0155 6.0 11523-0901 J. 11320-1339 0.U W*0223-2814 5 HE1401-0010 * 10102-5655 LE 117-0201

6500 6000 5500 5000 4500 4000 Teff (K)

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 com-

43 le-7+9.999998e-1

1.8

1.6

'Z' 1.4

1.2

1.0

5 10 15 20 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 kine- matic 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 . 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, veloc- ity, 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 uncer- tainties (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 param- eters. In certain cases, the extremity of orbit variation under 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 0.05, 0.6, and 0.35 respectively. The first is a spherically symmetric potential derived from power law density

45 (a)HE0201-3142XY (b)HE 201-3142Rz

1E06-19 20 to -o0 -10 (c) HE 0201-3142-RvR HE00481109 -15 - -20 --0 3 o 1

-a) -HE --0201 14 2-XY0 (b) HE 0048-3119-Rz 20 (d)HE0048-119XY -100- HE0054--110 HE3D13

30 5 to 15 20 25 3D 35 2 (f) HE 0048-1109-RvRc

10 1 2 HE115&-2313

HE32-026 & 0

-20 H 10 0 10 20 50 10 is 20 25 0 30 35 (k(Ekw7-36- z (g) HE 1158-2313-XY (i) HE 1158-2313-RvR (e E E043-1109 R HE1327--2326 WD E1327-2326

3 00 10 10 20 25 0 5 0

MO0-200 -100 0 too M0 No0 (j) HE 1327-2326-XY (h) HE 1143-3114-Rz HE1143-0114 c orbits or aHfewstars32n

-o

(i) HE 1143-0114-XY (o) HE 1143-0114-RvR

Figure 3-3: Integrated galacti X - Y (left), R - Z (middle), and R - VR (right) spaces.

46 HE1216-1554+++. HE1216-1554+-+ HE1215.1554+-- 30 40 .

10 10 0 . -10 -20 - -0 -20--

-60 O -2o 20 40 -60 -4W -- 20 0 to .0 w0 O -30 -20 -to a 10 2 400

(a) HE 1216-1554+++ (b) HE 1216-1554+-+ (c) HE 1216-1554+-

HE1216-1554 HE1216-2554.++

40 40

0 -20-

-20

-40 -40- -4o -20 20 40 -4a -20 0 0 40 (f) HE 12654+ (d) HE 1216-1554++- (e) HE 1216-1554 (f) EE1216-1554 HE1216-1554-+ KE1216-1554-- 30 30- 20- 10 0 0

-10 - -0 -20 -

-20 -

4to - -4D -20 0 0 -20 -10 0 to 2 3D -3 2 e0 10 20 30 (g) HE 1216-1554-+ (h) HE 1216-1554-+- (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 4bi(r) -exp(-(r/rc) 2 ) 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

2 2 4D2(R, z) = -(R + (a + /z +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 surrounding the

galaxy.

The Milky Way potential used in the preceding analysis assumed a Milky Way mass of 8 x 10 11MO. To determine the sensitivity of the above results to variations in the Milky Way mass, the above analysis was repeated with Milky Way masses

12 12 of 10 MO and 2 x 10 M0 . 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 1012 M® imply a halo scale of 19 and 31 kpc respectively 13, 5].

48 MW Mass = 8ell MW Mass = 1e12 MW Mass = 2e12

1 .2 -0 0 g0 3 00s 000 0,.6-.80 10 0 2 30 4 0 g0 .0 0 0 UC0 0

0 10 20 30 40 50

4 00 0 0 3 50

2 00- 0 S

0 00

0 10 20 30 40 50 00- 0a 3 000 2 . 2 50 , 00 00G oG 5000 0 0 oo- 0 3 0 0 50- 6' **0 3* , 40 50 0 10 20 30 40 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 Z 0.6[ 1 * 03I. c 0.4 . I-0.2 . 00-3.5 -3.0 -2.5 -2.0 -1.5 200., ,*.. 0- * 3 -200 . *.. : -400-

-3.5 -3.0 -2.5 -;.0 -1.5 500 -- 0- 04 00A -500- * -1000- -1500-. -3.5 -3.0 -2.5 -2.0 -1.5

-200 . *e-i s -1000600 * -3.5 -3.0 -2.5 -2.0 -1.5 [Fe/H]

Figure 4-1: Plots of Iron abundance [Fe/HI against eccentricity and U, V, W velocities.

