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Structure of the Obscured Galactic Disk with Pulsating Variables

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Structure of the Obscured Galactic Disk with Pulsating Variables STRUCTURE OF THE OBSCURED GALACTIC DISK WITH PULSATING VARIABLES Gergely Hajdu FACULTAD DE FÍSICA INSTITUTO DE ASTROFÍSICA STRUCTURE OF THE OBSCURED GALACTIC DISK WITH PULSATING VARIABLES BY GERGELY HAJDU Thesis submitted to the Faculty of Physics at Pontificia Universidad Católica de Chile in partial fulfillment of the requirements for the degree of Doctor in Astrophysics Thesis submitted to the Combined Faculties for the Natural and for Mathematics of the Ruperto-Carola University of Heidelberg for the degree of Doctor of Natural Sciences Thesis Advisor MÁRCIO CATELAN Santiago de Chile, September 2019 c MMXIX, GERGELY HAJDU Se autoriza la reproducción total o parcial, con fines académicos, por cualquier medio o procedimiento, incluyendo la cita bibliográfica del documento. FACULTAD DE FÍSICA INSTITUTO DE ASTROFÍSICA STRUCTURE OF THE OBSCURED GALACTIC DISK WITH PULSATING VARIABLES BY GERGELY HAJDU Members of the Committee Márcio Catelan (IA – PUC) Eva K. Grebel (ARI – UH) Mario Trieloff (GEOW – UH) Thomas H. Puzia (IA – PUC) Manuela Zoccali (IA – PUC) Santiago de Chile, September 2019 c MMXIX, GERGELY HAJDU ACKNOWLEDGEMENTS I would like to thank all the support I have received during the wonderful years I have spent at the Pontificia Universidad Católica de Chile, Santiago, as well as during my one year exchange at the Astronomisches Rechen-Institut der Universität Heidelberg. First and foremost, I would like to thank my supervisor, Prof. Márcio Catelan, for all the support, encouragement, and motivation I have received from him. Secondly, I am also deeply thankful for my co-advisor and exchange host, Prof. Eva Grebel, for allowing me to spend a very productive, wonderful year in Heidelberg. The work presented in this thesis would not have been possible without that of Dr. István Dékány. We started working together on VVV data at Católica, and continued to do so after he moved to Heidelberg, which has resulted in all the results presented here. I cannot overstate his inspirational effect on my work, as well on my development as an astronomer. I would like to thank my mother for all the patience she has displayed while I was con- ducting my studies in Chile and Germany. I cannot end the acknowledgements without mentioning my comrades among the stu- dents, both at the IA, as well as at the ARI. At the former, I could always count upon Diego Calderón, Rodrigo Carvajal, Camila Navarrete, Katerine Joachimi, and many others, to have stimulating conversations about our research, as well as just to remind ourselves to relax, and spend time together, in order to ease our minds of the everyday stress experienced by the majority of students, in this otherwise unforgiving academic environment. During my time at ARI, I have received much support from Michael Hanke, Zdenek Prudil and Josefina Michea in the daily goings of both the university, as well as Heidelberg. I would like to thank my officemates at ARI, Bekdaulet Shukirgaliyev and Clio Bertelli Motta for a wonderful time. Finally, I would like to acknowledge the “First La Serena School for Data Science"", where I have been introduced for the first time to many of the statistical and data science methods iv ACKNOWLEDGEMENTS v utilized throughout this thesis. In this endevour, the book “Statistics, Data Mining, and Ma- chine Learning in Astronomy” (Ivezić et al. 2014) has also been indispensable. During my studies, I have been financially supported by CONICYT-PCHA (Doctorado Nacional 2014-63140099), by the Graduate Student Exchange Fellowship Program between the Institute of Astrophysics of the Pontificia Universidad Católica de Chile and the Zentrum für Astronomie der Universität Heidelberg, funded by the Heidelberg Center in Santiago de Chile and the Deutscher Akademischer Austauschdienst, as well as by the Ministry for Econ- omy, Development, and Tourism’s Programa Iniciativa Milenio through grant IC1200009. Additional support is acknowledged by Proyecto Basal PFB-06/2007 and AFB-170002, by FONDECYT grants #1141141 and #1171273, by CONICYT Anillo grant ACT 1101, by CON- ICYT’s PCI program through grant DPI20140066, and the CONICYT’s “Beca Asistencia a eventos y cursos cortos para estudiantes de doctorado - convocatoria 2015”. Processing and analysis of data were partly performed on the Milky Way supercomputer, which is partly funded by the Sonderforschungsbereich SFB 881 “The Milky Way System” (subproject Z2) of the Deutsche Forschungsgemeinschaft. CONTENTS ACKNOWLEDGEMENTS iv LIST OF TABLES ix LIST OF FIGURES x 1 INTRODUCTION 1 1.1 The Milky Way disk . .1 1.2 RR Lyrae variables . .5 1.3 Cepheid variables . .9 1.4 The VISTA Variables in the Vía Láctea survey . 