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Time-Domain Studies in the New Eras of Multi-Messenger Astrophysics and Big Data

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Time-Domain Studies in the New Eras of Multi-Messenger Astrophysics and Big Data Time-Domain Studies in the New Eras of Multi-Messenger Astrophysics and Big Data The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Villar, Victoria Ashley. 2020. Time-Domain Studies in the New Eras of Multi-Messenger Astrophysics and Big Data. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365823 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Time-domain Studies in the New Eras of Multi-Messenger Astrophysics and Big Data A dissertation presented by Victoria Ashley Villar to The Department of Astronomy in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Astronomy & Astrophysics Harvard University Cambridge, Massachusetts April 2020 c 2020 — Victoria Ashley Villar All rights reserved. Dissertation Advisor: Professor Edo Berger Victoria Ashley Villar Time-domain Studies in the New Eras of Multi-Messenger Astrophysics and Big Data Abstract Time-domain astrophysics probes high-energy phenomena, the end-lives of massive stars and the creation of new elements which enrich the cosmos. Wide-field, untargeted photometric surveys have drastically increased the quantity and variety of known extragalactic transients, continuously challenging our understanding of late-stage stellar evolution. At the same time, gravitational wave detectors have ushered in a new era of multi-messenger astrophysics. This thesis presents a series of theoretical and observational studies which address the questions associated with a new era of data-driven, multi-messenger time-domain astrophysics. First, we explore the breadth of engines and progenitor systems which lead to extragalactic transients. Through a systematic census of optical light curves, we explore the spread of stellar eruptions, explosions and collisions in simple feature spaces. We then take a detailed look into the eruption of a massive star with a neutron star companion. Second, we focus on the collisions of compact objects: neutron star mergers and black hole-neutron star mergers. We present a detailed analysis of the complete photometric dataset covering the first two months of the kilonova associated with the gravitational event GW1701817, resulting in the strongest constraints on the kilonova properties. We then present the first Spitzer Space Telescope infrared observations of the kilonova. Our results highlight the discrepancy between theory and the observed iii late-time behavior of kilonovae. Finally, we focus on the upcoming age of large data streams. In late 2022, the Vera C. Rubin Observatory will begin its Legacy Survey of Space and Time (LSST), which will increase our annual discovery rate of extragalactic transients by two orders of magnitude. Transients which are currently rare will become commonplace. In this context, we first present a case study of the rare class of superluminous supernovae (SLSNe) in the new era of LSST. We find that the number of LSST SLSN discoveries will rival the current literature sample in less than a week, and we will be able to recover physical parameters for most events without multi-wavelength follow up. However, no population studies of transients will be possible without classification of LSST light curves. We present two data-driven machine learning methods, one supervised and one semi-supervised, which classify supernovae from the Pan-STARRs Medium Deep Survey. iv Contents Abstract iii Acknowledgments x Dedication xiii 1 Introduction 1 1.1 A Zoo of Extragalactic Transients . 2 1.1.1 Stellar Eruptions . 3 1.1.2 Stellar Explosions (Supernovae) . 4 1.2 The Birth of Multi-messenger Astrophysics . 5 1.3 A New Era of Big Data . 7 2 The Intermediate Luminosity Optical Transient SN 2010da: The Pro- genitor, Eruption and Aftermath of a Peculiar Supergiant High-mass X-ray Binary 12 2.1 Introduction . 13 2.2 Observations . 15 2.2.1 Spitzer Infrared Imaging . 17 2.2.2 Ground-based Near-Infrared Imaging . 19 2.2.3 Ground-based Optical Imaging . 19 2.2.4 HST Optical Imaging . 21 v CONTENTS 2.2.5 Optical Spectroscopy . 22 2.2.6 Swift/UVOT Imaging . 22 2.2.7 X-ray Spectral Imaging . 23 2.3 The Multi-wavelength Properties of SN 2010da, its Progenitor, and its Progeny . 24 2.3.1 Light Curve and Spectral Evolution . 24 2.3.2 Spectroscopic Properties of SN 2010da . 32 2.3.3 X-ray Spectral Modeling . 46 2.3.4 The X-ray and UV Light Curves . 48 2.4 Discussion . 51 2.4.1 The Progenitor of SN 2010da . 53 2.4.2 The Progeny of SN 2010da and Its Environment . 55 2.4.3 SN 2010da as a High Mass X-ray Binary . 58 2.4.4 Comparison to Other Dusty ILOTs and Impostors . 59 2.5 Summary and Conclusions . 