4.2 Correlations between Metallicity and Orbital Kine- matics

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 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 predomi- nantly 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 sam- ple 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 Bidelman-MacConnell Sample This work

ST 600 -0

400

. * 0 200

0 o . 00

0..46.1,* . 00 ~ 4% e .0 -200

-400

-600

-800 -4 -3 -2 -10

. . 00 .S .[Fe/H][email protected]. * . . .

0 % 0 %

0 i SBidelman-MacConnell Sample This work

. . .

* . 00 0.0

-200 [Fe/HI 0 E 0o 0 0 0 a 0Ot0% 0 . . -1 0 > -40( -4 -3 -2 Bidelman-MacConnell Sampe 0 illi This work 00 0S -60(

-80

0 -3 - 0 0 . -1 0 o 0 0 00 0000 0e 0 0 0 *0 to0 0 b 00

0 0 0 0% t * 00 00 0 0 0

30 * 0 0 0 0 00 20 00 0 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 Bidelman-MacConnell Sample This work

S 0 * 0 0 0 0 0 see* 0 S -1 0 0 S @0 ~* * 13. % * 0 % 0 0 I 0 G) -2 0 0 U- 000 0 00 0 * be * 0 % 0 %0 00 00 ,~ I -3 1- * 0 0 S 0 *D

-4

0.0 0.2 0.4 0.6 0.8 1.0 Eccentricity

Figure 4-3: Plots of Iron abundance [Fe/H] against eccentricity for both this project and the B+Mc sample. 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 Bidelman-MacConnell Sample This work

800 0- -

700

600 E ~~0 S------500 00

400 I , / 0 0 - -

/ / - 0 - 300 / O'0 00 /0 q 0 - .* * - .. 200 %4 0

/ 0 10( 0

1 l1

-1000 -800 -600 -400 -200 0 V [km/si

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.

-1.5 - -2.0 F -2.5 -3.0 -3.5-

25 3.0 34.0311 1 0 -0.5 0.0 0.5 1.0 1.5 2.0

.0

S0.4:

S0.2 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 400000 1.0 0 -

- 10 600 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 5 -500 1-.. E -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 Bidelman-MacConnell Sample This work

-2. .5 -10 0 .O0 W05 1o~e.0 0 5 205 3 . 0 0. 0

4- 00 *. yg vrm-. : * .-. 0. 0 1 0 5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 1.0 S0.8 0e 0.6 [C/ 3.0 c 0.4 .5 .5-1.0-. 05 0.0 0.5 1.0-0.5 1.5 2.0 2.5 3.0 0.0 0.5 1.0 1.5 2.0 2.5 S0.2 -0.5 -~0.0-0.2 S-1 A s. -*o** 0 . 30 20 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0

100 CL 80 0 ~60 S40 CL 0 0 20 L 6 q* 00 0 0 0 q0Op 0 -1.0 -05 00 05 1.0 1.5 2.0 2.5 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 popu- lation around [C/Fe]=0-0.5 is more heavily emphasized.

