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Improving SN Ia Distance Measurements Through Better Understanding of SN Ia Systematic Uncertainties

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Improving SN Ia Distance Measurements Through Better Understanding of SN Ia Systematic Uncertainties University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2013 Improving SN Ia Distance Measurements Through Better Understanding of SN Ia Systematic Uncertainties Jennifer Lynn Mosher University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Astrophysics and Astronomy Commons, and the Physics Commons Recommended Citation Mosher, Jennifer Lynn, "Improving SN Ia Distance Measurements Through Better Understanding of SN Ia Systematic Uncertainties" (2013). Publicly Accessible Penn Dissertations. 782. https://repository.upenn.edu/edissertations/782 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/782 For more information, please contact [email protected]. Improving SN Ia Distance Measurements Through Better Understanding of SN Ia Systematic Uncertainties Abstract Distance measurements using Type Ia Supernovae have enabled the startling discovery that the expansion of the universe is accelerating. To determine the nature and the source of this acceleration, systematic uncertainties on distance measurement must be reduced. Due to their importance to high- redshift optical SN Ia cosmology and their sensitivity to dust and progenitor metallicity effects, understanding rest-frame near-UV (NUV) measurements of Type Ia SNe is key to reducing these systematic uncertainties. Unfortunately, the calibration and acquisition of this data is challenging. We use direct comparisons of low-redshift SDSS-II and Carnegie Supernova Project NUV SN Ia photometry to quantify uncertainties on our ability to calibrate observer frame observations, and find that photometry in this region is consistent at the level of 2% in flux with a 6% scatter about the mean. Monte Carlo simulated SN Ia samples are used to directly measure Hubble Diagram biases resulting from SN Ia model training. Four simulated SN Ia samples are used to train the SALT-II SN Ia model: two width- luminosity adjustments and two intrinsic scatter models are tested. Adding intrinsic scatter to the training sample yields biased color laws and wavelength-dependent scatter in the NUV region, and causes the color correction parameter β to be systematically underestimated. Assuming a flat ΛCDM cosmology and including BAO and CMB constraints, three of our tests correctly recover the Dark Energy equation of state parameter w. The fourth test gives a w offset of 0.02, with a 4-σ significance. The software developed to support this work may be adapted to measure Hubble Diagram biases for any combination of SN Ia model and surveys. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Physics & Astronomy First Advisor Masao Sako Keywords Calibration, Cosmology, Distance Measurement, Simulations, Type Ia Supernovae Subject Categories Astrophysics and Astronomy | Physics This dissertation is available at ScholarlyCommons: https://repository.upenn.edu/edissertations/782 IMPROVING SN Ia DISTANCE MEASUREMENTS THROUGH BETTER UNDERSTANDING OF SN Ia SYSTEMATIC UNCERTAINTIES Jennifer Lynn Mosher A DISSERTATION in Physics and Astronomy Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy 2013 Supervisor of Dissertation Masao Sako, Associate Professor of Physics and Astronomy Graduate Group Chairperson A. T. Charlie Johnson, Professor of Physics and Astronomy Dissertation Committee Gary Bernstein, Professor of Physics and Astronomy Mark Devlin, Professor of Physics and Astronomy Ravi Sheth, Professor of Physics and Astronomy Joe Kroll, Professor of Physics and Astronomy Dedication For the family, friends and neighbors who have supported me and mine through the past seven years with kind words, meals, kid-schlepping, babysit- ting, yardwork, and Friday evening drinks around the Woodcrest Ave pic- nic table. I am so lucky to have you. I couldn’t have done this without you. For my husband Michael and son James, who have believed in me and given me strength when confidence has been hard to come by. ii Acknowledgments I would like to thank my advisor, Masao Sako, for his patience, support, and guidance over these past years. I thank the members of the Sloan Digital Sky Survey-II Supernova Survey and the Joint Lightcurve Analysis group for providing essential feedback on my research, editing of my papers, and data products used throughout this thesis. The names of those who have helped are many, but I would like to thank in particular John Marriner, Richard Kessler, and Julien Guy. I have been very fortunate to have my fellow group members Chris D’Andrea, Ravi Gupta, John Fischer, and Rachel Cane with whom to discuss all sorts of supernova related topics, computer issues, and statistical techniques over the final years of my thesis. I thank the