Planet Candidate Validation in K2 Crowded Fields by Rayna Rampalli
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Planet Candidate Validation in K2 Crowded Fields by Rayna Rampalli Submitted to the Departments of Astronomy and Physics in partial fulfillment of the requirements for Honors in Astrophysics at WELLESLEY COLLEGE May 2018 c Wellesley College 2018 Advisors: David Latham Harvard-Smithsonian Center for Astrophysics Andrew Vanderburg University of Texas Austin Allyson Bieryla Harvard-Smithsonian Center for Astrophysics Samuel Quinn Harvard-Smithsonian Center for Astrophysics Wesley Watters Wellesley College Department of Astronomy Planet Candidate Validation in K2 Crowded Fields Abstract The discovery of planets outside of our own solar system has captured the imagination of scientists and the public alike. In just the past decade, more than 3000 planets have been discovered with the groundbreaking NASA telescope missions Kepler and its successor K2. In just three years, the K2 mission has yielded some remarkable results, with the discovery of over 300 confirmed planets and 480 reported planet candidates to be validated. The K2 mission detects planets by recording any periodic dimming in stars; this dimming often indicates that a planet is in orbit around the star, blocking a portion of its light. A major challenge with the analysis of these data is to identify planets in star-crowded regions, where individual camera pixels overlap multiple stars. In this thesis, I developed, tested, and evaluated a validation process for ruling out false-positive detections of planets in K2 observations of star-crowded regions. Using Markov chain Monte Carlo analysis, I fitted a model to obtain the transit parameters for each candidate planetary system. Later, I used seeing-limited on/off imaging to rule out false positives due to nearby transiting binary star systems. These results were then evaluated using a software program called validation of exoplanet signals using a probabilistic algorithm (VESPA) to estimate the probability of a false-positive detection. Such techniques and results are important tools for conducting candidate validation and follow-up observations for space-based missions,including the upcoming Transiting Exoplanet Survey Satellite (TESS) mission, since its large camera pixels resemble K2 star-crowded fields. Acknowledgements My enormous thanks to my thesis advisors, Dave Latham, Andrew Vanderburg, Allyson Bieryla, and Sam Quinn, for their constant guidance and feedback on this research work. The amount of knowledge I gained spending the summer in Dave's office and throughout this past year surpasses anything I could have learned in a classroom. I am grateful to my on-campus advisor, Wes Watters, as well as my other thesis committee members, James Battat, Glenn Stark, and Smitha Radhakrishnan for their meticulous and thoughtful feedback and support. Additional thanks to Andy Mayo, Karen Collins, and the postdocs at the CfA, who helped me set up and run various software, and the observers on Mt. Hopkins who obtained TRES spectra and KeplerCam observations for the planet candidates. A special thanks to Casey Melton, Meba Gebre, Elif Samanci, and my other peers for giving me company and providing advice, laughter, and loud singing during the late nights in the science center. Lastly, I thank my family for their constant love and support as they continue to inspire and motivate me to pursue my scholastic goals. ii Contents Abstract i Acknowledgements ii List of Figures vi List of Tables xii 1 Introduction 1 1.1 History of Exoplanet Discoveries...........................1 1.2 Detection Methods...................................2 1.2.1 Radial Velocity Method............................2 1.2.2 The Transit Method..............................7 1.2.3 Additional Methods.............................. 12 1.3 The Kepler and K2 Missions............................. 13 1.3.1 Kepler ...................................... 15 1.3.2 From Kepler to K2............................... 16 1.4 Research Overview................................... 18 1.4.1 Candidate validation.............................. 18 1.4.2 Purpose..................................... 19 1.4.3 Thesis Overview................................ 20 2 Target Selection and Transit Prediction in Crowded Fields 21 2.1 K2 Campaign 9..................................... 21 2.2 Preparing for Candidate Validation.......................... 23 2.2.1 Target Selection................................ 23 2.2.2 Triage...................................... 25 2.2.3 Preparation for Ground-Based Follow-up.................. 27 2.3 C9 Results: Why so Few?............................... 30 2.4 Campaign 13...................................... 31 3 Transit-Fitting 32 3.1 Fitting.......................................... 