57 58

.'.. '...... ' "'s:- . .''4 -,r.. .n .0.- -- . -.. .;y-is ..- ,. ... - s , .L . . - , -g .g - .- y - : .- -;.-,0-0 .- ,: 4|')1 biOa-' e g s-e..gn. ~gy-.p t| ~ y--ae ss s--,y e e l y i1 m M39.9 y y mg1 L1B a-a a iJ ?Ie aJi)lM Ile i~llWlkii uIl nsoo lLr an~ali : rm siljiiaa imm m 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, inte- grated 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 RA Dec vhel pmRA Err pmRA pmDec Err pmDec HE1143-0114 11.775 0.958 133.7 0 50 0 50 HE1158-2313 12.020 -23.510 352.9 -21.3 1.6 -10.3 0.8 HE1210-2729 12.219 -27.764 -83.4 -1.2 0.7 -12.4 2.1 HE1214-2704 12.286 -27.351 316.2 -14.3 1.1 -9.2 2 HE1216-1554 12.313 -16.181 333.5 -10.6 1.6 -0.9 1.5 HE1243-2408 12.765 -24.417 271.5 -52.1 1.6 -55.2 1 HE1313-1916 13.263 -19.543 256.8 9.1 2.4 -28.9 0.8 HE1321-1750 13.404 -18.106 -39 -21.3 1.1 -1.6 1.5 HE1327-2116 13.505 -21.534 176.8 -34.2 0.9 -19.9 1 HE1340-2343 13.722 -23.970 6.5 -24 1.7 -28.1 1.5 HE1348+0135 13.843 1.339 70.3 -29.9 1.1 4.4 2.8 HE1431-1227 14.566 -12.677 106 -7 0.9 -8 1.3 HE0012-5643 0.255 -56.441 -268.3 100.4 0.9 -59.9 0.9 HE0033-2141 0.595 -21.416 -183.8 -6.1 0.9 -6.9 1 HE0037-4341 0.669 -43.422 53.2 1.7 0.9 -9.3 1.7 HE0039-0216 0.698 -2.009 221.1 16.9 1.3 -85.7 1.6 HE0048-1109 0.857 -10.887 220.3 -29.2 1.1 -180.4 1.3 HE2340-6036 23.728 -60.323 213.6 1.2 1.1 -9.7 1.1 HE0054-2542 0.955 -25.436 -232.9 8.2 2.4 0.5 2 HE0217-2819 2.336 -28.095 51.3 14.2 1.7 -58.3 0.9 HE2137-1240 21.673 -12.451 -124.8 -5.4 0.7 -28.8 1.3 HE0032-4056 0.576 -40.658 9999 80.6 1.5 -69.1 1.2 HE2303-5756 23.115 -57.676 9999 48.3 1.1 -83.7 1 HE0147-4926 1.819 -49.195 94.1 9 1 1.1 1 HE0201-3142 2.069 -31.466 89.7 5.2 1.5 1.4 1.1 HE0220-5947 2.364 -59.559 11.7 22.1 1.7 -34 1.3 HE0231-2101 2.562 -20.801 34.8 4.9 1.1 -5.3 1.7 HE0239-3236 2.690 -32.403 -51 3.1 1.2 -2 1.8 HE0242-5211 2.737 -51.973 29.8 14.3 0.9 -51.3 1 HE1005-0739 10.137 -7.902 48.2 -68.4 1.5 -35.4 1.5 HE1051-1331 10.903 -13.798 190.2 -4.8 1.2 -8.4 1.1 HE1052-1852 10.918 -19.143 23.1 -8.8 1.3 -9.2 1 HE1052-2139 10.920 -21.931 295.3 9.6 1 -10.2 1.5 HE1052-2548 10.922 -26.080 234.7 -24 1.1 -37.4 2 HE2159-0551 22.038 -5.613 -99.6 -0.9 1.6 -5.1 2.6 HE2201-4043 22.068 -40.489 -31.1 55.7 1.5 -6.2 0.9 HE2208-1239 22.181 -12.408 -9.8 22.2 0.8 -23.1 1.2 HE2220-4840 22.390 -48.414 -100.6 16.6 1.3 -38.4 1 HE2250-4229 22.894 -42.218 -25 30.4 0.8 -6.6 1.4 HE2226-1529 22.488 -15.231 -139.9 0.8 1.6 -8.3 1.2

62 Table A.2: Astrometry (cont)

Star Name RA Dec vhel pmRA Err pmRA pmDec Err pmDec HE2234-4757 22.622 -47.694 -102.4 -1.4 1.3 0.6 1.6 HE2235-5058 22.636 -50.712 52.5 15.7 1.2 -10.9 1.3 HE2243-0244 22.772 -2.483 48.9 -3.5 1.1 -23.5 1.9 HE2322-6125 23.426 -61.153 329.6 20.8 1.3 -10.8 1.3 HE0013-0522 0.274 -5.098 -175.5 27.3 1.8 -12 2.3 HE0015+0048 0.300 1.086 -48.8 -5 2.9 -13.5 1.9 H E1116-0634 11.310 -6.846 115.5 -8.1 2.3 2.6 1.9 HE2123-0329 13.228 -1.788 -219.4 -34.4 1.4 -2.8 1.7 HE1311-0131 13.228 -1.788 124.7 -34.4 1.4 -2.8 1.7 HE1317-0407 13.330 -4.386 124.7 -10.4 2 1.9 1.3 HE2319-5228 23.366 -52.195 292.7 1.4 1.2 -1.8 1.1 HE1327-2326 13.502 -23.698 63.6 -51.6 1.2 47.1 1.9 HE1225+0155 12.468 -1.642 99.4 -14.9 5.6 -13.9 5.6 HE1523-0901 15.434 -9.194 -221.1 -24.8 2.1 -31.3 2.6 HE1320-1339 13.379 -13.925 172.1 -22.2 1.3 4.6 1.3 HE0223-2814 2.421 -28.013 149.5 6.8 1 -30.6 1.2 HE1401-0010 14.068 -0.407 387.4 -14.5 2 -36.8 2.6 HE0102-5655 1.078 -56.662 269.7 6.7 1.4 -7.5 1.4 HE0117-0201 1.341 -1.771 -2 22.8 1.3 -9.1 2.1

