Trash to Treasure: Extracting Cosmological Utility from Sparsely Observed Type Ia Supernovae by Benjamin Ernest Stahl A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Physics in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Alexei V. Filippenko, Co-chair Associate Professor Daniel Kasen, Co-chair Professor Saul Perlmutter Professor UroˇsSeljak Spring 2021 Trash to Treasure: Extracting Cosmological Utility from Sparsely Observed Type Ia Supernovae Copyright 2021 by Benjamin Ernest Stahl 1 Abstract Trash to Treasure: Extracting Cosmological Utility from Sparsely Observed Type Ia Supernovae by Benjamin Ernest Stahl Doctor of Philosophy in Physics University of California, Berkeley Professor Alexei V. Filippenko, Co-chair Associate Professor Daniel Kasen, Co-chair Type Ia supernovae (SNe Ia) are magnificent explosions in the Cosmos that are thought to result from the thermonuclear runaway of white dwarf stars in multistar systems (see, e.g., Jha et al. 2019, for a recent review). Though the exact details of the progenitor system(s) and explosion mechanism(s) remain elusive, SNe Ia have proven themselves to be immensely valuable in shaping our understanding of the physical laws that govern the evolution of the Universe (i.e., physical cosmology). This value is manifested chiefly in two empirical facts: (i) SNe Ia are incredibly luminous (reaching the equivalent of several billion Suns), and (ii) the relatively similar peak luminosities that all \normal" SNe Ia reach can be further homogenized by exploiting a correlation with the rate of photometric evolution (e.g., Phillips 1993). Together, these facts make SNe Ia excellent extragalactic distance indicators, and their use as such led to the discovery of the accelerating expansion of the Universe (Riess et al. 1998; Perlmutter et al. 1999). Through this, the current cosmological paradigm came into favor | the so-called ΛCDM model, where the Universe consists primarily of repulsive dark energy (of which a leading candidate is Einstein's cosmological constant, Λ) and cold dark matter (CDM). In this thesis, I present a comprehensive study that follows the entire SN Ia cosmology life- cyle, from data acquisition to cosmological analysis (albeit of a different flavor than those mentioned above). While these \bookends" provide natural segmentation points in this the- sis, there is a third, intermediate segment which serves to present a complementary method for SN Ia distance measurement that is far less data intensive than conventional approaches. In this way, the segments are hierarchical, each depending on its predecessor and enabling its successor. After appropriately setting the stage in Chapter 1, I delve into the first segment (data 2 acquisition) with Chapter 2, a data release and analysis of 93 multipassband SN Ia light curves collected between 2005 and 2018, and Chapter 3, a complementary release of 637 low- redshift SN Ia optical spectra from a similar time interval. In both, I describe open-source software I developed for data processing and analysis purposes, and make | in addition to the data themselves | useful, value-added data products (e.g., fitted parameters from light curves) available to the community. When combined with prior releases, the Berkeley SN Ia sample now reaches nearly 2000 optical spectra and more than 250 multiband light curves, all observed and processed with the utmost care for quality and internal consistency. This large, homogeneous sample proves critical for the second segment of this thesis, in which I ultimately develop and validate the aforementioned technique | the snapshot distance method (SDM) | for estimating the distance to an SN Ia from sparse observations. As a prerequisite to the SDM, I develop, in Chapter 4, an open-source software package called deepSIP that is capable of determining the phase and light-curve shape of an SN Ia | both of which conventionally require a well-sampled light curve | from a single optical spectrum. At its heart, deepSIP consists of a set of three convolutional neural networks trained on a significant fraction of all publicly available SN Ia optical data (including those presented in the first segment of this thesis), with judicious augmentation steps included to promote telescope agnosticism and model robustness. The impressive performance of deepSIP enables the SDM, which, as I demonstrate in Chapter 5, is capable of deriving an SN Ia distance estimate from as little as one optical spectrum and one epoch of 2+ passband photometry with notable precision over a wide range of SN Ia parameters. This leads, finally, into the last segment of this thesis (cosmological analysis), where