Ages of LMC Star Clusters from Their Integrated Properties

Ages of LMC Star Clusters from their Integrated Properties A dissertation submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Physics of the College of Arts and Sciences by Randa Asa'd M.S University of Cincinnati May, 11 2012 Committee Chairs: M. M. Hanson, Ph.D. and M. D. Sokoloff, Ph.D Abstract Star Clusters are the building blocks of galaxies. Determining their ages gives us information about the formation history of their hosting galaxies. For far-away galaxies, star clusters are not resolved. Only their integrated properties can be observed. Both integrated photometry and integrated spectra have been used as age indicators of stellar clusters. The Large Magellanic Cloud (LMC) is a perfect galaxy to test these methods of age determination, because its clusters are close enough to see their individual stars, but also far enough away so that each cluster can be observed as a whole. This work first shows that the traditional methods of using the integrated broad-band photometry for age determination are highly inaccurate. This is attributed primarily to two things. First, the UBV integrated broad-band aging methods require matching a cluster with an expected model prediction of the cluster colors as a function of age. The biggest problem we find is the stellar clusters in our sample do not typically lie on the model line based on their known age and extinction. That is to say, real cluster colors often do not match the model colors and can be found some distance from expected model values. A second issue, which has been previously documented in numerous studies, is the strong degeneracy between age and extinction in the UBV color-color plane. Certainly, providing more photometric bands will reduce degeneracy between age and reddening. Better yet, if extinction can be independently determined, we show that ages from methods based on integrated broad-band colors will more closely match those obtained from more accurate methods based on stellar photometry. But the underlying issue remains. Simple stellar population models often do not accurately represent the colors of real stellar clusters due to the incomplete and stochastic sampling of the stellar mass function in low and moderate mass stellar clusters. On the other hand, integrated spectra provide better age predictions than broad-band photometry in the wavelength range 3626 6248 A when compared with high resolution computational models. I obtained the integrated spectra of 20 clusters that didn't have integrated spectra in the optical range, or they have never been observed before. Using ths sample and 7 other clusters from the literature I show that the statistical Kolmogorov- Smirnov (KS) test can better find the closest match between the observed spectrum and theoretical model than the traditional χ2. Finally, I present a new software routine that efficiently predicts the age of a star cluster given its optical integrated spectrum compared to spectra generated by computational models. To my parents : Dorina and Samir Acknowledgements I would like to acknowledge my advisors, Dr. Margaret Hanson and Dr. Mike Sokoloff. I have been lucky to have them as advisors and mentors. Dr. Margaret Hanson is a role model for me. She not only trusted me by giving me the opportunity to choose and shape my own research project, but also helped me improve both my teaching and leadership skills by encouraging me to attend workshops, classes and even conferences overseas. Dr. Sokoloff has been very patient with me from the first day when I told him that I don't have the needed background knowledge for any research work. He simply replied: "That's why you are here; To learn!". And indeed, ever since then he has been teaching and guiding me. My gratitude also goes to Dr. Andrea Ahumada, who although not physically present with me, was a great advisor for me. She was so patient and kind to answer tens of my questions about my project. I would also like to thank all my professors in the physics department at University of Cincinnati, my professors at Jordan University of Science and Technology who taught me the basics of physics, as well as all my professors and teachers who taught me since the day I learned how to hold a pencil in my hand. My words won't be enough to express my gratitude to the great people I met in Cincinnati. They not only made the six years I spent here a joyful time, but also inspired and taught me a lot. They reshaped