63 Table A.3: Heliocentric Distances

Star Name mB Mv B-V R[kpc] Rerr[kpc] HE1143-0114 12.973 3.155 0.426 0.802 0.491 H E1158-2313 11.59 -2.182 0.900 3.753 1.007 HE1210-2729 13.37 -2.432 0.938 9.393 1.671 HE1214-2704 12.95 -1.049 0.755 4.455 1.008 HE1216-1554 12.8 -2.679 0.972 7.969 0.279 HE1243-2408 10.76 -1.255 0.778 1.768 0.425 HE1313-1916 12.3 -2.182 0.900 5.205 1.396 HE1321-1750 12.34 -1.333 0.838 3.690 0.992 HE1327-2116 12.65 0.858 0.680 1.669 0.251 HE1340-2343 12.43 -1.660 0.892 4.360 1.228 HE1348+0135 13.026 1.301 0.624 1.660 0.271 HE1431-1227 12.911 -2.979 1.012 9.454 0.218 HE0012-5643 12.07 3.005 0.424 0.535 0.020 HE0033-2141 12.92 0.816 0.645 1.958 0.236 HE0037-4341 13.72 -0.931 0.782 5.939 1.443 HE0039-0216 13.75 3.558 0.373 0.920 0.124 HE0048-1109 11.64 3.269 0.389 0.395 0.019 HE2340-6036 13.68 -2.517 0.942 11.249 0.965 HE0054-2542 13.51 3.242 0.480 0.907 0.042 HE0217-2819 13.69 4.141 0.369 0.685 0.063 HE2137-1240 11.78 -2.562 0.960 4.746 0.526 HE0032-4056 13.27 2.912 0.458 0.955 0.044 HE2303-5756 13.78 2.959 0.441 1.191 0.014 HE0147-4926 12.11 -1.979 0.870 4.402 1.181 HE0201-3142 13.37 -2.432 0.938 9.393 1.671 HE0220-5947 12.97 -0.201 0.682 3.146 0.358 HE0231-2101 13.57 -0.939 0.744 5.663 1.296 HE0239-3236 12.88 -1.156 0.767 4.506 1.007 HE0242-5211 12.41 0.559 0.658 1.732 0.284 HE1005-0739 13.48 3.138 0.432 0.959 0.086 HE1051-1331 13.56 -2.517 0.942 10.644 0.913 HE1052-1852 13.74 -2.486 0.936 11.428 1.165 HE1052-2139 13.57 -1.760 0.840 7.906 1.998 HE1052-2548 13.492 2.913 0.459 1.057 0.032 HE2159-0551 13.32 -2.432 0.938 9.179 1.633 HE2201-4043 11.93 -0.002 0.691 1.771 0.244 HE2208-1239 12.48 -0.621 0.713 3.003 0.598 HE2220-4840 11.78 -1.050 0.745 2.612 0.604 HE2250-4229 13.22 -1.677 0.829 6.510 1.554

64

...... Table A.4: Heliocentric Distances (cont)

Star Name mB Mv B - V R[kpc] Rerr[kpc] HE2226-1529 12.95 -2.345 0.925 7.482 1.642 HE2234-4757 13.33 -2.380 0.930 9.038 1.824 HE2235-5058 13.81 1.635 0.640 2.027 0.316 HE2243-0244 11.46 1.056 0.635 0.899 0.150 HE2322-6125 13.1 0.687 0.652 2.250 0.350 HE0013-0522 13.71 -0.856 0.735 5.836 1.285 HE0015+0048 14.04 -1.282 0.781 8.094 1.857 HE1116-0634 12.65 -2.679 0.972 7.437 0.274 HE2123-0329 14.63 -1.473 0.804 11.479 2.734 HE1311-0131 13.446 -1.384 0.793 6.420 1.314 HE1317-0407 12.97 -2.380 0.930 7.657 1.545 HE2319-5228 14.31 -2.189 0.888 13.252 1.852 HE1327-2326 13.9 2.718 0.387 1.442 0.100 HE1225+0155 13.62 -0.621 0.713 5.076 1.011 HE1523-0901 12.25 -2.253 0.911 5.227 1.390 HE1320-1339 11.28 -0.856 0.735 1.906 0.420 HE0223-2814 13.17 3.062 0.400 0.874 0.074 HE1401-0010 13.9 3.062 0.400 1.223 0.103 HE0102-5655 13.93 -2.432 0.938 12.157 2.163 HE0117-0201 13.28 -1.269 0.778 5.676 0.900