graduate students and post-docs I’ve interacted with over the course of my physics career - Michelle Calder, Jessamyn Fairfield, Tsz Yan Lam, Marisa March, Heather Campbell, Ben Dilday, Anjana Shah, Rebecca Surman, Peter Bertone, Carrie Rowland, Vera Hansper, and Diane Markoff. Your friendship, leadership, advice, and support have been invaluable to my growth as a scientist. I’d also like to thank the UNC-Chapel Hill, Emma Willard College, Longwood Uni- versity, and University of Pennsylvania students I’ve taught. I’ve learned as much from you as you’ve learned from me. I thank UNC faculty Christian Iliadis and Hugon Karwowski, Bryn Mawr faculty Peter Beckmann and Neil Abraham, Union College faculty Jonathan Marr and Rebecca iii Koopman, and the NIST Neutron Physics Group for guiding my interests in physics and research. Finally, I thank my committee members, Gary Bernstein, Mark Devlin, Ravi Sheth, and Joe Kroll for reading my dissertation and providing feedback. iv ABSTRACT IMPROVING COSMOLOGICAL DISTANCE MEASUREMENTS THROUGH BETTER UNDERSTANDING OF SN Ia SYSTEMATIC UNCERTAINTIES Jennifer Lynn Mosher Masao Sako Distance measurements using Type Ia Supernovae have enabled the startling discovery that the expansion of the universe is accelerating. To determine the nature and the source of this acceleration, systematic uncertainties on distance measurement must be under- stood. Due to their importance to high-redshift optical SN Ia cosmology and their sen- sitivity to dust and progenitor metallicity effects, rest-frame near-UV (NUV) measure- ments of Type Ia SNe are key to constraining systematic uncertainties. Unfortunately, the calibration and acquisition of this data is challenging. We use direct comparisons of low-redshift SDSS-II and Carnegie Supernova Project NUV SN Ia photometry to quan- tify uncertainties on our ability to calibrate observer frame observations, and find that photometry in this region is consistent at the level of 2% in flux with a 6% scatter about the mean. Monte Carlo simulated SN Ia samples are used to directly measure Hubble Diagram biases resulting from SN Ia model training. Four simulated SN Ia samples are used to train the SALT-II SN Ia model: two width-luminosity adjustments and two intrin- sic scatter models are tested. Adding intrinsic scatter to the training sample yields biased color laws and wavelength-dependent scatters in the NUV region, and causes the color correction parameter b to be systematically underestimated. Assuming a flat LCDM cos- mology and including BAO and CMB constraints, three of our tests correctly recover the Dark Energy equation of state parameter w. The fourth test gives a w offset of 0.02, with a 4-s significance. The software developed to support this work may be adapted to measure Hubble Diagram biases for any combination of SN Ia model and surveys. v Contents Dedication ii Acknowledgments iii Abstract v List of Tables ix List of Figures x 1 Introduction 1 1.1 Distance Measurements and Quantitative Cosmology . .2 1.1.1 Early Distance Measurements . .2 1.1.2 Quantitative Astronomy . .4 1.1.3 A new theory advances cosmology . .5 1.1.4 Hubble and the expanding universe . .7 1.2 Type Ia Supernovae as Standardizeable Candles . .8 1.3 Key Sources of SN Ia systematic uncertainties . 11 1.3.1 Calibration . 13 1.3.2 Dust . 14 1.3.3 Evolution . 15 1.3.4 K-corrections and S-corrections . 19 1.4 SN Ia UV . 19 1.5 Overview . 23 2 Comparison of SDSS-II and CSP SN Ia photometry 24 2.1 Photometry . 27 2.1.1 SDSS-II Supernova Survey . 29 2.1.2 CSP Supernova Program . 30 2.1.3 Calibration Star Comparison . 33 2.1.4 S-correction Procedure . 33 2.1.5 Interpolation . 36 2.1.6 Systematic Uncertainties . 38 vi 2.2 Results . 43 2.3 Discussion and Conclusions . 58 2.3.1 Outlier SNe in gri ......................... 60 2.3.2 Stellar calibration and SN 2005hc . 61 2.3.3 Conclusions . 62 3 Measuring Hubble Diagram biases with synthetic training tests of SALT-II 69 3.1 Introduction . 69 3.2 Training SALT2 . 74 3.2.1 SALT-II model configuration . 74 3.2.2 SALT-II training process . 78 3.2.3 Training Test Overview . 81 3.3 Simulations . 82 3.3.1 The SN Ia Data Samples . 83 3.3.2 SED-based simulations . 84 3.4 SN Ia input models . 87 3.4.1 G10 model . 87 3.4.2 GP model . 87 3.4.3 H model . 88 3.4.4 Intrinsic scatter models . 88 3.4.5 Input model naming conventions . 90 3.5 Analysis . 90 3.5.1 Quantities Derived From Training . 91 3.5.2 Quantities Derived From Light Curve Fitting . 91 3.5.3 Best-Fit Cosmologies . 93 3.6 Redshift-dependent Bias Corrections . 94 3.6.1 Overview of “Malmquist Bias” Correction Techniques . 94 3.6.2 Individual Components of Redshift-dependent Bias . 95 3.6.3 Details of our Bias Correction Method . 97 3.7 Test Case 1: Ideal Training . 97 3.7.1 Training Set Composition . 98 3.7.2 Training Configuration . 99 3.7.3 Test Set Composition . 99 3.7.4 Ideal Training Results .
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