13 1.5 The layout of the thesis . 14 2 RR LYRAE LIGHT CURVES IN THE NEAR-INFRARED 17 2.1 Light-curve properties of RR Lyrae variables . 17 2.2 Model representation of near-IR RRab light-curves . 21 2.2.1 The light-curve training set . 22 2.2.2 Data preparation . 23 2.2.3 Application of PCA to the KS-band light curves . 27 2.2.4 Approximation of the J-band light curve shape . 32 2.3 Robust fitting of RRL KS-band light curves . 35 2.4 Metallicity estimation from KS-band photometry . 39 2.4.1 VVV photometry of bulge RR Lyrae variables . 40 2.4.2 Revision of I-band RR Lyrae photometric metallicity estimates . 41 2.4.3 Iron abundance estimation from the KS-band light-curve shapes . 48 vi 2.4.4 Validation of the metallicity estimates . 52 3 RR LYRAE VARIABLES IN THE VVV DISK FIELDS 57 3.1 Searching for RR Lyrae stars in the VVV disk fields . 57 3.1.1 The VVV disk data set . 57 3.1.2 Variability and period search . 58 3.1.3 Classification of RRL candidates . 59 3.1.4 Calculation of individual RRL properties . 61 3.2 Properties of the RRL sample . 63 3.2.1 Spatial distribution . 63 3.2.2 Metallicity distribution . 69 3.2.3 Spatial variations in metallicity . 72 3.2.4 Comparison of the MDF to independent results . 75 3.3 Discussion and future directions . 78 4 RECALIBRATION OF VISTA PHOTOMETRY 81 4.1 VVV photometric inconsistencies . 81 4.2 VISTA calibration procedures . 82 4.2.1 The 2MASS Point Source catalog in the VISTA system . 83 4.3 Revision of H-band VISTA observations . 84 4.4 VISTA zero point bias in dense stellar fields . 86 4.5 Recalibration of VVV JHKS observations . 91 4.5.1 Effects of the photometric corrections . 93 4.5.2 Recommendations for future usage of VISTA observations . 96 5 CEPHEIDS IN THE VVV SURVEY 100 5.1 Cepheid search in the corrected VVV photometry . 100 5.1.1 Classification of Cepheid candidates . 101 5.1.2 Cepheid period-luminosity relationships . 105 vii 5.1.3 The near-infrared extinction law . 107 5.2 Distribution of the VVV classical Cepheid sample . 112 5.2.1 Apparent Cepheid pairs . 118 5.3 The radial age trend of VVV Classical Cepheids . 120 5.4 The VVV Classical Cepheids and the spiral arms . 124 5.5 Summary and future outlook . 128 6 CONCLUSIONS 130 6.1 Outlook . 132 AUTHOR’S PUBLICATIONS 134 REFERENCES 138 viii LIST OF TABLES 2.1 Collection of RR Lyrae near-IR photometric observations . 18 2.2 Parameters of the hyperparameter grid search . 50 2.3 Number of RR Lyrae variables in different metallicity bins . 51 3.1 Parameters of the GMM components towards the bulge and the disk . 71 5.1 Candidate Classical Cepheid pairs in the VVV Cepheid sample . 119 ix LIST OF FIGURES 1.1 Face-on map of the atomic hydrogen distribution in the Milky Way disk . .2 1.2 Galactic map of high mass star forming regions with measured parallaxes . .3 1.3 SDSS images of galaxies with warping disks . .4 1.4 Schematic Hertzsprung-Russel diagram of the positions of pulsating variable classes . .6 1.5 UBVIKS light curves of a typical RR Lyrae . .8 1.6 UBVRIJK light curves of a typical Classical Cepheid variable . 10 1.7 Galactic distribution of Classical Cepheids in the local neighborhood . 12 2.1 Example of local LASSO fits of RR Lyrae J and KS light curves . 24 2.2 Comparison of light-curve fits with circular normal and Fourier bases. 26 2.3 KS -band normalized, and J-band zero-point aligned RR Lyrae light curves . 27 2.4 RR Lyrae KS band principal components . 29 2.5 Principal component amplitude distribution . 31 2.6 Residuals of the J-band light-curve approximations . 34 2.7 Continuous Fourier representation of the principal components and the J-band regression coefficients . 35 2.8 Example light-curve fits of RR Lyrae stars . 38 2.9 I-band photometric metallicity distribution of bulge RR Lyrae 1 . 43 2.10 Separation of Oosterhoff classes . 44 2.11 I-band photometric metallicity distribution of bulge RR Lyrae 2 . 45 2.12 Photometric metallicities of bulge RR Lyrae with the Kepler calibration . 47 2.13 The KS-band light-curve parameter – metallicity training sample . 49 2.14 Revision of the KS-band metallicity estimates . 53 x 2.15 RR Lyrae KS band principal components . 55 3.1 Examples of VVV disk RR Lyrae light curves . 60 3.2 Principal component amplitude – period distribution of disk RR Lyrae . 62 3.3 Distribution of VVV disk RR Lyrae on the Galactic plane . 64 3.4 Distribution of VVV disk RR Lyrae in Galactic coordinates . 66 3.5 Color excess and magnitude distribution of disk RR Lyrae . 67 3.6 Galactic height and distance distribution of VVV disk RR Lyrae . 68 3.7 Kernel density estimates of the MDF of the bulge and disk RR Lyrae samples .
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