Measuring and Extrapolating the Chemical Abundances of Normal and Superluminous Core-Collapse Supernovae

Dissertation

Presented in Partial Fulﬁllment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Rebecca A. Stoll

Graduate Program in Astronomy

The Ohio State University 2013

Dissertation Committee: Professor Richard W. Pogge, Advisor Professor Paul Martini Professor Todd A. Thompson Copyright by

Rebecca A. Stoll

2013 Abstract

We present All-Sky Automated Survey data starting 25 days before the discovery of the recent type IIn SN 2010jl, and we compare its light curve to other luminous IIn SNe, showing that it is a luminous (MI 20.5) event. Its host galaxy, ≈ − UGC 5189, has a low gas-phase oxygen abundance (12+log(O/H) = 8.2 0.1), which ± reinforces the emerging trend that over-luminous core-collapse supernovae are found in the low-metallicity tail of the galaxy distribution, similar to the known trend for the hosts of long GRBs. We compile oxygen abundances from the literature and from our own observations of UGC 5189, and we present an unpublished spectrum of the luminous type Ic SN 2010gx that we use to estimate its host metallicity. We discuss these in the context of host metallicity trends for diﬀerent classes of core-collapse objects. The earliest generations of stars are known to be enhanced in [O/Fe] relative to the Solar mixture; it is therefore likely that the stellar progenitors of these overluminous supernovae are even more iron-poor than they are oxygen-poor.

A number of mechanisms and massive star progenitor systems have been proposed to explain the most luminous core-collapse supernovae. Any successful theory that tries to explain these very luminous events will need to include the emerging trend that points towards low-metallicity for the massive progenitor stars. This trend for

ii very luminous supernovae to strongly prefer low-metallicity galaxies should be taken into account when considering various aspects of the evolution of the metal-poor early universe, such as enrichment and reionization.

Type II SNe can be used as a star formation tracer to probe the metallicity distribution of global low-redshift star formation. We present oxygen and iron abundance distributions of type II supernova progenitor regions that avoid many previous sources of bias. Because iron abundance, rather than oxygen abundance, is of key importance for the late stage evolution of the massive stars that are the progenitors of core-collapse supernovae, and because iron enrichment lags oxygen enrichment, we ﬁnd a general conversion from oxygen abundance to iron abundance.

The distributions we present here are the best yet observational standard of comparison for evaluating how diﬀerent classes of supernovae depend on progenitor metallicity. We spectroscopically measure the gas-phase oxygen abundance near a representative subsample of the hosts of type II supernovae from the ﬁrst-year

Palomar Transient Factory (PTF) supernova search. The median metallicity is

12+log(O/H) = 8.65 and the median host galaxy stellar mass from ﬁts to SDSS

9.9 photometry is 10 M⊙. They do not show a systematic oﬀset in metallicity or mass from a redshift-matched sample of the MPA/JHU value-added catalog. In contrast to previous supernova host metallicity studies, this sample is drawn from a single survey. It is also drawn from an areal rather than a targeted survey, so supernovae in the lowest-mass galaxies are not excluded. Indeed, the PTF supernova search has

iii a slight bias towards following up transients in low mass galaxies. The progenitor region metallicity distribution we find is statistically indistinguishable from the metallicity distribution of type II supernova hosts found by targeted surveys and by samples from multiple surveys with different selection functions. Using the relationship between iron and oxygen abundances found for Milky Way disk, bulge, and halo stars, we translate our distribution of type II SN environments as a function of oxygen abundance into an estimate of the iron abundance, since iron varies more steeply than oxygen. We find that though this sample spans only 0.65 dex in oxygen abundance, the gap between the iron and oxygen abundance is 50% wider at the low-metallicity end of our sample than at the high-metallicity end.

We compare the host metallicity distribution of core-collapse supernovae

(CCSNe) with peak absolute magnitudes brighter than 20 with the host metallicity − distribution of type II SNe from the Palomar Transient Factory ﬁrst-year sample.

We ﬁnd that luminous CCSNe come from much more metal-poor environments than normal type II SNe; the K-S probability of the two samples being drawn from the same underlying oxygen abundance distribution is only 0.04%. Iron is a key source of opacity for massive star winds, and at lower metallicity, α-elements like oxygen are enhanced relative to iron compared to the solar mixture. The progenitors of luminous CCSNe are much poorer in iron than the progenitors of normal type II

SNe, suggesting mass loss is a key factor for abnormally high luminosity.

iv Dedication

Dedicated to the memories of Kay Stoll and Kendra Lipinski.

The world is poorer without you, Wanderer and Wonderer.

v Acknowledgments

Thanks to Rick Pogge for broad instrumentation trouble-shooting experience, for mentorship, for wide-ranging conversations on history, geography, politics, and literature, and for consistently making one want to strive to be a better scientist.

Thanks to Paul Martini for giving us practice every week reading papers eﬃciently and thoroughly, talking about them cogently and concisely, and deconstructing them or haring away on interesting tangents. Thanks to Paul, Rick, Mark Derwent, Dan

Pappalardo, and Tom O’Brien for taking on an instrumentation newbie and sending her on as an old hand. Thanks to Kim McLeod for the introduction to research and for insightful mentorship, even more insightful than I at the time realized. Thanks to Richard French and Mike Brotherton, for valuable mentorship and for extending opportunities when critical. Thanks to Richard C. Henry for demonstrating that astronomy was physics and therefore interesting rather than just pretty. Thanks to

Kris Stanek for lessons in the cost of standing by what I know to be true. Thanks to

Todd Thompson for distilling the complicated alchemy that is daily coffee into a set of clear and usually achievable guidelines, and for being one of the members of the department who most consistently brings the glee to discussions of difficult scientific problems. Thanks to Barbara Ryden for deliciously sardonic wit, history of science,

vi and patiently putting up with burbling about Shakespeare or about crazy schemes for recruiting undergrads. Thanks to Andy Gould for statistical mental shortcuts, for putting us in the habit of looking at things sideways and upside down, and for a unique view of world events.

Thanks to Virginia Stoll, for all which cannot be summarized, and to Carine,

Amber, Sam, Kaelin, Ann, Samantha, Bethany, Sarah, Lisa, Kate, and Sondy. To the grad students and the postdocs and the guys in the lab.

Many thanks to my family for everything, the smallest but latest example of which is excellent proofreading—just in time for me to correct the proofs of my latest journal article as well as the dissertation itself! You caught a number of important things the professional copy editor missed.

And thanks to Mrs. Ross, partly for Hamlet and the Scottish play, but mostly because a sixth grader has no context for aptitude test scores, and doesn’t know she should advocate for herself with the school district; being put in ‘advanced math’ sounded advanced enough to an eleven-year-old. Algebra was vastly more interesting than patiently sitting through stultifyingly boring fractions again, and geometry at the high school led to free time in the middle-school library to read all the relativity books, which nurtured the interest in physics (and Orwell, which nurtured the interest in dystopia, but that’s another story entirely.) All the research shows that elementary school teachers can be key to students’ future success in (or fear of)

vii mathematics, but this was really above and beyond the call of duty. Sometimes you don’t realize someone has changed your life until decades later.

viii Vita

September 22, 1982 ...... Born – Santa Clara, CA, USA

2006 ...... A.B. Physics, Wellesley College

2006 – 2007 ...... Research Scientist, University of Wyoming

2007 – 2008 ...... Graduate Teaching Associate, The Ohio State University

2008 – 2010 ...... Graduate Research Associate, The Ohio State University

2010 – 2011 ...... David G. Price Fellow in Astronomical Instrumentation, The Ohio State University

2011 – 2012 ...... Graduate Research Associate, The Ohio State University

2012 – 2013 ...... David G. Price Fellow in Astronomical Instrumentation, The Ohio State University

Publications

Research Publications

1. R. Stoll, P. Martini, M. A. Derwent, R. Gonzalez, T. P. O’Brien, D. P. Pappalardo, R. W. Pogge, M.-H. Wong, and R. Zhelem, “Mechanisms and instrument electronics for the Ohio State Multi-Object Spectrograph (OSMOS)”, Proc. SPIE, 7735, 154, (2010).

ix 2. R. Khan, J. L. Prieto, G. Pojma´nski, K. Z. Stanek, J. F. Beacom, D. M. Szczygie l, B. Pilecki, K. Mogren, J. D. Eastman, P. Martini, and R. Stoll, “Pre-discovery and Follow-up Observations of the Nearby SN 2009nr: Implications for Prompt Type Ia Supernovae”, ApJ, 726, 106, (2011).

3. P. Martini, R. Stoll, M. A. Derwent, R. Zhelem, B. Atwood, R. Gonzalez, J. A. Mason, T. P. O’Brien, D. P. Pappalardo, R. W. Pogge, B. Ward, and M.-H. Wong, “The Ohio State Multi-Object Spectrograph”, PASP, 123, 187, (2011).

4. R. Stoll, J. L. Prieto, K. Z. Stanek, R. W. Pogge, D. M. Szczygie l, G. Pojma´nski, J. Antognini, and H. Yan, “SN 2010jl in UGC 5189: Yet Another Luminous Type IIn Supernova in a Metal-poor Galaxy”, ApJ, 730, 34, (2011).

5. R. Khan, K. Z. Stanek, R. Stoll, and J. L. Prieto, “Super-Chandrasekhar SNe Ia Strongly Prefer Metal-poor Environments”, ApJ, 737, 24, (2011).

6. S. L. Cales, M. S. Brotherton, Z. Shang, V. N. Bennert, G. Canalizo, R. Stoll, R. Ganguly, D. Vanden Berk, C. Paul, and A. Diamond-Stanic, “Hubble Space Telescope Imaging of Post-starburst Quasars”, ApJ, 741, 106, (2011).

7. P. W. A. Roming, T. A. Pritchard, J. L. Prieto, C. S. Kochanek, C. L. Fryer, K. Davidson, R. M. Humphreys, A. J. Bayless, J. F. Beacom, P. J. Brown, S. T. Holland, S. Immler, N. P. M. Kuin, S. R. Oates, R. W. Pogge, G. Pojmanski, R. Stoll, B. J. Shappee, K. Z. Stanek, and D. M. Szczgiel, “The Unusual Temporal and Spectral Evolution of the Type IIn Supernova 2011ht”, ApJ, 751, 92, (2012).

8. R. W. Pogge, B. Atwood, T. P. O’Brien, P. L. Byard, M. A. Derwent, R. Gonzalez, P. Martini, J. A. Mason, P. S. Osmir, D. P. Pappalardo, R. Zhelem, R. A. Stoll, D. P. Steinbrecher, D. F. Brewer, C. Colarosa, and E. J. Teiga, “On-Sky Performance of the Multi-Object Double Spectrograph for the Large Binocular Telescope”, Proc. SPIE, 8446, 0, (2012).

9. S. L. Cales, M. S. Brotherton, Z. Shang, J. C. Runnoe, M. A. DiPompeo, V. N. Bennert, G. Canalizo, K. D. Hiner, R. Stoll, R. Ganguly, and A. Diamond- Stanic, “The Properties of Post-starburst Quasars Based on Optical Spectroscopy”, ApJ, 762, 90, (2013).