65 Table A.5: Stellar Parameters

Star Name Teff log(g) [Fe/H] p HE1143-0114 5917 3.95 -2.51 1.3 HE1158-2313 4693 1.05 -2.83 2.25 HE1210-2729 4702 0.8 -2.97 2.45 HE1214-2704 4880 1.6 -2.9 1.85 HE1216-1554 4470 0.4 -3.64 3.05 HE1243-2408 4873 1.5 -2.92 1.9 HE1313-1916 4630 1.05 -2.75 2.45 HE1321-1750 4837 1.45 -2.49 2.15 HE1327-2116 5251 2.45 -1.96 2.15 HE1340-2343 4783 1.3 -2.7 2.15 HE1348+0135 5340 2.7 -2.5 1.8 HE1431-1227 4410 0.35 -3.19 3.8 HE0012-5643 6280 3.4 -3.05 1.5 HE0033-2141 5320 2.4 -2.56 1.9 HE0037-4341 4860 1.65 -2.54 2.3 HE0039-0216 6430 4 -2.52 1.55 HE0048-1109 6430 3.65 -2.55 1.55 HE2340-6036 4630 0.65 -3.58 2.6 HE0054-2542 5760 3.05 -2 2 HE0217-2819 6650 4.4 -2.37 1.3 HE2137-1240 4660 0.65 -3.16 2.4 HE0032-4056 5880 3.1 -2.98 1.4 HE2303-5756 6120 3.35 -3.09 1.45 HE0147-4926 4765 1.15 -2.94 2.05 HE0201-3142 4603 0.8 -3.11 2.6 HE0220-5947 5008 2 -2.76 1.9 HE0231-2101 4900 1.65 -2.9 1.9 HE0239-3236 4846 1.55 -2.9 2.1 HE0242-5211 5134 2.35 -2.72 2.05 HE1005-0739 6016 3.6 -2.7 1.55 HE1051-1331 4530 0.65 -3.31 2.65 HE1052-1852 4576 0.7 -3.6 2.4 HE1052-2139 4750 1.25 -3.16 2.2 HE1052-2548 5971 3.3 -2.75 1.55 HE2159-0551 4650 0.8 -2.87 2.3 HE2201-4043 5090 2.1 -2.63 1.75 HE2208-1239 4990 1.8 -2.8 2 HE2220-4840 4945 1.6 -3.32 1.9 HE2250-4229 4801 1.3 -2.97 2.1

66 Table A.6: Stellar Parameters (cont)

Star Name Teff log(g) [Fe/H] p HE2226-1529 4720 0.9 -2.92 2.35 HE2234-4757 4630 0.85 -2.85 2.5 HE2235-5058 5350 2.85 -2 1.6 HE2243-0244 5285 2.6 -2.25 1.5 HE2322-6125 5273 2.4 -2.4 1.7 HE0013-0522 4913 1.7 -3.22 1.75 HE0015+0048 4810 1.49 -2.88 1.9 HE1116-0634 4360 0.5 -3.32 2.5 HE2123-0329 4810 1.4 -3.14 1.8 HE1311-0131 4918 1.4 -3.09 2.1 HE1317-0407 4720 0.85 -2.92 2.4 HE2319-5228 4815 0.9 -3.25 2.15 HE1327-2326 6180 3.7 -5.6 1.7 HE1225+0155 4842 1.8 -2.75 1.85 HE1523-0901 4630 1 -2.95 2.6 HE1320-1339 4935 1.69 -2.78 1.97 HE0223-2814 6232 3.7 -2.77 1.65 HE1401-0010 6214 3.75 -2.83 1.7 HE0102-5655 4729 0.8 -3.24 2.55 HE0117-0201 4954 1.4 -2.84 2.15

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