63 3 Theoretical Models of Optical Transients: A Broad Exploration of the Duration–Luminosity Phase Space 96 3.1 Introduction . 98 3.2 One-Zone Models and Mathematical Framework . 101 3.3 Specific Engine Sources . 104 3.3.1 Adiabatic Expansion (No Central Heating Source) . 105 3.3.2 Radioactive Heating from Decay of 56Ni . 106 3.3.3 r-Process Radioactive Heating (Kilonovae) . 115 3.3.4 Magnetar Spin-down . 120 3.3.5 Ejecta-CSM Interaction . 123 3.3.6 Hydrogen Recombination (Type IIP SNe) . 133 3.3.7 GRB Afterglows . 137 vi CONTENTS 3.3.8 Tidal Disruption Events . 140 3.3.9 Other Subclasses . 143 3.3.10 Combined Models: 56Ni Decay and Magnetar Spin-down . 148 3.3.11 Combined Models: 56Ni Decay and Ejecta-CSM Interaction . 150 3.4 Discussion . 152 3.4.1 Specific Engine Insights . 152 3.4.2 The Optical Transient Landscape . 159 3.4.3 Observability & Survey Considerations . 161 3.5 Conclusion . 163 3.6 Detailed CSM Models . 165 3.6.1 Recovering the Cacoon SBO Solution . 166 4 The Combined Ultraviolet, Optical, and Near-Infrared Light Curves of the Kilonova Associated with the Binary Neutron Star Merger GW170817: Unified Data Set, Analytic Models, and Physical Implications 175 4.1 Introduction . 178 4.2 Ultraviolet, Optical, and Near-Infrared Data . 188 4.3 Kilonova Model . 191 4.3.1 Asymmetric Model . 194 4.3.2 Fitting Procedure . 195 4.4 Results of the Kilonova Models . 202 4.5 Discussion and Implications . 204 4.6 Conclusions . 208 5 Spitzer Space Telescope Infrared Observations of the Binary Neutron Star Merger GW170817 258 5.1 Introduction . 259 5.2 Observations and Data Analysis . 261 vii CONTENTS 5.3 Comparison to a Kilonova Model . 263 5.4 Implications for IR Observations of Future BNS Mergers . 265 5.5 Conclusions . 267 6 Superluminous Supernovae in LSST: Rates, Detection Metrics, and Light Curve Modeling 272 6.1 Introduction . 273 6.2 Simulation Set-Up . 278 6.2.1 Constructing Simulated SLSN Light Curves . 278 6.2.2 Description of the LSST Simulation . 289 6.3 Characteristics of SLSNe discovered by LSST . 290 6.3.1 Efficiency and Metrics for Detectability . 296 6.4 Recovering the SLSN Parameters . 299 6.4.1 Injection and Recovery of Representative SLSNe . 300 6.4.2 Correlating SLSN Properties to Parameter Recovery . 303 6.5 Summary and Conclusions . 305 6.6 Appendix . 307 7 Supernova Photometric Classification Pipelines Trained on Spectro- scopically Classified Supernovae from the Pan-STARRS1 Medium-deep Survey 313 7.1 Introduction . 315 7.2 PS1-MDS Supernova Light Curves and Spectroscopic Classifications . 318 7.3 Analytical Light Curve Model and Fitting . 321 7.4 Classification Pipelines . 327 7.4.1 Feature Selection . 328 7.4.2 Data Augmentation . 330 7.4.3 Classification . 332 viii CONTENTS 7.5 Classification Results . 334 7.5.1 General Trends . 335 7.5.2 Assessing Misclassifications . 337 7.6 Comparison to Previous Photometric Classification Approaches . 339 7.7 Limitations and Future Directions . 342 7.8 Conclusions . 345 8 Let It RAENN: A Semi-supervised Photometric Classification Pipeline for Spectroscopically-classified PanSTARRs Supernovae 358 8.1 Introduction . 359 8.2 The PS1-MDS Supernova Sample . 362 8.3 A Semi-supervised Classification Pipeline . 366 8.3.1 Pre-processing with Gaussian Processes . 368 8.3.2 Unsupervised Learning: A Recurrent Neuron-based Autoencoder (RAENN) . 371 8.3.3 Supervised Learning: Random Forest Classifier . 376 8.4 Classification Results . 380 8.5 Discussion . 382 8.5.1 Comparison to Other Works . 384 8.5.2 RAENN Architecture: Limitations and Benefits . 385 8.6 Conclusions . 389 9 Conclusions and Future Directions 396 References 399 ix Acknowledgments This thesis – and the years of work leading up to it – would not have been possible without the encouragement and support of others. First, I would like to thank my advisor Edo Berger, who has mentored me throughout my six years at Harvard. Edo has prepared me with the toolset to pursue my own research interests, to continuously challenge myself, and to otherwise survive the world of academia. Edo’s advice and guidance have shaped the scientist I have become, and I will always be grateful to him. Faculty and staff at Harvard and beyond have also guided my path throughout graduate school. Thank you to the members of my research exam and thesis committees: Charlie Conroy, Josh Grindlay, Selma de Mink, Ramesh Narayan and Rosanne Di Stefano. Thank you as well to my external reader, Renee Hlozek. My graduate career would have been notably more hectic without the endless work of Lisa Catella, Peg Herlihy and Robb Scholten – thank you all for everything you do. I truly appreciate the financial support in part by the National Science Foundation, Harvard University, and the Ford Foundation through fellowships.
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