Fields of Study

Major Field: Astronomy

x Table of Contents

Abstract ...... ii

Dedication ...... v

Acknowledgments ...... vi

Vita ...... ix

List of Tables ...... xiv

List of Figures ...... xvi

Chapter 1: Introduction ...... 1

1.1 RelationtoPreviousWork ...... 3

1.2 ScopeoftheDissertation...... 8

Chapter 2: SN 2010jl in UGC 5189: Yet Another Luminous Type IIn Supernova in a Metal-Poor Galaxy ...... 10

2.1 Introduction...... 10

2.2 Observations...... 13

2.2.1 Photometry ...... 13

2.2.2 Spectroscopy ...... 14

2.3 Resultsandanalysis ...... 16

2.3.1 SN 2010jl light curve ...... 16

2.3.2 Metallicity near SN 2010jl ...... 17

2.3.3 Metallicity elsewhere in the host galaxy ...... 19

2.3.4 Host metallicity trends ...... 20

xi 2.4 Discussion...... 24

2.4.1 Luminoussupernovae...... 27

2.4.2 Metallicity diagnostics ...... 30

2.5 Conclusions ...... 32

Chapter 3: Probing the low-redshift star formation rate as a function of metallicity through the local environments of type II supernovae ...... 43

3.1 Introduction...... 43

3.2 Spectroscopic observations and analysis ...... 51

3.3 Characterizing the spectroscopic subsample ...... 55

3.3.1 Extracting ﬂuxes from SDSS imaging ...... 55

3.3.2 Galaxyproperties...... 57

3.4 ResultsandDiscussion ...... 58

3.4.1 How representative is the metallicity sample? ...... 59

3.4.2 The type II metallicity sample in context ...... 60

3.4.3 Comparing with other SNe host samples ...... 63

3.4.4 Comparing with SFR metallicity distributions from galaxy populationstatistics ...... 65

3.4.5 Selectioneﬀects ...... 67

3.4.6 Strong-line metallicity diagnostics ...... 69

3.4.7 Ironabundances ...... 71

3.5 Conclusions ...... 76

Chapter 4: Abnormally luminous core-collapse supernovae are more metal-poor than normal type II supernovae ...... 111

4.1 Introduction...... 111

4.2 DataandAnalysis ...... 113

4.2.1 Oxygen abundance determination ...... 114

xii 4.2.2 Comparisonsample...... 115

4.3 Results...... 116

4.4 Discussion...... 116

4.4.1 Diﬀerent standards of comparison ...... 118

4.4.2 What counts as a super-luminous supernova? ...... 119

4.5 Conclusions ...... 120

References ...... 128

Appendix A: Metallicity conversions ...... 136

xiii List of Tables

2.1 OpticalphotometryofSN2010jl...... 40

2.2 List of luminous CCSNe collected from the literature ...... 41

2.2 List of luminous CCSNe collected from the literature (cont’d) . . . . 42

3.1 Observationproperties ...... 91

3.2 Measuredhostmetallicities...... 92

3.2 Measured host metallicities (cont’d) ...... 93

3.2 Measured host metallicities (cont’d) ...... 94

3.2 Measured host metallicities (cont’d) ...... 95

3.3 Measured line ﬂuxes and metallicities at non-supernova locations withinthegalaxy ...... 96

3.4 Host properties measured from SDSS photometry ...... 97

3.4 Host properties measured from SDSS photometry (cont’d) ...... 98

3.4 Host properties measured from SDSS photometry (cont’d) ...... 99

3.4 Host properties measured from SDSS photometry (cont’d) ...... 100

3.4 Host properties measured from SDSS photometry (cont’d) ...... 101

3.5 Host properties derived from SDSS photometry ...... 102

3.5 Host properties derived from SDSS photometry (cont’d) ...... 103

3.5 Host properties derived from SDSS photometry (cont’d) ...... 104

3.5 Host properties derived from SDSS photometry (cont’d) ...... 105

3.5 Host properties derived from SDSS photometry (cont’d) ...... 106

xiv 3.6 Host galaxy properties ﬁt from SDSS photometry ...... 107

3.6 Host galaxy properties ﬁt from SDSS photometry (cont’d) ...... 108

3.6 Host galaxy properties ﬁt from SDSS photometry (cont’d) ...... 109

3.7 Cuts to MPA/JHU comparison sample ...... 110

4.1 Measuredhostmetallicities...... 126

4.2 Luminous CCSN host metallicities collected from the literature. . . . 127

xv List of Figures

2.1 Host galaxy of SN 2010jl and positions of spectra ...... 33

2.2 OSMOS spectra of SN 2010jl and surrounding emission line regions . 34

2.3 Spectrum of SN 2010gx with du Pont 2.5-m telescope ...... 35

2.4 Multiwavelength light curve of SN 2010jl from ASAS and Swope . . . 36

2.5 Host metallicities of luminous SNe compared with regular galaxies . . 37

2.6 Host metallicity comparison scaled to diﬀerent diagnostics ...... 38

2.7 Comparing metallicities of luminous and normal SNe ...... 39

3.1 Example type II SN hosts arranged by mass and metallicity . . . . . 79

3.2 Type II SN host metallicity distributions in various diagnostics . . . . 80

3.3 Comparison of star formation rate estimation methods ...... 81

3.4 The type II metallicity sample is representative of the entire ﬁrst-year PTFtypeIIsampleinmassandagedistributions...... 82

3.5 The type II metallicity sample is representative of the entire ﬁrst-year PTFtypeIIsampleinSFRdistribution...... 82

3.6 Type II host metallicity distribution has no strong dependence on redshift 83

3.7 Type II host sample is oﬀset slightly to higher SFR from SDSS MPA/JHUgalaxysample ...... 84

3.8 PTF type II sample and our type II metallicity sample consistent with SDSS MPA/JHU galaxy sample in mass and SFR ...... 85

3.9 Type II host metallicity distribution not substantially diﬀerent from distributionsfromheterogeneoussamples ...... 86

xvi 3.10 Type II host metallicity distribution compared with metallicity distributionofstarformation ...... 87

3.11 Metallicity and host mass distributions of type II SNe contrasted with othertypesofcore-collapseSNe ...... 88

3.12 Iron abundance is low compared with oxygen abundance at low metallicity...... 89

3.13 The most oxygen-poor SN hosts are even more iron-poor ...... 90

4.1 Five MODS spectra of luminous CCSNe and their host galaxies . . . 122

4.2 Three MODS spectra of luminous CCSN host galaxies ...... 123

4.3 Contrasting the metallicity of luminous and normal CCSNe ...... 124

4.4 Contrasting the oxygen and iron distributions of luminous CCSNe . . 125

A.1 Avoiding double-valued metallicity conversions ...... 139

xvii Chapter 1: Introduction

Core-collapse supernovae (CCSNe) are the ﬁnal stages of massive stars. Nuclear burning is less and less eﬃcient at creating energy the farther it progresses and the heavier the atoms that are fusing, so as a massive star uses up light elements in its core, less and less energy is produced. The star becomes less capable of supporting itself against gravity, and the core becomes denser and hotter, fusing heavier atoms.

This leads to a crisis which ends in the collapse of the core, emitting an enormous outburst of neutrinos. The outer layers of the star are thrown oﬀ at great speeds.

The exact details of how the outer layers are launched are an active area of research, and must depend not only on the energy source and coupling eﬃciency but on the structure of the outer layers of the doomed star.

Discovering what properties of a massive star dictate its ﬁnal fate is a problem that can be attacked in several ways. In this dissertation, I examine circumstantial evidence about the surroundings after a supernova happens, in order to constrain properties of the progenitor star.

Other methods for attacking this fundamental question abound. Theoretical studies combine known physics and sometimes massive computational resources to

1 predict a particular star’s death throes from ﬁrst principles. Searches in archival data for the progenitor star after a supernova has exploded can yield very speciﬁc information on the star in question. All are useful and complementary.

The particular beneﬁt of studying the progenitor regions is that it can be done years after the supernova has already faded. Indeed, sometimes it must be done years after the supernova has faded, so that the light from surrounding starforming regions with similar properties is not drowned out by the supernova itself.

In this dissertation, I concentrate on studies of emission line regions, hot gas in star-forming regions that emits at certain speciﬁc wavelengths. The relative strength of these emission lines can give us rich information on the chemical composition and radiative environment of the gas.

Core collapse supernovae form from massive stars, which burn fast and die young. They are nearly always found near starforming regions, enabling all the emission line science that is particularly suited to the medium resolution spectrographs OSU has been building over the past decade. This dissertation contains some of the earliest data from OSMOS, the Ohio State Multi-Object

Spectrograph (Martini et al. 2011; Stoll et al. 2010), and MODS1, the ﬁrst of two

2 Multi-Object Double Spectrographs (Pogge et al. 2010) built for the Large Binocular

Telescope.

1.1. Relation to Previous Work

The question of how certain rare classes of supernovae (SNe) depend on progenitor metallicity has been limited by the lack of an unbiased standard of comparison. It is incorrect to use the metallicity distribution of galaxies for this because CCSNe trace star formation rather than stellar mass. Existing supernova host galaxy metallicity distributions are selected to compare matched samples. They use supernova surveys that do not search in the faintest hosts, and are not likely to be suitable as a standard of comparison for rare events that appear to be more common in low-mass galaxies. We address this by measuring a metallicity distribution of type II supernova progenitor regions from a single source survey that is areal rather than targeted.

The observational properties of CCSNe span a broad range of spectral types, luminosities, apparent kinetic energies, and other properties. Interpreting this diversity remains a fundamental theoretical and observational challenge, particularly as to how diﬀerences in the stellar progenitors of the SNe are related to the explosions. For example, the relative fractions of hydrogen-rich type II SNe and hydrogen-poor type Ib/c SNe can be predicted as a function of metallicity based on models for mass-loss from the progenitor stars (Eldridge et al. 2008; Georgy et al.

2009, 2012). Standard mass loss models for massive stars are based on line-driven

3 winds (e.g. Kudritzki & Puls 2000); the eﬃciency of these winds depends on metallicity because metals, particularly iron, dominate the line opacities driving the winds (Vink & de Koter 2005). Furthermore, the explosion energy of normal type II

SNe may depend on the progenitor metallicity (Kasen & Woosley 2009), and certain types of SNe may occur only for low-metallicity progenitor stars (Ober et al. 1983;

Heger & Woosley 2002; Langer et al. 2007).

A serendipitous observation of a SN progenitor star prior to explosion is one useful way to characterize the progenitor and determine how the SN properties depend on the progenitor properties. Unfortunately, observing or strongly constraining the properties of the progenitor star is only possible for SNe in nearby galaxies, leading to very small samples (e.g. Smartt 2009, and references therein).

This limits the utility of this technique for constraining the properties of subclasses of events that are rare and therefore observed mostly at large distances, such as extremely optically luminous CCSNe.

Observational techniques for addressing this question without pre-explosion data involve estimating the progenitor properties from the environments that remain behind. These techniques range from estimating progenitor age and mass from the degree of correlation with Hα (Anderson & James 2008) and NUV (Anderson et al. 2012) emission, to detailed characterization of resolved stellar populations near supernova explosion sites in nearby galaxies (Badenes et al. 2009; Murphy et al. 2011) to extrapolation of stellar population properties from galaxy photometry

4 and spectroscopic observations (e.g. Kelly et al. 2008; Arcavi et al. 2010; Kelly &

Kirshner 2012).

Spectroscopic observations of H II regions near a supernova can be used to estimate the metallicity using strong-line oxygen abundance indicators. There are precision and accuracy limitations to strong-line abundance estimates, but more rigorous abundance measurements using faint auroral lines are too costly (or completely unfeasible) for statistical surveys of SN host metallicities.

The frequency of several classes of CCSN types has been found to vary with host metallicity. Prantzos & Boissier (2003) found that the ratio between type Ib/c and type II SNe increases with increasing host luminosity, and because of the galaxy luminosity/metallicity relationship, they suggested this was probably a metallicity eﬀect. This was conﬁrmed and expanded upon by Prieto et al. (2008) in a subsequent study, which found that hosts of type Ib/c SNe are higher metallicity than hosts of type II and type Ia SNe based on spectroscopically measured metallicity. Stanek et al. (2006) found that type Ic SNe associated with nearby long gamma-ray bursts

(GRBs) were in faint, metal-poor galaxies, proposing a progenitor metallicity cut-oﬀ above which GRBs do not occur, and Modjaz et al. (2008) found that type Ic SNe that were associated with GRBs were in more metal-poor regions than those that were not. Anderson et al. (2010) found at marginal signiﬁcance that type Ib and type Ic SNe hosts have slightly higher metallicity than hosts of type II and type Ia

SNe. The metallicity local to type Ic SNe without broad lines was found by Modjaz

5 et al. (2011) to be on average higher than near type Ib SNe, regardless of which strong-line metallicity diagnostic was used, consistent with the results of Kelly &

Kirshner (2012), though Anderson et al. (2010) and Leloudas et al. (2011) found the diﬀerence to not be statistically signiﬁcant. Kelly & Kirshner (2012) found that although hosts of type Ib and type Ic SNe are higher in metallicity than hosts of type II SNe, hosts of broad-lined type Ic (Ic-BL) SNe are lower in metallicity.

CCSNe which are abnormally optically luminous appear to occur more often in low-mass, low-luminosity galaxies (Neill et al. 2011). Spectroscopic abundance measurements of hosts of ﬁve such luminous CCSNe indicate this is likely due to metallicity, as shown by Stoll et al. (2011) (including data from Young et al. 2010;

Koz lowski et al. 2010). These abnormally luminous CCSNe are predominantly

SNe Ic or IIn. Subsequent spectroscopic measurements of the host of the luminous

SN 2008am (Chatzopoulos et al. 2011) and the luminous SN 2010ay (Prieto &

Filippenko 2010; Modjaz et al. 2010; Sanders et al. 2012), and photometric limits on the hosts of the luminous SNe PS1-10awh and PS1-10ky (Chomiuk et al. 2011) reinforce this conclusion.

Many studies (e.g. Neill et al. 2009; Sanders et al. 2012; Stoll et al. 2011;

6 luminosity. Supernovae trace star formation rather than overall stellar mass, and star formation is not evenly distributed among galaxies of a given mass. Recent results (Lara-L´opez et al. 2010; Mannucci et al. 2010) show that the scatter in the galaxy mass-metallicity relationship may be reduced by considering star formation as a third parameter, and that star formation rates (SFRs) are higher in lower metallicity galaxies at a given mass (but see also Yates et al. 2012, which ﬁnds that the form of any such relation between mass, metallicity, and star formation rate depends on the strong-line metallicity diagnostic used).