my personality in a good way. I would also like to acknowledge my two sisters, my friends and relatives overseas who never stopped encouraging me throughout the years. I am thankful for every person who had a role in my life helping me reach this point. Last and not least I want to thank my dear parents who trusted me, allowed me to follow my dreams and never stopped believing in me. For Chapter 2: Rolf Andreassen provided significant assistance in creating the χ2 surface plots. We also acknowledge critical suggestions and guidance early on in this project from Rupali Chandar, Ata Sarajedini, Bogdan Popescu and in particular, Mark Hancock, who shared his χ2 minimization software with us. This material is based upon work supported by the Na- tional Science Foundation under Grant No. AST-0607497 and AST-1009550 to the University of Cincinnati, P.I., M. Hanson. R.S.A. was supported in part by NSF Grant No. PHY-0855860 to the University of Cincinnati, P.I., M. Sokoloff. For Chapter 4: I acknowledge Dr. Stephane Blondin who helped me learn IRAF, Dr. Mario Muray for the assistance with the flux calibration, Dr. Nidia Morel helped establishing the collaboration of this work. Dr. San- tos, Dr. Fernandes, Claus Leitherer and Bogdan Popescu provided useful comments. I also acknowledge Dr. Sean Points for his assistance the staff at CTIO and SOAR for their valuable help and guidance. This material is based upon work supported by the National Science Foundation under Grant No. AST-0607497 and AST-1009550 to the University of Cincinnati, P.I., M. Hanson. NOAO sponsored my travels to Blanco and SOAR viii Contents List of Figures xi List of Tables xvii 1 Introduction 1 1.1 The Large Magellanic Cloud (LMC) . .1 1.2 Ages and Colors of Stars . .3 1.3 Simple Stellar Populations . .3 1.4 Masses of clusters using MASSCLEAN . .4 1.5 Some attempts to determine accurate ages of star clusters . .5 2 Investigating aging methods of LMC star clusters using integrated colours 9 2.1 abstract . .9 2.2 Introduction . 10 2.3 The cluster sample . 12 2.3.1 Using MASSCLEAN to estimate cluster mass . 12 2.4 CMD age compared with different studies of photometric age . 14 2.4.1 s-parameter age . 14 2.4.2 Hunter et al. photometric ages . 16 2.4.3 A χ2 minimization method . 19 2.5 The s-parameter age and Hunter age . 22 2.6 The χ2 minimization method . 23 2.6.1 The colours produced by the model . 26 2.6.2 Further investigations of the χ2 minimization method . 27 2.6.3 Restricting the extinction limit . 30 ix CONTENTS 2.6.4 The χ2 minimization surface plots . 31 2.6.5 χ2 minimization for lower metallicity . 35 2.7 Discussion . 35 2.8 Conclusion . 37 3 Integrated Spectra of Stellar Clusters 49 3.1 The Spectrum of a Star . 49 3.1.1 The Spectral Classes . 51 3.2 Ages of Star Clusters from their Integrated Spectra . 52 3.3 Observations . 58 3.3.1 Day Calibration images . 62 3.3.2 Night observations . 66 3.4 Data Reduction . 67 4 Ages of LMC star clusters from integrated spectra 73 4.1 Introduction . 73 4.2 The Data . 74 4.3 Integrated Spectra Models . 81 4.4 The Method . 84 4.5 More Integrated Spectra from the Literature . 97 4.6 Review of Previous Results on the Subsample of this Work . 119 4.7 Discussion . 119 4.8 Conclusion and future work . 119 5 Conclusion and Future Work 131 5.1 Introduction . 131 5.2 Introducing New Software: ASAD . 132 5.3 Future Work . 133 Bibliography 137 x List of Figures 1.1 The Large Magellanic Cloud galaxy . .2 1.2 The HR diagram . .3 1.3 Integrated colors for different SSP models as a function of age for solar metallicity . .4 1.4 Star clusters follow a main sequence similar to the one of the stars. .6 1.5 S parameter . .7 2.1 The number of clusters in our sample for each log (age). Our sample has few age gaps, the biggest one is between log (age)= 8.2 and 8.6 yrs. 13 2.2 Estimating cluster mass. 14 2.3 The number of clusters in our sample for each mass region listed in section 2.1. 15 2.4 The s-parameter-derived age vs CMD-derived age. The two aging methods show a very good match, with a correlation coefficient of 0.95. 16 2.5 Mass regions on the s-parameter age versus CMD age diagram. 17 2.6 Hunter et al. (2003) photometry-derived age vs CMD-derived age. 18 2.7 Similar to Figure 6, but the mass regions are identified. For this sample the scatter about the line doesn't appear to be a clear function of cluster mass. 18 2.8 The modeled colour-colour diagram for different values of reddening.

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