32 3.2 Markov Chain Monte Carlo Sampling........................ 34 3.2.1 Affine-Invariant Sampling Algorithm..................... 35 3.2.2 Example: Fitting a Line to Data....................... 36 3.3 Emcee.......................................... 37 3.4 BATMAN........................................ 40 iii iv 3.5 Transit-Fitting Tool Compiling and Testing..................... 41 3.6 C13 Light Curve Fit Results.............................. 45 4 Seeing-Limited On/Off Imaging Technique 59 4.1 Overview........................................ 59 4.1.1 Problem..................................... 59 4.1.2 Technique.................................... 61 4.1.3 Estimating the Magnitude Difference Threshold for Contamination.... 63 4.2 Implementation..................................... 66 4.2.1 Preparing for On/Off Observations: Choosing Candidates......... 66 4.2.2 KeplerCam Observations........................... 68 4.2.3 KeplerCam Analysis.............................. 71 5 Seeing-Limited On/Off Imaging Results 74 5.1 Case 1: Target as Source................................ 74 5.2 Case 2: NEB as Contaminant............................. 78 5.3 Case 3: No sources of variation............................ 79 5.3.1 EPIC 210875208................................ 81 5.3.2 EPIC 246825406................................ 82 5.3.3 EPIC 247383003................................ 84 5.4 Conclusions....................................... 85 6 VESPA 91 6.1 Ruling Out False-Positives............................... 91 6.2 Validation........................................ 92 6.3 VESPA Validation Procedure............................. 93 6.3.1 VESPA Inputs................................. 93 6.3.2 Population Simulations............................ 94 6.3.3 Priors...................................... 95 6.3.4 Likelihoods................................... 95 6.3.5 Final Calculation And Outputs........................ 96 6.4 Results.......................................... 98 6.4.1 EPIC 247383003................................ 100 6.4.2 EPIC 246825406................................ 100 6.4.3 EPIC 246787971................................ 103 6.4.4 EPIC 210875208................................ 104 6.4.5 EPIC 246865365................................ 105 6.5 Evaluating VESPA................................... 108 7 Conclusions 111 7.1 Summary........................................ 111 7.2 Future Work...................................... 113 A Selecting Target Stars Using the TIC 114 B Querying the TIC 116 C Transit Predictor 119 v D Transit-Fitter 123 E Phase-Folded Light Curve 129 F C9 Target Stars 131 Bibliography 161 List of Figures 1.1 Geometry of radial velocity method with center of mass in red, star in yellow, and the planet in green. The system orbits counterclockwise. With respect to the observer, if the planet gravitationally pulls the star towards the Earth, we observe a blueshift in the stellar spectrum (left). If the planet pulls the star away Earth, we observe a redshift (red)...........................3 1.2 Radial velocity curve of 51 Peg B in orbital phase versus velocity shifts. At quarter and three quarters phase, the star is unshifted along our line of sight. We can see at half and full phase, the radial velocity shifts are the greatest because this is when the greatest blue or redshifts were measured as demonstrated in figure 1.1. Reproduced from [1].............................4 1.3 Schematic of how radial velocities are measured with the star as the yellow circle and the planet as the green circle. The radial velocity is measured with respect to the angle of inclination, i, so it is measured as v∗ sin i..............6 1.4 Radial Velocity Signals for various kinds of planets orbiting a solar-type star at a range of distances. Reproduced from [2]. As of now, we can get an RV precision of about 0.4 m/s.....................................7 1.5 Transit data from HD 209458b. Reproduced from [3]................8 1.6 Simplified model of a transit. Reproduced from [4]..................9 1.7 Geometry of the impact parameter. Adapted from [5]................9 1.8 Illustration of the Rossiter-McLaughlin (RM) Effect. Each column shows a sub- sequent transit phase represented by velocity versus spectral line profile. The first row shows the stellar disk with the grayscale indicating the rotation velocity. The approaching limb is black and the receding limb is white. The second row shows the bump caused by the planet blocking some of the red or blue shift from the stellar rotation. Reproduced from [4]....................... 11 1.9 Detections are primarily comprised of transit and RV measurements. As ex- pected, transits are period-limited. Reproduced from [6].............. 14 1.10 Detections were primarily comprised of RV measurements, and then transits grew to overtake these measurements with the launch