Levesque et al. (2010b) and Han et al. (2010) suggested that instead of a metallicity cutoﬀ for GRBs there is a separate luminosity-metallicity relationship for

GRB host galaxies offset to lower metallicity than the normal galaxy mass-metallicity relationship of Tremonti et al. (2004). (These results are consistent with a simple metallicity cutoff with the exception of a single high-mass GRB host galaxy that appears to be high metallicity.) No such offset has been observed for type Ia SNe

(Neill et al. 2009). This question of an oﬀset in the mass-metallicity relationship has confused many subsequent discussions of a metallicity cutoﬀ.

The purpose of this dissertation is to clarify this issue and determine whether luminous CCSNe are indeed metal-poor when compared with overall star formation, as traced by type II SNe.

7 1.2. Scope of the Dissertation

In Chapter 2 I present spectroscopic abundance measurements of the hosts of two abnormally luminous CCSNe, and show that, together with three other measurements from the literature, a strong pattern emerges of luminous SNe occurring in host galaxies that are metal-poor compared to the overall galaxy population.

In Chapter 3 I present metallicity measurements, host photometry, and best-ﬁt galaxy properties for a representative subsample of the Palomar Transient Factory’s

ﬁrst-year type II SN sample. The purpose of the in-depth study of this sample is to serve as a reasonable standard of comparison for rare SN events. The secondary aim is to continue to press the discussion in the community about both the usefulness and the limitations of comparing SN hosts to samples of galaxies. I also present a conversion from gas-phase oxygen abundance measurements to implied iron abundances, and discuss the logic, uses, and limitations of this conversion.

In Chapter 4 I present metallicity measurements for a sample of eight hosts of abnormally luminous core-collapse supernovae. Comparing them and four measurements from the literature, I ﬁnd that that luminous CCSNe come from much more metal-poor environments than normal type II SNe. This implies that the progenitors of luminous CCSNe are much poorer in iron than the progenitors

8 of normal type II SNe, suggesting mass loss is a key factor for abnormally high luminosity.

Chapter 2 contains material drawn from a study published as R. Stoll,

J. L. Prieto, K. Z. Stanek, R. W. Pogge, D. M. Szczygie l, G. Pojma`nski,

J. Antognini, and H. Yan, 2011, ApJ, 730, 34. Chapter 3 contains material drawn from a study accepted for publication in ApJ by R. Stoll, J. L. Prieto, K. Z. Stanek, and R. W. Pogge. Chapter 4 contains material from an as-yet unpublished study done by R. Stoll, R. W. Pogge, J. L. Prieto, K. Z. Stanek, and K. V. Croxall.

9 Chapter 2: SN 2010jl in UGC 5189: Yet Another Luminous Type IIn Supernova in a Metal-Poor Galaxy

2.1. Introduction

The bright SN 2010jl, discovered on UTC 2010 November 3.5 (Newton & Puckett

2010) was classiﬁed as a type IIn supernova (Benetti et al. 2010; Yamanaka et al.

2010). A possible luminous blue progenitor has been identiﬁed in archival HST

WFPC2 data (Smith et al. 2011b). If this detection is of a massive compact cluster, the turnoﬀ mass is constrained to be > 30 M⊙. If it is a single star, it is either a massive η Carinae-like star or something fainter that has been caught in an LBV-like eruption phase, possibly a precursor explosion. Spectropolarimetric observations indicate possible asymmetry in the explosion geometry and limit the amount of dust in the progenitor environment (Patat et al. 2011).

Prieto & Stanek (2010) point out that the supernova’s host galaxy, UGC 5189, has been shown to be metal-poor based on a spectrum obtained by the SDSS survey a few arcsec away from the site of the supernova. It is included in the Tremonti et al. (2004) DR4 catalog of galaxy metallicities, which estimates

12+log(O/H) = 8.15 0.1 dex based on strong recombination and forbidden emission ± 10 lines in the spectrum. Pilyugin & Thuan (2007) estimate 12 + log(O/H) = 8.3 dex from the same spectrum based on the “direct” photoionization-based abundance method using an estimate of the electron temperature (Te) from the [O III]λ4363A˚ auroral line.

Throughout this paper, as above, we will adopt O/H as a proxy for the

“metallicity” of the host galaxy, because only gas-phase oxygen abundance estimates are available. In 2.4 we will discuss how this relates to total metal abundance and § iron abundances.

There is mounting evidence showing that the majority of the most optically luminous supernovae explode in low-luminosity, star-forming galaxies, which are likely to be metal-poor environments. Circumstantial evidence comes from the fact that most of these objects have been discovered in new rolling searches that are not targeted to bright galaxies, even though they were bright enough to be discovered in galaxy-targeted searches (the only exception is the recently discovered

SN 2010jl). Some of the rolling searches that are discovering the most energetic supernovae include the Texas Supernova Search (TSS; Quimby 2006), the Catalina

Real-Time Transient Survey (CRTS; Drake et al. 2009), the Palomar Transient

Factory (PTF; Rau et al. 2009), and the Panoramic Survey Telescope & Rapid

Response System (Pan-STARRS). Neill et al. (2011) ﬁnd that luminous SNe occur predominantly in the faintest, bluest galaxies. Li et al. (2011) ﬁnd that SNe IIn may preferentially occur in smaller, less-luminous, later-type galaxies than SNe II-P. The

11 mass–metallicity relationship implies that such small, faint galaxies should tend to be low-metallicity (Tremonti et al. 2004).

Koz lowski et al. (2010) presented the ﬁrst host galaxy luminosity vs. oxygen abundance diagram with a small sample of three energetic type IIn and type Ic core-collapse supernovae (CCSNe) compiled from published work in the literature

(two objects) and new data for SN 2007va. These initial results suggest that the host galaxy environments of the most energetic CCSNe are on average metal-poor

(metallicities 0.2 0.5 Z⊙) compared to the bulk of star-forming galaxies in SDSS, ∼ − and are similar in metallicity to the hosts of local GRBs (Stanek et al. 2006). It has now been well-established that long GRBs are accompanied by broad-line type

Ic SNe (e.g. Stanek et al. 2003), ﬁrmly connecting these very energetic explosions to the deaths of massive stars. In contrast, luminous type IIn SNe are not shown to be connected with local GRBs (although see Germany et al. 2000; Rigon et al.

2003). The sample of luminous supernovae with measured abundances is small and incomplete because only some of the brightest host galaxies have been targeted, which hinders the interpretation and comparison of the results.

In this paper we expand this sample with metallicities of the hosts of SN 2010gx and SN 2010jl and put all metallicity measurements on a common scale to conﬁrm the emerging trend that these optically luminous core-collapse events appear to occur in low-metallicity or low-luminosity hosts.

12 2.2. Observations

2.2.1. Photometry

Photometric data were obtained with the 10-cm All-Sky Automated Survey (ASAS)

North telescope in Hawaii (Pojmanski 2002; Pigulski et al. 2009). These data were processed with the reduction pipeline described in detail in Pojmanski (1998, 2002).

The V magnitudes are tied to the Johnson V scale using Tycho (Høg et al. 2000) and Landolt (Landolt 1983) standard stars. The I magnitudes were calibrated using transformed SDSS DR7 (Abazajian et al. 2009) magnitudes of standards in the ﬁeld.

The magnitudes were measured using aperture photometry with a 2 pixel (30 arcsec) aperture radius. We subtract the contribution from the host galaxy measuring the median magnitude of the host in the same aperture using all archival ASAS images before the supernova, which give Vhost = 14.97 mag and Ihost = 14.28 mag.

We obtained UBV RI images of SN 2010jl with the SITe-3 CCD camera mounted on the Swope 1-m telescope at Las Campanas Observatory on three consecutive nights from UT November 15 17 2010. The images were reduced with − standard tasks in the IRAF1 ccdproc package. The data were flat-fielded using domeflats obtained for BV RI images and twilight skyflats for U-band images. We

1IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the

Association of Universities for Research in Astronomy (AURA) under cooperative agreement with the National Science Foundation.

13 applied a linearity correction using the prescription and coefficients from Hamuy et al. (2006). The photometry of the supernova was obtained using aperture photometry and a 2′′ radius aperture with the phot task in IRAF. The magnitudes were calibrated using field stars with SDSS DR7 ugriz photometry, converted to standard UBV RI magnitudes through standard equations on the SDSS website. We subtracted the contribution from the host galaxy fluxes measuring the magnitudes in the same aperture using the pre-explosion SDSS images. In Table 2.1 we gather all optical photometry presented in this paper.

2.2.2. Spectroscopy

Spectra were obtained on November 5 and November 11 2010 with the Ohio State

Multi-Object Spectrograph (OSMOS, Martini et al. 2011; Stoll et al. 2010) on the 2.4-m Hiltner telescope. The OSMOS slit was oriented N/S. Although the instrument supports a 20 arcmin longslit, we observed the target with the CCD readout in a subframe that limited the eﬀective slit length to 5 arcmin to reduce the readout time. A schematic of the slit coverage on the ﬁeld is shown in Figure 2.1.

The 1.2 arcsec slits we used in the OSMOS observations are shown with black rectangles. The irregular galaxy has four archival SDSS spectra; the locations of the

SDSS ﬁber apertures are shown with red circles.

We processed the raw CCD data using standard techniques in IRAF, including cosmic ray rejection using L.A.Cosmic (van Dokkum 2001). Each source was

14 extracted individually with apall, extracting a sky spectrum simultaneously. The range of extraction apertures was 14 24 pixels, corresponding to 3.8 6.6 arcsec, − − or 0.8 1.4 kpc at the distance of UGC 5189. With the 1.2 arcsec slit used in our − observations, the OSMOS VPH grism provides R 1600 at 5000 A.˚ The red slit ∼ position we used provides a wavelength coverage of 3960 6880 A.˚ Wavelength − calibration was done with Ar arclamp observations, and a spectrophotometric standard from Oke (1990) was observed each night, with which we performed relative

ﬂux calibrations.

The top panel of Figure 2.2 shows a spectrum of SN 2010jl obtained on Nov. 5.

Taking advantage of the N/S slit, we extract host galaxy spectra centered 7 arcsec ∼ on either side of the SN, corresponding to 1.5 kpc at the distance of UGC 5189. ∼ In 2.3.2 we use strong-line abundance diagnostics on these host galaxy spectra to § estimate the gas-phase oxygen abundances of the progenitor environment.

We also present in this paper a previously unpublished spectrum of the luminous (MB 21.2) type Ic SN 2010gx (Pastorello et al. 2010) obtained ≈ − with the WFCCD imager and spectrograph on the du Pont 2.5-m telescope at

Las Campanas Observatory using a longslit mask (slit width 1.7′′) and the 300 l/mm grism. We obtained 1200-sec spectra on three consecutive nights (UT March 22 24 − 2010), or 4 2 days (rest frame) prior to the B-band peak. The WFCCD data ± were reduced using standard tools in IRAF. We derived the wavelength solutions with HeNeAr arclamps obtained after each spectrum and did independent relative

15 ﬂux calibration using spectrophotometric standards observed each night. The

ﬁnal spectrum in Figure 2.3 is a combination of the three individual exposures, obtained in order to increase the S/N. The spectrum covers the wavelength range

3700 9200 A˚ and has a FWHM resolution of 7 A.˚ Assuming a Galactic − ∼ reddening law with RV = 3.1, the total V -band extinction was estimated to be AV = 0.27 mag using the ratio of Hα to Hβ fluxes and correcting to the theoretical case-B recombination value of 2.85. The spectrum was corrected for this reddening before calculating line ratios. The continuum was subtracted locally to each emission line before measuring the flux; the effect of the SN contamination on the metallicity determination should be small. We estimate the oxygen abundance to be 12 + log(O/H) = 8.36 using the diagnostic of Kobulnicky

& Kewley (2004), which uses R23 = ([O II]λ3727+[O III]λλ4959, 5007)/Hβ and

O32 = ([O III]λ5007+[O III]λ4959)/[O II]λ3727. These are highly sensitive to reddening, so the correction is crucial. (In subsequent discussion we convert this metallicity to the scale of Pettini & Pagel (2004) for uniformity.)