I use the SDM to turn trash (i.e., SN Ia observations that were previously unusable owing to data sparsity) into treasure (i.e., reliable distance estimates to be used in a cosmological study). In particular, in Chapter 6, I combine a novel sample of 137 SDM-resurrected SNe Ia with a large literature sample of SNe Ia and SNe II to measure peculiar velocities and set leading (from an SN-only perspective) constraints on the cosmological parameter combination fσ8 and the nature of bulk flows in the local Universe. Moreover, the methods by which I perform this analysis establish a reproducible and extensible blueprint for future such analyses as large-scale surveys come online and unleash an unprecedented data volume. i Dedicated to those across the globe whose lifestyles, livelihoods, and very lives have been disrupted by the COVID-19 pandemic. That this dissertation was completed in the midst of a global crisis is a testament not to myself, but to my privilege. ii Contents Contents ii List of Figures iv List of Tables vi 1 Introduction 1 1.1 Supernovae . 1 1.2 Observables . 4 1.3 Supernova Cosmology . 6 1.4 This Thesis . 7 2 Photometry of 93 SNe Ia 8 2.1 Introduction . 9 2.2 Observations . 10 2.3 Data Reduction . 13 2.4 Results . 22 2.5 Discussion . 30 2.6 Conclusion . 36 2.7 Sample Information . 36 2.8 Light Curves . 39 3 Spectroscopy of 247 SNe Ia 50 3.1 Introduction . 51 3.2 Data . 52 3.3 Classification . 56 3.4 Results . 63 3.5 Conclusion . 76 3.6 Sample Information . 77 4 deepSIP 81 4.1 Introduction . 82 4.2 Data . 83 iii 4.3 Models . 94 4.4 Results . 98 4.5 Conclusion . 107 4.6 Supplementary Light Curves . 109 4.7 Light-Curve Fitting . 111 4.8 Usage . 114 5 SN Ia Snapshot Distances 115 5.1 Introduction . 116 5.2 The Snapshot Distance Method . 117 5.3 Validating the Snapshot Distance Method . 120 5.4 Discussion . 128 6 Peculiar-velocity Cosmology with Supernovae 132 6.1 Introduction . 133 6.2 Data . 134 6.3 Method . 141 6.4 Results . 145 6.5 Conclusion . 152 6.6 Snapshot Distance Sample Selection . 153 7 Conclusion 157 Bibliography 160 iv List of Figures 2.1 Transmission curves . 12 2.2 Galaxy subtraction example . 15 2.3 Uncertainty distributions . 18 2.4 Nickel2 colour terms as a function of time . 20 2.5 Nickel2 atmospheric correction terms as a function of time . 21 2.6 KAIT4 − Nickel2 calibration star residual distributions . 23 2.7 Distributions of dataset parameters . 25 2.8 Number of photometry epochs vs. average cadence . 26 2.9 Distributions of ∆m15 and E(B − V )host ...................... 31 2.10 ∆m15(B) vs. ∆m15 .................................. 33 2.11 Distributions of ∆ and AV .............................. 34 2.12 Comparison of light-curve fitter results . 35 2.13 Observed BVRI and unfiltered light curves . 40 3.1 Low, medium, and high SNR SN Ia spectra . 54 3.2 SNID-determined redshifts versus host-galaxy redshifts . 60 3.3 SNID-determined phases versus those derived from light-curve maxima . 62 3.4 Distributions of SN-level parameters . 64 3.5 Distributions of spectrum-level parameters . 65 3.6 Example of spectral processing with respext ................... 67 3.7 Evolution of pseudo-equivalent widths . 70 3.8 Evolution of expansion velocities . 72 3.9 Measured velocity shifts from nebular spectra . 74 4.1 Cuts made to obtain final compilation . 85 4.2 Dataset parameter distributions . 86 4.3 Distribution of ∆m15 and phase for the spectra in our compilation . 88 4.4 Sequences of variance spectra binned by phase or ∆m15 . 90 4.5 Example of spectral preprocessing procedure and data augmentation strategy . 93 4.6 Neural network architecture used by deepSIP models . 96 4.7 Validation ROC curve for Model I . 100 4.8 Validation RMSE and mean predicted uncertainty values for Models II and III . 104 v 4.9 Phase and ∆m15 predictions . 105 4.10 Estimated uncertainties versus predicted labels . 108 4.11 Observed light curves for previously unpublished SNe Ia . 111 5.1 Schematic representation of the snapshot distance method . 118 5.2 Comparison of SDM-derived distance moduli to SNooPy reference values . 123 5.3 Mean residuals as a function of passband combination and photometric epoch . 124 5.4 Residuals as a function of photometric and spectroscopic properties . 125 5.5 One- and two-dimensional projections of parameter residuals . 127 6.1 Cuts made in selecting the OSC subcatalogues . 137 6.2 Hubble diagram and redshift distribution . 140 6.3 On-the-sky distribution of sample . 141 6.4 σint posterior distributions . 146 6.5 Bulk-flow amplitude . 149 6.6 S8 comparison . 150 vi List of Tables 2.1 Colour Terms . 14 2.2 Photometry . 24 2.3 SN Ia sample . 37 2.4 Light-curve properties . ..