2.3. Results and analysis

2.3.1. SN 2010jl light curve

The ASAS light curves of SN 2010jl include several pre-peak observations beginning

25 days before discovery. We plot them in Figure 2.4 along with the V - and I-band

Swope data. For comparison, we plot R-band light curves from the literature of the

16 ultraluminous SN 2006gy (Smith et al. 2007) and SN 2006tf (Smith et al. 2008), and the luminous type IIn SN 1998S (Fassia et al. 2000). UGC 5189 was behind the Sun and unobservable prior to the ﬁrst ASAS observations. The color of the supernova changed from V I 0.4 mag at the epoch of ASAS discovery to V I 0.7 mag − ≈ − ≈ at the latest epochs. The UBVRI photometry from Swope is consistent with a

7000 K blackbody, as Smith et al. (2011b) conclude from a ﬁt to the spectrum. ∼

The normal range for peak luminosities of type IIn SNe is 18.5 < MR < 17 − ∼ ∼ − (Kiewe et al. 2012). The pre-discovery ASAS data constrain the peak luminosity of SN 2010jl (MV 19.9, MI 20.5), placing it ﬁrmly in the class of luminous ≈ − ≈ −

(peak MR < 20) type IIn supernovae. −

2.3.2. Metallicity near SN 2010jl

We use the empirical O3N2 and N2 diagnostics of Pettini & Pagel (2004) and the theoretical and empirical N2 diagnostic of Denicol´oet al. (2002) to determine the metallicity of the OSMOS-observed H II regions south and north of SN 2010jl.

We choose these diagnostics based on the S/N and wavelength coverage of our observations. The N2 diagnostic of Pettini & Pagel (2004) and of Denicol´oet al.

(2002) each depend solely on [N II]λ6584/Hαλ6563. This ratio is very insensitive to reddening due to the proximity of the lines. The O3N2 diagnostic of Pettini &

Pagel (2004) also depends on [O III]λ5007/Hβλ4861, another closely-spaced pair of lines. Although we do not correct these spectra for reddening, any reddening eﬀect

17 is very small compared to the scatter in each abundance diagnostic. For subsequent analysis in 2.3.4, we convert metallicities of other galaxies determined using § other diagnostics to these scales to enable direct comparison, using the empirical conversions of Kewley & Ellison (2008).

For the galactic H II regions 7 arcsec ( 1.5 kpc) south and north ∼ ∼ of the SN site, using the Pettini & Pagel (2004) O3N2 diagnostic, we

find 12 + log(O/H) = 8.2 0.1 dex. Using the N2 diagnostic, we find ± 12 + log(O/H) = 8.2 0.1 dex for the H II region north of the SN and 8.3 0.1 dex ± ± for the one to the south. Using the Denicolóet al. (2002) diagnostic, we find

12 + log(O/H) = 8.2 0.1 dex and 8.4 0.1 dex for the north and south regions, ± ± respectively.

Our estimates of the oxygen abundance are consistent with previously published estimates for UGC 5189 based on the SDSS spectrum from 5 arcsec ( 1 kpc) to the ∼ ∼ northeast of the SN site. Tremonti et al. (2004) report 12+log(O/H) = 8.15 0.1 dex ± for this galaxy, based on strong recombination and forbidden emission lines. Pilyugin

& Thuan (2007) report 12 + log(O/H) = 8.3 dex from the same spectrum based on direct electron temperature using the [O III]λ4363 auroral line. For our analysis in 2.3.4 we convert the Tremonti et al. (2004) metallicities, but for lack of an § established empirical conversion, we omit the Pilyugin & Thuan (2007) metallicities.

18 2.3.3. Metallicity elsewhere in the host galaxy

There are two other SDSS spectra of the galaxy (see Figure 2.1), including a bright point source in the south, approximately 20 kpc from the location of the supernova, marked in Figure 2.1 as SDSS 2. Pilyugin & Thuan (2007) report

12 + log(O/H) = 8.3 dex for this spectrum. The detection of He II λ4686A˚ indicates that the ionizing continuum is hard (e.g. Garnett et al. 1991), outside the regime where the Pettini & Pagel (2004) and Denicol´oet al. (2002) diagnostics are valid.

Brinchmann et al. (2008) include this spectrum in their SDSS-based study of nearby galaxies with Wolf-Rayet features. Tremonti et al. (2004) do not ﬁt a metallicity to this spectrum despite the high S/N, presumably because of the peculiar line ratios.

The other SDSS spectrum is of the eastern-most clump of UGC 5189, approximately 30 projected kpc from the location of the supernova (marked in

Figure 2.1 as SDSS 3). Neither Tremonti et al. (2004) nor Pilyugin & Thuan

(2007) derive metallicities from this spectrum, which has lower S/N. We ﬁnd

12 + log(O/H) = 8.2 0.1 dex with all three line ratio diagnostics using the SDSS ± spectrum.

Approximately 40 projected kpc from SN 2010jl is another blue galaxy at the same redshift (0.0106), which also has an SDSS spectrum, marked in Figure 2.1 as SDSS 4. The redshift of the spectrum from the supernova site is 0.0107; these redshifts are consistent within the estimated 0.0001 redshift error for this lower S/N

19 spectrum. From the shape and orientation of the wildly irregular UGC 5189, it seems very likely that this neighbor is a part of the interacting system. Using the

SDSS spectrum, we ﬁnd 12 + log(O/H) = 8.2 0.1 dex with both Pettini & Pagel ± (2004) diagnostics and 8.3 0.1 dex with Denicol´oet al. (2002). ±

UGC 5189 appears to be a high-mass, metal-poor outlier from the mass– metallicity relationship (e.g. Peeples et al. 2009). Such similar metallicity measurements from widely spaced locations and widely varying diagnostics provide reassurance that there are no signiﬁcant metallicity variations in UGC 5189, and that the three metallicity measurements described in Sec. 2.3.2 from a kpc or more away from the supernova site are likely to well-describe the supernova progenitor region. All evidence points toward a SN 2010jl metallicity of < 0.3 Z⊙. ∼

2.3.4. Host metallicity trends

The low metallicity of the host of SN 2010jl is another conﬁrmation of the emerging trend of low-metallicity hosts of very luminous supernovae (Koz lowski et al. 2010).

In Figure 2.5 we show this trend for SN 2010jl (green squares) and other luminous

SNe (blue pentagons; SN 2003ma, SN 2007bi, SN 2007va, and SN 2010gx). The oxygen abundance measurement for the host of SN 2010gx was obtained from our own spectroscopic observations, which we present here. The luminous supernovae without detected hydrogen (SN 2010gx and SN 2007bi) are indicated with double pentagons. We plot three metallicity measurements for the the host region of

20 SN 2010jl with green squares: two from our OSMOS spectra of the regions 1 ∼ kpc north and south of the supernova, and one from the SDSS spectrum 1 kpc ∼ northeast of the supernova (the value from strong line ﬁtting published by Tremonti et al. 2004). We cannot convert the Pilyugin & Thuan (2007) measurement of 8.3 to the scale of the Pettini & Pagel (2004) N2 diagnostic and so to maintain a strictly uniform abundance scale we do not plot it here. All four are consistent to within the errors, indicating that there is no strong metallicity variation in the area and that the strong line diagnostics are well-behaved and not in the poorly-calibrated regime of very hard radiation ﬁelds. For comparison, we also plot contours showing the distribution of the measured SDSS galaxy metallicities from Tremonti et al.

(2004). We also plot for comparison the medians and ranges of nebular abundances measured in the LMC and SMC with strong line diagnostics from H II ﬂux ratios reported by Russell & Dopita (1990) and absolute B magnitudes from Karachentsev

(2005). We stress that the actual range in the LMC and SMC metallicities is likely lower than the range given by applying the Pettini & Pagel (2004) N2 diagnostic to these H II regions; there is a fairly large scatter inherent in the diagnostic. We plot six low-z GRB/SN hosts (red circles) to show the similarity in metallicity trends for luminous SNe and GRB hosts (Chornock et al. 2010; Levesque et al. 2010a).

For high-luminosity SNe which do not yet have direct host metallicity measurements, we plot arrows at the host absolute magnitudes or at the upper limit host absolute magnitude (from left to right: SN 2005ap, SN 2006tf, SN 2008fz,

21 SN 2005gj, PTF 10hgi, SN 2008es, PTF 10heh, PTF 09cnd, SCP06F6, PTF 10vqv,

SN 2009jh, SN 1999as, PTF 09atu, and SN 2006gy). By comparing the distribution of these to the Tremonti et al. (2004) contours, it is clear that the host galaxies of these luminous SNe are in fainter galaxies. Neill et al. (2011) have also shown that the host galaxies of the most luminous SNe are the faintest, bluest galaxies. These hosts for which the metallicity has not yet been determined themselves provide support for the low-metallicity trend among luminous SNe hosts, because the faintest galaxies tend to be the most metal-poor (e.g. Tremonti et al. 2004).

These metallicity determinations were made with a number of diﬀerent diagnostics, and so we have converted them all to a single scale using the empirical conversions of Kewley & Ellison (2008). To emphasize that the low-metallicity trend is not an artifact of the particular common scale we choose, we plot the same thing on two diﬀerent scales, that of the N2 diagnostic of Pettini & Pagel (2004) in

Figure 2.5 and that of Denicol´oet al. (2002) and Tremonti et al. (2004) in Figure 2.6.

These were chosen to avoid a gap in the range where the Kewley & Ellison (2008) conversions from Tremonti et al. (2004) are valid. Slightly under 2% of the galaxies plotted have Tremonti et al. (2004) metallicities outside the 8.05–9.2 span where the Kewley & Ellison (2008) conversions are valid (mostly > 9.2), and these are excluded from the contour plot; the ﬂat contour at the top of each plot is due to this exclusion. In Figure 2.6, we approximated the Kewley & Dopita (2002) metallicity of GRB020903 as 8.1 instead of 8.07 0.1 in order for the Kewley & Ellison (2008) ±

22 conversion to be valid, and averaged the converted metallicities from the N2 scale of

Pettini & Pagel (2004) and Denicol´oet al. (2002) for the LMC, the SMC, and the north and south galaxy regions of the host of SN 2010jl observed by OSMOS. On each of these three scales, the low-metallicity trend is clear.

Prieto et al. (2008) found that SN Ib/c occur more frequently in higher- metallicity galaxies compared to SN II and SN Ia. The highly luminous SNe we show in Figure 2.5 appear to show a larger bias towards low-metallicity hosts than normal

SN Ib/c. For comparison, in Figure 2.7 we plot host metallicities for type II and type Ib/c SNe that have hosts that have SDSS spectra and are in the redshift range

0.04 >z> 0.01. We caution that these SNe are from many diﬀerent surveys, each of which has its own selection biases. On the left, the galactic metallicities are provided as observed, in the central three arcsec of the galaxy. On the right, they have been approximately corrected to the position of the supernova for the expected metallicity gradient often observed in similar galaxies. We use an empirical formula for the metallicity gradient in dex/kpc as a function of absolute B magnitude of the host galaxy that is derived from the data published in Moustakas & Kennicutt (2006).

We assume that dwarf galaxies with MB > 18 mag are chemically homogeneous, − and we assume no correction to the metallicity for supernovae within the 1.5′′ radius of the SDSS ﬁber.

To roughly quantify the diﬀerence, we performed KS tests and found that for the naive (and likely incorrect) assumption of uniform selection, the probability of

23 the luminous SNe and the type II or type Ib/c SNe progenitor regions being drawn from the same distribution in metallicity ranges from 3.9 10−4 to 5.6 10−5. × × Given the very diﬀerent selection functions of these samples, though, this is in no way statistically rigorous, and should merely be taken as thought-provoking. Some related results have been recently shown to hold for homogeneous and untargeted samples; Arcavi et al. (2010) show that there is a signiﬁcant excess of SNe IIb in dwarf hosts, and that while normal SNe Ic dominate in giant galaxies, all type I core-collapse events in dwarf galaxies are type Ib or broad-lined type Ic.

2.4. Discussion

The metallicity of the hosts of SN 2010jl and SN 2010gx support and strengthen the emerging trend that luminous supernovae occur preferentially in metal-poor hosts.

This result suggests that luminous SNe have low-metallicity progenitors. It is an interesting parallel to the similar trend observed for the hosts of long GRBs. The trend is not an artifact of which metallicity diagnostic is chosen for the host galaxies.

The relative iron abundance of these metal-poor hosts is expected to be even lower than suggested by the oxygen abundance that we use as a proxy for metallicity. At low metallicity, α-elements like oxygen are enhanced relative to iron, compared to the solar mixture. In the galactic disk and halo, at [Fe/H] > 1, − [O/Fe] is approximately inversely proportional to [Fe/H], while below [Fe/H] = 1, − the relationship ﬂattens out at a constant (and lower) relative iron abundance (e.g

24 Tinsley 1979; McWilliam 1997; Johnson et al. 2007). The iron abundance is more fundamentally important for the late-stage evolution of massive stars because iron provides much of the opacity for radiation-driven stellar winds (e.g. Pauldrach et al.

1986; Vink & de Koter 2005). By using oxygen abundance as a metallicity tracer, we are in fact underestimating the magnitude of the eﬀect of low-metallicity on mass-loss. If there is a threshold in iron abundance for certain types of luminous SNe, the observed eﬀect in oxygen abundance will be less pronounced. The substantial preference we see for these SNe to occur in hosts with low nebular abundance of oxygen points to a more stringent constraint in iron abundance.

Nebular oxygen abundances determined by electron temperature methods appear to track the stellar abundances of massive young stars very closely, as

Bresolin et al. (2009) demonstrate by comparing the gas-phase metallicity gradient from H II regions to the gradient in stellar abundances in NGC 300, so the gas-phase oxygen abundances we discuss should well characterize the stellar abundances of the massive young progenitors of these supernovae.

A number of observational results for normal SN Ib/c and SN II support a change in their number ratios as a function of metallicity. In an early study, Prantzos

& Boissier (2003) used the absolute magnitudes of supernova host galaxies as a proxy for their average metallicities from the luminosity-metallicity relationship, and found that the number ratio of normal SN Ib/c to SN II increases with metallicity.

Prieto et al. (2008) took advantage of the large sample of well-observed and typed

25 supernovae in the literature with star-forming host galaxies that have high S/N spectra obtained by the Sloan Digital Sky Survey (SDSS). They found strong evidence that normal SN Ib/c, dominated in rates by SN Ic, occur more frequently in higher-metallicity galaxies compared to SN II and SN Ia. Recently, Modjaz et al.

(2011) found that the local metallicity of the hosts of SN Ic is on average higher than for hosts of SN Ib (but see Anderson et al. 2010). All these results are consistent with state-of-the-art stellar evolution models of massive stars with rotation, either single or in binaries (e.g. Eldridge et al. 2008; Georgy et al. 2009, 2011).

Studies of local and cosmological long-duration GRBs ﬁnd that their host galaxies have low metallicity compared to the general population of star-forming galaxies and other CCSNe. These results help put constraints on progenitor models and the physics of production of the GRB jet, and they have fundamental implications for the use of GRBs as star-formation tracers at high redshift (e.g.

Kistler et al. 2009). Fruchter et al. (2006) initially pointed out that the host environments of cosmological GRBs were diﬀerent from CCSNe, with GRBs being hosted by strongly star-forming dwarf galaxies (see also Le Floc’h et al. 2003).

Stanek et al. (2006) compared direct host metallicity measurements of ﬁve local

GRBs with those of star-forming galaxies in SDSS, and found that long GRBs prefer low-metallicity environments. This result has recently been conﬁrmed using a larger sample of events (Levesque et al. 2010b). Modjaz et al. (2008) used direct host galaxy metallicity estimates to study nearby broad-lined SN Ic. They ﬁnd that the

26 metallicities of hosts of SN Ic without GRB are higher than the metallicities of hosts of SN Ic associated with GRBs.

2.4.1. Luminous supernovae

One of the most surprising results of the new transient surveys that are monitoring many square degrees of the sky every few nights has been the discovery of very luminous supernovae in the nearby Universe that are 10 times more luminous than ∼ normal core-collapse supernovae and can release 1051 ergs in UV-optical light. ∼

The ﬁrst observed events of this kind were SN 2005ap (Quimby et al. 2007) and SN 2006gy (Smith et al. 2007), discovered by TSS. Their light curves, spectral properties, and environments are remarkably diﬀerent, although they both share extreme energetics compared to normal CCSNe. SN 2005ap was discovered in a low-luminosity galaxy at z = 0.28. It showed a relatively fast evolution in its light curve, but with an extreme peak absolute magnitude of 23 mag never before seen − in a supernova. The spectrum had very broad features that were mainly associated with CNO events. The absence of hydrogen in the spectrum, the features at high velocity, and the extreme energetics led Quimby et al. (2007) to propose that

SN 2005ap could be associated with long GRBs, which connected this event with a high-mass progenitor star.

SN 2006gy was discovered close to the center of a nearby (z = 0.02) S0 galaxy with recent star formation. Its spectrum and light curve were similar to type IIn

27 SNe, but they were much more extreme, with a peak absolute magnitude of 22 and − clear signs of interaction between the supernova ejecta and a massive, hydrogen-rich circumstellar shell. This led Smith et al. (2007) to propose that the progenitor of SN 2006gy had been a very massive star (initial mass > 100 M⊙) similar to ∼ the well-studied galactic Luminous Blue Variable (LBV) η-Carinae. The explosion mechanism of SN 2006gy might be explained by the pulsational pair-instability model (Woosley et al. 2007).

van Marle et al. (2010) explore possible models for luminous type IIn SNe, and

ﬁnd within the parameter space that they explore that sustained high luminosity requires massive circumstellar shells. Metzger (2010) considers interaction with a relic protostellar disk, ﬁnding that when the disk is massive and compact and the cooling time is long compared to the expansion timescale, more of the energy is radiated in the optical, which they suggest is a possible explanation of luminous type

IIn events. Murase et al. (2011) explore potential consequences of such interactions with circumstellar shells for the production of gamma rays and neutrinos.

If LBV-like stars are the progenitors of these extremely luminous supernovae, that does not constrain them to live in high-metallicity environments; LBVs have been identiﬁed in galaxies with 12 + log(O/H) < 8.0 (Izotov & Thuan 2009; Herrero ∼ et al. 2010). The progenitor of the luminous type IIn SN 2010jl is constrained by Smith et al. (2011b) to be a massive LBV, a less massive object temporarily experiencing an LBV-like eruption in a precursor explosion, or a member of a

28 compact, massive cluster with turnoff mass > 30 M⊙. The progenitors of two less ∼ luminous type IIn events have been identified. One of these, SN 2005gl, occurred in a region with gas-phase metallicity of 12 + log(O/H) = 9.1 0.3, and its progenitor ± was identified as an LBV with MV = 10.3 (Gal-Yam & Leonard 2009). The other, − SN 1961V, a low-metallicity, normal-luminosity type IIn (Kochanek et al. 2011;

Smith et al. 2011a), had a progenitor with MZAMS > 80M⊙, which must have been greater than 30 M⊙ at the time of explosion to retain any hydrogen (Kochanek ∼ et al. 2011).

Since the discovery of SN 2005ap and SN 2006gy, there have been several very energetic supernovae (peak MR < 20) discovered by diﬀerent surveys out − to redshift z 1. We present a list in Table 2.2; we include whether each event ≈ is classiﬁed as type I (without hydrogen) or type II (with hydrogen). Many host absolute B magnitudes are converted and errors might be 0.2 mag or larger. The ∼ new discoveries include objects with a variety of properties. Some events are similar to SN 2005ap, including SCP06F6, PTF 09cnd, PTF 09atu, and SN 2009jh (Quimby et al. 2011), SN 2010gx (Mahabal et al. 2010; Quimby et al. 2010b; Mahabal & Drake

2010; Pastorello et al. 2010), PTF 10hgi (Quimby et al. 2010c), and PTF 10vqv

(Quimby et al. 2010d), and to SN 2006gy, including SN 2003ma (Rest et al. 2011),

SN 2005gj (Aldering et al. 2006; Prieto et al. 2007), SN 2006tf (Smith et al. 2008),

SN 2008es (Miller et al. 2009; Gezari et al. 2009), SN 2008fz (Drake et al. 2010), and PTF 10heh (Quimby et al. 2010a). Others are more similar to broad-lined

29 SN Ic in spectroscopic properties, but with extreme luminosity, such as SN 1999as and SN 2007bi (Gal-Yam et al. 2009). We also include a unique event, SN 2007va, that was completely enshrouded in its own circumstellar dust and only observable at mid-infrared wavelengths; its infrared luminosity indicated high reprocessed optical luminosity (Koz lowski et al. 2010). The most recent example (and nearest, at z = 0.011) is the type IIn SN 2010jl, the main subject of this paper. The variety of observed properties in these luminous CCSNe likely represents diﬀerent explosion mechanisms (e.g. normal collapse of the core vs. pair instability vs. pulsational pair-instability), diﬀerent mechanisms that power the energetics of the light curves

(e.g. circumstellar interaction vs. radioactive decay vs. magnetar spindown), and diﬀerent progenitor mass-loss histories and masses (e.g. LBV vs. Wolf-Rayet stars vs. red supergiants).

2.4.2. Metallicity diagnostics

The forbidden and recombination lines in the optical spectra of star-forming galaxies provide the observables needed to estimate chemical abundances, star-formation rates, and other physical properties of the H II regions, like electron temperature and density. These emission lines are produced by collisional excitation and recombination processes in gaseous nebulae where massive (> 20 M⊙) stars form, ∼ thanks to the interactions of the ionizing UV photons provided by the stars and the surrounding gas (e.g. Osterbrock 1989).

30 There is a vast literature on a number of techniques that have been developed over the last three decades to estimate the oxygen abundances and physical conditions of H II regions in star-forming galaxies (e.g. Kewley & Ellison 2008, and references therein). The methods can be divided into four diﬀerent categories: direct

(uses an estimate of the electron temperature measured from the faint [O III]λ4363A˚ auroral line), empirical (e.g. Pettini & Pagel 2004), theoretical (e.g. Kobulnicky &

Kewley 2004), and a combination of empirical and theoretical (e.g. Denicol´oet al.

2002). All these methods are based on high S/N measurements of the line ratios of diﬀerent combinations of emission lines from ions present in the optical region of the spectrum ( 3700 6700A):˚ [O II], [O III], [N II], [S II], Hα, and Hβ. ≃ −

Each method has relative advantages and shortcomings that have been investigated in a number of studies in the literature (e.g. Yin et al. 2007;

Moustakas et al. 2010), and there are known discrepancies of as much as

0.6 dex between diﬀerent estimates of the oxygen abundances. For example, ∼ the estimated oxygen abundance of the host galaxy of the luminous SN 2007va

(Koz lowski et al. 2010) obtained from the direct electron temperature method is 0.3 dex lower than the value obtained from the theoretical calibration of

R23 = ([O II]λ3727+[O III]λλ4959, 5007)/Hβ (Kobulnicky & Kewley 2004). Bresolin et al. (2009) demonstrate the oﬀsets between the various techniques for their determination of the metallicity gradient in NGC 300. The abundances estimated with one diagnostic, the N2 diagnostic of Pettini & Pagel (2004), essentially track

31 the stellar and (Te) nebular abundances they measure, albeit with rather more scatter. Because the eﬀective zero points and scales vary somewhat, regardless of which method is used, it is important to use (or convert to) just one.

With the S/N and wavelength coverage of our observations, we content ourselves in this paper with the empirical O3N2 and N2 diagnostics of Pettini & Pagel (2004) and the theoretical and empirical N2 diagnostic of Denicol´oet al. (2002). We use the empirical conversions of Kewley & Ellison (2008) to convert metallicities determined on other scales to these for the purpose of direct comparison.

2.5. Conclusions

Recent observations of the environments of supernovae and GRBs show that metallicity is a key parameter in the lives and deaths of massive stars. Our ﬁnding that the hosts of luminous SNe lie in the low-metallicity extremes of the distribution of star-forming galaxies supports this emerging picture. Verifying and constraining this emerging relationship with metallicity may be an important probe of the mechanisms of the most luminous supernovae, and this trend may be an important factor in various aspects of the evolution of the metal-poor early universe.

32 Fig. 2.1.— A schematic of the host galaxy of SN 2010jl. North is up and east is to the left. The OSMOS slit position is marked with a black rectangle 1.2 arcsec wide. The slit was centered on the supernova. The ﬁber positions of archival SDSS spectra of the galaxy are marked with the red circles, which have a diameter of 3 arcsec. The background is constructed from SDSS g imaging. The small blue galaxy in the southwest corner of the image has a similar spectrum and is at the same redshift (z=0.0107). It is almost certainly part of the interacting system. The position of SN 2010jl is marked, as well as the emission regions to the north and south for which we extract OSMOS spectra.

33 Fig. 2.2.— OSMOS spectra of SN 2010jl and the emission line regions 7 arcsec ( 1.4 kpc) to the north and south. Negative ﬂux values from noise have been∼ truncated∼ to zero. Dashed lines show the wavelengths of Hβ λ4861, [O III]λλ4959, 5007, Hαλ6563, [N II]λ6584, and [S II]λλ6717, 6731 at z = 0.0107.

34 Fig. 2.3.— Spectrum of the luminous SN 2010gx obtained with the du Pont 2.5-m telescope at the Las Campanas Observatory in March 2010, before the supernova reached maximum light. The spectrum has a blue continuum with broad ( 10000 km s−1) absorption features likely associated with ionized oxygen, nitrogen, and∼ carbon. Although the nature of these events is still under debate, they share characteristics that are similar to the spectra of SN Ib/c (Quimby et al. 2011; Pastorello et al. 2010). The dashed lines mark the wavelengths of narrow emission lines of [O II]λ3727, Hβ λ4861, [O III]λλ4959, 5007, and Hα λ6563 detected from the star-forming host galaxy. Using these lines, we can estimate an oxygen abundance for the host of SN 2010gx of 12 + log(O/H) = 8.36, using the strong line diagnostic of Kobulnicky & Kewley (2004). This corresponds to 0.3 Z⊙, similar to the metallicity of the LMC. In subsequent ﬁgures we convert this∼ value to the scale of the N2 diagnostic of Pettini & Pagel (2004), giving 12 + log(O/H) = 8.16.

35 Fig. 2.4.— Absolute magnitude light curve of SN 2010jl, plotted for comparison with other type IIn supernovae from the literature. The ﬁlled circles show the V - and I-band light curves of SN 2010jl from ASAS North and Swope. For all the other SNe we show the R-band light curves: the ultraluminous SN 2006gy (open circles, Smith et al. 2007) and 2006tf (open triangles, Smith et al. 2008), and the type IIn 1998S (Fassia et al. 2000). The normal range of peak luminosities for type IIn SNe is 18.5 < MR < 17 (Kiewe et al. 2012). The horizontal axis is the time with respect to− the∼ earliest∼ detection.− In the case of SN 2010jl, the SN was discovered on Nov 3.5, 2010 (Newton & Puckett 2010), but the ﬁrst detection from ASAS North is from Oct 9.6, 2010, 25 days earlier.

36 Fig. 2.5.— Host galaxy metallicities and absolute magnitudes are plotted for luminous SNe (blue pentagons) including SN 2010jl (green squares). The luminous supernovae without detected hydrogen (SN 2010gx and SN 2007bi) are indicated with double pentagons. The contours show the distribution of SDSS galactic metallicities from Tremonti et al. (2004). For comparison we plot the host galaxies of low- redshift long GRBs (red points) showing that both they and the luminous SNe hosts appear primarily in the low-metallicity tail of the galaxy distribution. We calculate abundances of the (Russell & Dopita 1990) sample of H II regions for the LMC and SMC calculated with the N2 diagnostic of Pettini & Pagel (2004) and plot ranges and medians with cyan stars. (We stress that the actual ranges in the LMC and SMC metallicities are likely lower than the ranges given by applying the Pettini & Pagel (2004) N2 diagnostic to these H II regions; there is a fairly large scatter inherent in the diagnostic.) Host galaxies of luminous SNe that have no measurements yet of host metallicity are indicated with arrows at their absolute B magnitude. The right vertical axis shows the corresponding scale of oxygen abundance in terms of the Solar value from Delahaye & Pinsonneault (2006). All metallicity measurements have been converted to the scale of the N2 diagnostic of Pettini & Pagel (2004) using the conversions of Kewley & Ellison (2008).

37 Fig. 2.6.— Same as Figure 2.5, except (left) with all metallicities on or converted to the scale of the Denicol´oet al. (2002) strong-line diagnostic, or (right) with all metallicities on or converted to the scale of the Tremonti et al. (2004) diagnostic. We emphasize that the low-metallicity trend is not an artifact of which metallicity scale one prefers.

38 Fig. 2.7.— Type II and type Ib/c SNe that have hosts with SDSS spectra and metallicities (Tremonti et al. 2004) are plotted in the same way as Fig. 2.5. On the left, the galactic metallicities have been approximately corrected for metallicity gradients to the position of the SN. On the right, no such correction has been made. As in previous ﬁgures, all metallicities have been corrected to the scale of the Pettini & Pagel (2004) N2 diagnostic using the conversions of Kewley & Ellison (2008). The hosts of the overluminous SNe from Fig 2.5 have been plotted here again for reference.

39 HJD U σ B σ V σ R σ I σ Telescope / -2450000 (mag) (mag) (mag) (mag) (mag) Instrument

5479.14 13.79 0.10 ASAS ············ ············ 5484.13 13.73 0.10 13.28 0.10 ASAS ············ ······ 5488.12 13.67 0.10 13.09 0.10 ASAS ············ ······ 5493.14 13.73 0.10 ASAS ············ ············ 5494.12 13.00 0.10 ASAS

40 ························ 5504.10 13.78 0.10 13.10 0.10 ASAS ············ ······ 5509.10 13.86 0.10 ASAS ············ ············ 5509.13 13.18 0.10 ASAS ························ 5515.85 13.79 0.05 14.21 0.05 13.82 0.05 13.44 0.05 13.18 0.05 Swope 5516.85 13.79 0.05 14.23 0.05 13.84 0.05 13.43 0.05 Swope ······ 5517.13 13.28 0.10 ASAS ························ 5517.85 13.82 0.05 14.23 0.05 13.84 0.05 13.19 0.05 Swope ······

Table 2.1. Optical photometry of SN 2010jl SN Name RA Dec SN Spectral Redshift MB(host) Host Metallicity? (J2000.0) (J2000.0) Type (mag) (12+log[O/H], PP04N2)

SN 1995av 02:01:36.7 03:38:55 IIn 0.3 No ··· SN 1997cy 04:32:54.8 –61:42:57 IIn 0.063 17.8 No − SN 1999as 09:16:30.8 +13:39:02 Ic 0.13 18.4 No − SN 1999bd 09:30:29.2 +16:26:08 IIn 0.151 18.4 No − SN 2000ei 04:17:07.2 +05:45:53 IIn 0.600 > 19.3 No − SN 2003ma 05:31:01.9 –70:04:16 IIn 0.29 19.9 8.45 − SN 2005ap 13:01:14.8 +27:43:31 Ic? 0.28 16.1 No

41 − SN 2005gj 03:01:12.0 +00:33:14 Ia/IIn 0.06 -16.7 No SCP06F6 14:32:27.4 +33:32:25 Ic? 1.19 > 18.1 No − SN 2006gy 03:17:27.0 +41:24:20 IIn 0.02 20.3 No − SN 2006tf 11:56:49.1 +54:27:26 IIn 0.07 16.2 No − SN 2007bi 13:19:20.2 +08:55:44 Ic 0.13 16.3 8.18 − SN 2007va 14:26:23.2 +35:35:29 IIn? 0.19 16.2 8.19 − SN 2008am 12:28:36.2 +15:34:49 IIn 0.234 19.6 No − SN 2008es 12:46:15.8 +11:25:56 IIL 0.21 > 17.1 No − SN 2008fz 23:16:16.6 +11:42:48 IIn 0.13 > 16.6 No −

Table 2.2. List of luminous CCSNe collected from the literature (cont’d) Table 2.2—Continued

SN Name RA Dec SN Spectral Redshift MB(host) Host Metallicity? (J2000.0) (J2000.0) Type (mag) (12+log[O/H], PP04N2)

PTF 09atu 16:30:24.6 +23:38:25 Ic? 0.50 > 19.8 No −

42 PTF 09cnd 16:12:08.9 +51:29:16 Ic? 0.26 > 18.1 No − SN 2009jh 14:49:10.1 +29:25:11 Ic? 0.35 > 18.3 No − SN 2010gx 11:25:46.7 –08:49:41 Ic? 0.23 17.2 8.16 − PTF 10heh 12:48:52.0 +13:26:24.5 IIn 0.34 18.0 No − PTF 10hgi 16:37:47.0 +06:12:32.3 Ic? 0.10 > 17.0 No − PTF 10vqv 03:03:06.8 –01:32:34.9 Ic? 0.45 18.2 No − SN 2010jl 09:42:53.3 +09:29:41.8 IIn 0.01 19.3 8.19 − Chapter 3: Probing the low-redshift star formation rate as a function of metallicity through the local environments of type II supernovae

3.1. Introduction

The question of how certain rare classes of supernovae (SNe) depend on progenitor metallicity has been limited by the lack of an unbiased standard of comparison. It is incorrect to use the metallicity distribution of galaxies for this because core-collapse supernovae (CCSNe) trace star formation rather than stellar mass. Existing supernova host galaxy metallicity distributions are selected to compare matched samples and use supernova surveys that do not search in the faintest hosts, and are not likely to be suitable as a standard of comparison for rare events that appear to be more common in low-mass galaxies. We address this by measuring a metallicity distribution of type II supernova progenitor regions from a single source survey that is areal rather than targeted.

43 as to how diﬀerences in the stellar progenitors of the SNe are related to the explosions. For example, the relative fractions of hydrogen-rich type II SNe and hydrogen-poor type Ib/c SNe can be predicted as a function of metallicity based on models for mass-loss from the progenitor stars (Eldridge et al. 2008; Georgy et al.

2009, 2012). Standard mass loss models for massive stars are based on line-driven winds (e.g. Kudritzki & Puls 2000); the eﬃciency of these winds depends on metallicity because metals, particularly iron, dominate the line opacities driving the winds (Vink & de Koter 2005). Furthermore, the explosion energy of normal type II

SNe may depend on the progenitor metallicity (Kasen & Woosley 2009), and certain types of SNe may occur only for low-metallicity progenitor stars (Ober et al. 1983;

Heger & Woosley 2002; Langer et al. 2007).

A serendipitous observation of a SN progenitor star prior to explosion is one useful way to characterize the progenitor and determine how the SN properties depend on progenitor properties. Unfortunately, observing or strongly constraining the properties of the progenitor star is only possible for SNe in nearby galaxies, leading to very small samples (e.g. Smartt 2009, and references therein). This limits the utility of this technique for constraining the properties of subclasses of events that are rare and therefore observed mostly at large distances, such as extremely optically luminous CCSNe.

Observational techniques for addressing this question without pre-explosion data involve estimating the progenitor properties from the environments that remain

44 behind. These techniques range from estimating progenitor age and mass from the degree of correlation with Hα (Anderson & James 2008) and NUV (Anderson et al.

2012) emission, to detailed characterization of resolved stellar populations near supernova explosion sites in nearby galaxies (Badenes et al. 2009; Murphy et al.

2011), to extrapolation of stellar population properties from galaxy photometry and spectroscopic observations (e.g. Kelly et al. 2008; Arcavi et al. 2010; Kelly &

Kirshner 2012).

(GRBs) were in faint, metal-poor galaxies, proposing a progenitor metallicity cut-oﬀ

45 above which GRBs do not occur, and Modjaz et al. (2008) found that type Ic SNe that were associated with GRBs were in more metal-poor regions than those that were not. Anderson et al. (2010) found at marginal signiﬁcance that type Ib and type Ic SNe hosts have slightly higher metallicity than hosts of type II and type Ia

SNe. The metallicity local to type Ic SNe without broad lines was found by Modjaz et al. (2011) to be on average higher than near type Ib SNe, regardless of which strong-line metallicity diagnostic was used, consistent with the results of Kelly &

Koz lowski et al. 2010). These abnormally luminous CCSNe are predominantly

SNe Ic or IIn. Subsequent spectroscopic measurements of the host of the luminous

SN 2008am (Chatzopoulos et al. 2011) and the luminous SN 2010ay (Prieto &

Filippenko 2010; Modjaz et al. 2010; Sanders et al. 2012), and photometric limits on the hosts of the luminous SNe PS1-10awh and PS1-10ky (Chomiuk et al. 2011) reinforce this conclusion.

46 Many studies (e.g. Neill et al. 2009; Sanders et al. 2012; Stoll et al. 2011;

Campisi et al. 2011; Vergani et al. 2011) compare host galaxies of SNe or GRBs to the overall galaxy population, which is a good way to put the host metallicity results in context. It is not, however, a secure way to evaluate whether metallicity is a key parameter governing whether a supernova has a given spectral type or luminosity. Supernovae trace star formation rather than overall stellar mass, and star formation is not evenly distributed among galaxies of a given mass. Recent results (Lara-L´opez et al. 2010; Mannucci et al. 2010) show that the scatter in the galaxy mass–metallicity relationship may be reduced by considering star formation as a third parameter, and that star formation rates (SFRs) are higher in lower metallicity galaxies at a given mass (but see also Yates et al. 2012, which ﬁnds that the form of any such relation between mass, metallicity, and star formation rate depends on the strong-line metallicity diagnostic used).

Levesque et al. (2010b) and Han et al. (2010) suggested that instead of a metallicity cutoﬀ for GRBs there is a separate luminosity-metallicity relationship for

GRB host galaxies offset to lower metallicity than the normal galaxy mass–metallicity relationship of Tremonti et al. (2004). (These results are consistent with a simple metallicity cutoff with the exception of a single high-mass GRB host galaxy that appears to be high metallicity.) No such offset has been observed for type Ia SNe

(Neill et al. 2009). This question of an oﬀset in the mass–metallicity relationship has confused many subsequent discussions of a metallicity cutoﬀ.

47 Mannucci et al. (2011) notes that for galaxies at a given mass, lower metallicity galaxies have higher average star formation rates (Mannucci et al. 2010) and thus core-collapse events, which trace star formation, should have a mass–metallicity distribution shifted to slightly lower metallicity at a given mass. This effect is qualitatively similar enough to the offset proposed by Levesque et al. (2010b) and Han et al. (2010) that Mannucci et al. (2011) conclude that GRB hosts do not differ substantially from the typical galaxy population and therefore there is no metallicity dependence to GRB hosts. Kocevski & West (2011) quantify the expected metallicity shift, and find that the star-formation-weighted relationship between galaxy mass and metallicity is shifted towards lower metallicity at a given mass, but that this alone cannot explain the observed GRB host distribution, and a metallicity dependence is still required. Many subsequent studies have claimed to disprove any dependence on metallicity for GRBs or luminous SNe by showing that the host galaxy metallicity is consistent with its mass and star formation rate according to the relationship between the three (Lara-López et al. 2010; Mannucci et al. 2010). While this is evidence against a distinct mass–metallicity relationship for the hosts of these SNe, it is not evidence against a metallicity dependence.

This shift can also be quantiﬁed semi-analytically by convolving the galaxy mass function with the relationship between galaxy mass, metallicity, and star formation rate to ﬁnd the overall distribution of star formation as a function of metallicity. Niino (2011) does so by comparing the observed metallicity distribution

48 of GRB hosts to the metallicity distribution of star formation, calculated two different ways; first, calculated observationally from the stellar mass function, the galaxy M–SFR relation, and the galaxy mass–metallicity relation, and second, calculated using the relationship between galaxy mass, metallicity, and SFR defined by Mannucci et al. (2010), and assigning metallicities based on SFR as well as mass.

He finds the difference between these two estimates is less than 0.5 dex in oxygen abundance on the KK04 scale. Regardless which method is used, he finds that the

GRB host metallicity distribution is incompatible with the metallicity distribution of star formation unless the GRB fraction depends on metallicity. (This is not the primary result of that study, which examines the fact that galaxies do not have one single metallicity, but show an internal spread. Assuming a hypothetical transient phenomenon which has a strict cutoﬀ metallicity above which it cannot occur, Niino

(2011) shows that such a spread in internal galaxy metallicities would serve to widen the observed host metallicity distribution of that transient phenomenon.)

Only by comparing the metallicity distribution of a SN variety to the overall metallicity distribution of star formation can one rigorously test for a metallicity dependence. Our metallicity distribution of type II SN hosts can serve as a standard of comparison for evaluating how rare classes of SNe depend on progenitor metallicity.

Essentially all of these previous studies relating metallicity and SN properties draw on SN samples from multiple surveys. A perfectly homogeneous and unbiased

49 SN sample does not currently exist. At present, the Palomar Transient Factory

(Rau et al. 2009, PTF) appears to supply the closest approximation to this, in the sense that the SN selection does not exclude the smallest, most vigorously star-forming galaxies. This would not be true, for example, of the Lick Observatory

Supernova Search (LOSS) survey (Li et al. 2011), which explicitly targets larger galaxies. In contrast, the PTF biases their followup slightly towards supernovae in low-mass galaxies (I. Arcavi, private communication). In this paper we will focus on the 52 type II SNe found in the ﬁrst year of PTF operations. Because some subtypes of CCSNe are known to have diﬀerent distributions in host metallicity, we focus on type II SNe. We have measured metallicities for the environments of a representative subsample of 34 of these type II SNe, and present the resulting metallicity distribution.

This distribution probes the low-redshift star formation rate as a function of metallicity in an independent way from current methods relying on galaxy population statistics. This is an important distribution to characterize because in order to determine whether a massive star outcome has a metallicity dependence, we need to examine its frequency relative to the metallicity distribution of star formation, not to the metallicity distribution of existing stellar mass (the galaxy metallicity distribution). Because the PTF followup is biased slightly towards transients in lower mass host galaxies, we expect the progenitor region metallicity distribution we measure may be slightly more metal-poor than the true overall progenitor

50 distribution of type II SNe. This makes our distribution a rigorous standard of comparison for a population suspected to be metal-poor; if that population is signiﬁcantly more metal-poor than the distribution we present here, it is certainly more metal-poor than the overall distribution of type II SNe.

We place the metallicity distribution of type II SNe environments in context by comparing it with previous supernova host studies (Prieto et al. 2008; Anderson et al.

2010; Kelly & Kirshner 2012), with the SDSS DR7 MPA/JHU value-added catalog

(Kauﬀmann et al. 2003; Tremonti et al. 2004; Brinchmann et al. 2004; Salim et al.

2007), and with estimates from galaxy population statistics (Stanek et al. 2006).

Noting that iron is more fundamental than oxygen to the evolutionary outcomes of massive stars because iron opacity drives stellar winds, we then translate our oxygen abundance distribution of type II SNe environments into an iron abundance distribution by using the observed relationship between oxygen and iron abundance in Milky Way bulge, disk, and halo stellar abundances.

3.2. Spectroscopic observations and analysis

We drew our targets from the ﬁrst-year sample of the Palomar Transient Factory

(PTF) survey (Arcavi et al. 2010), an areal rather than a targeted survey, so supernovae in the lowest-mass galaxies are not excluded by selection. The sample selection for the survey is not yet published, but transients in dwarf hosts are prioritized for spectroscopic followup (I. Arcavi, private communication), so a slight

51 bias towards metal-poor environments is expected. In contrast, a bias towards metal-rich environments would be expected for galaxy-targeted surveys, which miss low-mass galaxies entirely. There are 52 type II SNe in the full ﬁrst-year PTF CCSN sample, of which we have measured spectroscopic metallicity determinations for a subsample of 34. This subsample is representative of the overall type II sample, as we show in 3.4.1. §

We obtained host galaxy spectra of these SNe using the Ohio State Multi-Object

Spectrograph (OSMOS, Martini et al. 2011; Stoll et al. 2010) on the 2.4-m Hiltner telescope, the Wide Field Reimaging CCD Camera (WFCCD) on the 2.5-m du Pont telescope, and the dual imaging spectrograph (DIS) on the 3.5-m Astrophysical

Research Consortium telescope. We also use twelve archival spectra from SDSS DR7

(Abazajian et al. 2009; Uomoto et al. 1999; York et al. 2000; Gunn et al. 2006).

The properties of the spectroscopic observations are summarized in Table 3.1. We processed the data using standard techniques in IRAF1, individually extracting each spectrum. Relative ﬂux calibration was done with observations of spectrophotometric standard stars taken each night. The PP04N2 metallicity diagnostic is extremely insensitive to reddening because it depends on the ﬂux ratio of two very close lines, so intrinsic extinction corrections were not applied. Images of nine representative

1IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the

Association of Universities for Research in Astronomy (AURA) under cooperative agreement with the National Science Foundation.

52 type II hosts spanning the observed range of metallicity and galaxy mass are shown in Figure 3.1. We also measured spectroscopic metallicities for the hosts of three type Ib, two type IIb, three type Ic, and one type Ic-BL from the ﬁrst-year PTF sample. These numbers are too small for rigorous statistical comparison, so we exclude these from our analysis of the type II sample, and discuss them further in

3.4.5. §

The new observations were made either at the supernova position or at a similar galactocentric radius to minimize any biases from metallicity gradients in the host galaxies. We include the angular distance from the host galaxy center to the supernova site in Table 3.2 for easy comparison to the seeing and to the spectroscopic aperture, listed in Table 3.1. We include the projected physical distance from the galaxy center to facilitate future comparison to studies that use galactocentric spectra. The SDSS spectra are the only spectra taken at the galaxy center rather than at the galactocentric radius of the supernova. Note that for these

SDSS spectra, most SN locations are within the ﬁber diameter, and only one is more than 2 ﬁber diameters away. We obtained new spectra for any hosts with existing

SDSS spectra more distant than this. Any resulting eﬀect from galactic metallicity gradients on the metallicity distribution should be minimal. Line ﬂuxes of Hα λ6563 and [N II]λ6584 are given in Table 3.2.

For host galaxies with multiple SDSS spectra or with spectra from multiple sources, we ﬁt a metallicity gradient where possible and provide the best ﬁt

53 metallicity at the galactocentric radius of the supernova progenitor; these are labeled as ‘grad’. Line ﬂuxes, observed galactocentric radii, and derived metallicities for these measurements are given in Table 3.3.

We primarily consider host metallicities determined with the N2 diagnostic of

Pettini & Pagel (2004), which we directly measure for each of our targets. This diagnostic depends solely on [N II]λ6584/Hαλ6563, and is extremely insensitive to reddening, though it has a larger intrinsic scatter than other strong-line diagnostics based on the physical conditions of the H II regions. There are a number of techniques that are used to estimate the oxygen abundances of H II regions in star-forming galaxies, and there is a substantial literature discussing their various merits and drawbacks (e.g. Kewley & Ellison 2008, and references therein). A full recapitulation of this is outside the scope of this paper, but we discuss the consequences of these uncertainties for our study in 3.4.6. §

In Table 3.2 we list the metallicities of the progenitor regions of 34 type II

SNe, three type Ib, two type IIb, three type Ic, and one type Ic-BL from the PTF

ﬁrst-year core-collapse sample. All subsequent analysis is of the type II hosts, for which we have good statistics. We do not include type IIb SNe in the type II sample because their spectral similarity to type Ib SNe at all but early times can lead to some typing issues, and because previous studies (e.g. Modjaz et al. 2011; Kelly &

Kirshner 2012) considered them in the stripped-envelope subclass. We show the metallicity distribution of the type II hosts for several diﬀerent strong-line metallicity

54 diagnostics using the empirical conversions of Kewley & Ellison (2008) in Figure 3.2.

In subsequent ﬁgures, we choose as our scale convention only the N2 diagnostic of

Pettini & Pagel (2004).

3.3. Characterizing the spectroscopic subsample

To investigate whether we have acquired spectra of a representative subsample of the hosts of ﬁrst-year PTF CCSNe (Arcavi et al. 2010), we compared the stellar mass and star formation rates of the hosts with and without metallicity estimates. We used SED models of the SDSS photometry of the hosts within the DR8 footprint to estimate the masses, SFR, and characteristic stellar ages. Of the 52 type II SNe in the full sample, 47 have SDSS photometry. We also analyzed the properties of the

19 non-type II PTF CCSN hosts that fell in the DR8 footprint, but we will restrict our comparisons with our spectroscopic sample of type II SNe to these 47 type II

SNe to avoid any of the currently known selection eﬀects with metallicity linked with supernova type (see 3.1). §

3.3.1. Extracting ﬂuxes from SDSS imaging

We began with ugriz images of the 66 ﬁrst-year PTF CCSN ﬁelds in the SDSS Data

Release 8 (Aihara et al. 2011). These images are fully calibrated in the SDSS natural system, which is close to the AB system, and sky-subtracted. We combined the most sensitive SDSS bands (gri) for each supernova ﬁeld in order to make a deeper

55 stacked image that can be used to find all the galaxies and define their photometric apertures. We used these deeper stacked images as the reference image for source detection using SExtractor (Bertin & Arnouts 1996) and checked by eye the positions around each supernova in order to select the most likely host galaxy. We were able to assign likely host galaxies for 64/66 SNe. The two events without host galaxy detections, PTF09be and PTF09gyp, have sources that are > 13 kpc (projected) ∼ from the positions of the SNe. We note that the host of PTF09gyp has a reported magnitude of r = 21.75 mag in Arcavi et al. (2010) from pre-explosion PTF survey co-adds (I. Arcavi, private communication), but we cannot confirm this detection with DR8, although a galaxy detected approximately 15 arcsec away has a similar magnitude. After selecting the host galaxies in the stacked images, we used imedit in IRAF2 to mask nearby stars, which could contaminate the flux measurements,

filling the masked regions with the local background. Finally, we ran SExtractor on the individual ugriz images using the apertures defined from the deeper stacked images to obtain total (AUTO) galaxy fluxes. We applied the small (< 0.04 mag) ∼ corrections derived in Kessler et al. (2009) to transform the SDSS fluxes to the AB system. The resulting coordinates and fluxes of the host galaxies are presented in

Table 3.4, and absolute magnitudes k-corrected and corrected for galactic extinction are presented in Table 3.5. We include 3σ upper limits for the hosts of PTF09be and

2IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the

Association of Universities for Research in Astronomy (AURA) under cooperative agreement with the National Science Foundation.

56 PTF09gyp, which were calculated assuming a circular aperture of radius r = 5 kpc at the distance of the SN.

3.3.2. Galaxy properties

We used the code for Fitting and Assessment of Synthetic Templates (FAST v0.9b,

Kriek et al. 2009) to ﬁt these host galaxy spectral energy distributions to estimate the stellar mass, star formation rates (SFRs), and characteristic ages. We chose the Bruzual & Charlot (2003) libraries, a Salpeter IMF, and Solar (Z = 0.02) metallicity to do the ﬁts. We also assumed an exponentially declining SFR model with τ = 1 Gyr for the star forming component of the model. The results are presented in Table 3.6.

In order to check the SFRs calculated with FAST, we also estimated SFR based on the results presented by Salim et al. (2007). This method is also based on toy models for the SFR as a function of time, but combines bursts, constant star formation, and exponentially declining star formation, which make it more realistic than FAST. We used the results of Salim et al. (2007) for 50000 SDSS galaxies with ∼ GALEX photometry to derive a relation between the absolute u-band magnitudes, corrected for intrinsic attenuation, and their derived SFRs. We ﬁt a linear relation between Mu and SFR ( in M⊙/yr) of the form log(SFR) = 0.36 Mu 6.73 − × −

(for a Salpeter IMF), valid for 2 > log(SFR) > 2 and log(SFR/M∗) > 10.5, − − with an rms scatter of 0.23 dex. We applied this relation to obtain SFRs for the

57 supernova host galaxies, using the Low-Resolution Template code of Assef et al.

(2008) to derive k-corrected u-band absolute magnitudes corrected for Galactic extinction. After further correcting these magnitudes by intrinsic attenuation using the values obtained with FAST and the Calzetti reddening law, we applied the linear relation derived from the Salim et al. (2007) data to estimate SFRs. These values are presented in Table 3.6. The agreement with the SFRs derived by FAST is fairly good in general, with a Kolmogorov-Smirnov (K-S) test probability of 81% of the results of the two diﬀerent methods being drawn from the same underlying distribution. The two SFR estimation methods are directly compared in Figure 3.3.

The SFR calculated from the u-band luminosity with aperture corrections is more consistent with the method the MPA/JHU value-added catalog uses to determine star formation rates than the FAST template ﬁtting.

3.4. Results and Discussion

This is the largest sample yet of supernova host galaxy spectra metallicity measurements from a single survey. Nevertheless, limited observing time made following up the entire PTF type II sample impractical, so we ﬁrst show in 3.4.1 § that our spectroscopic host sample is representative of the full sample. We then place the SN hosts in context in 3.4.2 by comparing their properties to those of galaxies § in the MPA/JHU value-added catalog. We ﬁnd that while they are well-matched in galaxy mass and metallicity, the type II hosts appear to be biased toward higher

58 star formation rates than the galaxies in the catalog. We show in 3.4.3 that the § metallicity distribution of these type II hosts is remarkably similar to that found by previous studies of hosts of type II SNe, despite coming from multiple surveys with different selection functions. Their metallicity distribution is also consistent with a distribution of star formation calculated from galaxy population statistics ( 3.4.4). § We discuss in 3.4.5 how our study avoids some selection effects due to supernova § type and host galaxy type that might influence the metallicity distribution. A key future use of the metallicity distribution of type II SNe we find here will be to evaluate possible metallicity dependence of other subclasses of CCSNe. We discuss the advantages and disadvantages of the metallicity diagnostic we choose for this study in 3.4.6. Finally, we fit a relationship between oxygen and iron abundances § in 3.4.7, and convert our observed oxygen abundance distribution into an assumed § iron abundance distribution, as iron is more important to the evolution of massive stars than oxygen.

3.4.1. How representative is the metallicity sample?

To investigate how representative the subsample for which we have spectra and metallicities is of the entire sample of PTF type II SN hosts we compared the distributions of the two samples in galaxy mass, characteristic stellar age, and star formation rate, using the 32 (47) hosts with (and without) measured metallicities that also lie in the SDSS DR8 imaging footprint. Figures 3.4 and 3.5 show that the

59 two sub-samples have essentially identical distributions in host mass (K-S probability

99.9%), age (57.8%), and SFR (64–74%, depending on the SFR estimation method).

We also investigated the eﬀects of redshift on the completeness of the sample by dividing it into lower and higher redshift subsamples and comparing the properties of the two. In Figure 3.6 we show the metallicity distribution of these two subsamples.

A K-S test indicates that the two have a 19% probability of being drawn from the same metallicity distribution, consistent at approximately 1σ. The hosts in the two redshift bins have essentially identical distributions in host mass (K-S probability

63%), age (91%), and SFR (91–99%, depending on the estimation method).

3.4.2. The type II metallicity sample in context

We next place our type II host sample in context by comparing it to galaxy properties in the DR7 SDSS MPA/JHU value-added catalog (Kauﬀmann et al. 2003;

Tremonti et al. 2004; Brinchmann et al. 2004; Salim et al. 2007). We compare to the subset of the DR7 objects which have redshifts within the range of our sample, successful estimates of the stellar mass and star formation rate, a 12+log(O/H) metallicity estimate, and an [N II]λ6584/Hαλ6563 ﬂux ratio within the valid range for the PP04 N2 metallicity diagnostic. This last requirement has almost no eﬀect, reducing the sample by only 0.6%. The strictest condition by far is the requirement of a valid Tremonti et al. (2004) metallicity estimate, as shown in Table 3.7. Note that the galaxy properties in the MPA/JHU value-added catalog are a function of

60 redshift because it is not a volume-limited sample. The selection that deﬁnes the catalog is reﬂected in the properties of its constituents.

As shown in Figure 3.7, the SN hosts appear to trace the MPA/JHU sample well. Their mass–metallicity relationships are consistent at approximately one sigma. Strong conclusions should not be drawn from this, as the sample selection functions of the PTF type II host galaxies and the SDSS galaxy mass–metallicity sample of the MPA/JHU value-added catalog are each complex and based in part on parameters unrelated to the galaxies themselves. The fiber allocation in SDSS prioritized galaxies lower than other target classes, including brown dwarfs and quasar candidates (e.g. Blanton et al. 2003). This means that the selection function of galaxies observed spectroscopically in a given field is mediated by the density of other targets in that field rather than being a simple function of the properties of the galaxies themselves. To zeroth order, SDSS galaxy spectroscopic observations are a function of stellar mass, while core-collapse supernovae (and by extension our host sample) are instead a function of star formation rate in the recent past.

Inclusion in the mass–metallicity sample of the MPA/JHU value-added catalog is not a simple function of the galaxy having been spectroscopically observed. In order to be included, the galaxy must have a reasonable ﬁt using the group’s Bayesian metallicity determination. The strength of the spectroscopic features that enable this determination is greater for galaxies with a higher star formation rate, which means that the star formation rate is to a certain extent a hidden parameter in

61 this selection function. The PTF followup selection function is unpublished, and therefore impossible to model. Reconciling the selection functions of these samples suﬃciently to draw strong conclusions based on comparing their mass–metallicity slopes is not possible, nor is it the aim of this study. Instead, we compare them to place our results in context.

The SDSS galaxy spectra are primarily nuclear spectra but not exclusively so. In contrast, 50% (17/34) of our type II host metallicity measurements are of locations more than 3 arcsec away from the nucleus, as can be seen in Table 3.2. The

SN location was within 3 (4,6) arcsec of the galaxy center in all but 5 (3,1) of the remaining cases. In one case (PTF09aux), we were unable to measure [N II] and Hα with suﬃcient S/N to measure metallicity. Type II SNe have no known metallicity dependence, so they are not expected to favor the outer, more metal-poor regions of galaxies.

Although quantitative conclusions should not be drawn from this similarity seen in Figure 3.7 due to these unavoidable sample selection incongruities, some qualitative conclusions can be drawn. The type II SN hosts do not appear to be biased toward lower metallicities at a given mass (left panel) as a simplistic interpretation of the results of Mannucci et al. (2010) might suggest. It has been suggested that core-collapse SNe and GRBs may be less frequently observed in higher metallicity environments than in lower metallicity environments due to higher extinction (e.g. Maiolino et al. 2002; Mannucci et al. 2003; Cresci et al. 2007; Campisi

62 et al. 2011), but this sample does not show evidence for such a bias. The SN hosts do, however, appear to be biased toward higher star formation rates3 (right panel) than the MPA/JHU galaxy sample, as would be expected if they do indeed trace star formation. They appear to trace the distribution of the MPA/JHU sample well in galaxy mass and star formation rate, as shown in Figure 3.8.

3.4.3. Comparing with other SNe host samples

Work on the metallicity distribution of supernova hosts by Prieto et al. (2008) and

Kelly & Kirshner (2012) looked at overall galaxy metallicity with serendipitous

SDSS spectra, without isolating the SN site. Kelly & Kirshner (2012) subdivide their sample into those discovered by galaxy-impartial searches and by targeted searches.

Studies by Anderson et al. (2010) tried to measure abundances at the SN site or at a similar galactocentric radius, as we have done in this study. These previous studies had uniform spectroscopy but the source SN samples were heterogeneous, including

SNe discovered in a wide variety of ways with very different selection effects. Our source SN sample is from a single survey with uniform selection. The source survey is areal rather than galaxy-targeted, which enables the detection of events in the lowest-mass galaxies, removing or at least mitigating a possible bias towards high metallicity environments which we expect exists in prior supernova host samples. We do not attempt to define a volume-limited supernova sample here. Rather, we point

3Here we use the SFR calculated from the u-band luminosity with aperture corrections.

63 out that this existing sample represents a substantial step forward from previous similar samples for the purpose of providing a comparison for a non-matched sample of rare core-collapse events, particularly one thought to depend on metallicity such as GRBs or abnormally luminous CCSNe.

Kelly & Kirshner (2012) used abundances following Tremonti et al. (2004) and the O3N2 method of Pettini & Pagel (2004). In this paper we use the N2 method of Pettini & Pagel (2004), so we must convert these to a common scale. The valid range of the conversion deﬁned by Kewley & Ellison (2008) from the scale of the

O3N2 method to the scale of the N2 method does not span the abundances here, so we omit three of the 124 hosts in the sample, and convert from O3N2. Prieto et al.

(2008) uses abundances from the method of Tremonti et al. (2004); we omit the

14 of 152 hosts in the sample that have T04 metallicities above or below the valid conversion range deﬁned by Kewley & Ellison (2008).

We compare our type II host metallicity distribution with those found by

Prieto et al. (2008), Kelly & Kirshner (2012) targeted (T) and galaxy-impartial

(I), and Anderson et al. (2010) in Figure 3.9. The K-S test probabilities that our type II sample could be selected from the same underlying distribution as those in the earlier studies are 43%, 68%, 4%, and 21%, respectively. The agreement of our

SN metallicity distribution with the results of these prior studies is striking, given the diﬀerent sample selection. The most diﬀerent sample (at a K-S probability of

64 only 4%) is the one which would seem to be the most similar: the heterogeneous galaxy-impartial sample from Kelly & Kirshner (2012).

3.4.4. Comparing with SFR metallicity distributions from galaxy

population statistics

One semi-observational way of determining the global distribution of star formation as a function of metallicity is to combine the observed galaxy mass function, the observed mean star formation rate as a function of galaxy mass, and the observed galaxy mass–metallicity relationship. Stanek et al. (2006) combines the 2MASS and

SDSS galaxy stellar mass function from Bell et al. (2003) with the Tremonti et al.

(2004) mass–metallicity relation and the Brinchmann et al. (2004) star formation rate density. Niino (2011) calculates the metallicity distribution of star formation two diﬀerent ways; ﬁrst, observationally from the stellar mass function, the galaxy

M–SFR relation, and the galaxy mass–metallicity relation, nearly identically to Stanek et al. (2006) except using the mass–metallicity relation of Savaglio et al. (2005), and second, calculated using the relationship between galaxy mass, metallicity, and SFR defined by Mannucci et al. (2010), and assigning metallicities based on SFR as well as mass. Each of the three observed relationships that are inputs to these alternate methods of finding the metallicity distribution of star formation has its own set of selection effects. Potential biases or redshift-dependent effects in the samples used to define the relation could offset the distribution in

65 metallicity. The width of the distribution may be misrepresented by using a mean relationship to translate from one property into another, such as the mass–metallicity relationship (Tremonti et al. 2004) or the three-way relationship between mass, metallicity, and star formation rate (Lara-L´opez et al. 2010; Mannucci et al. 2010), because the scatter around that relationship may not be carried through to the ﬁnal distribution.

In Figure 3.10 we plot our distribution against those of Stanek et al. (2006) and Niino (2011) based on galaxy population statistics. We convert the metallicities using Kewley & Ellison (2008). The non-monotonicity of the conversions from

T04 and KK04 to PP04N2 results in non-physical double-values in the cumulative distributions. On the right, metallicity conversions are done by inverting the reverse conversions, as described in Appendix A. This maintains the physicality of the distribution function, but may increase inaccuracy at higher metallicities.

Because the forward and reverse conversions do not match, multiple conversions between metallicity scales will compound errors. This is likely the reason why the distribution from Stanek et al. (2006) (in green) does not match the distribution from Niino (2011) which considers the mass–metallicity relation independently of star-formation (in red). Instead of using Tremonti et al. (2004), Niino (2011) uses the mass–metallicity relation of Savaglio et al. (2005) on the KK04 scale, though they are otherwise identical.

66 Core-collapse SNe, as the deaths of massive, young stars, are a relatively good tracer of SFR. Using the metallicity distribution of a uniform sample of type II SN sites to approximate the metallicity distribution of star formation, as we have done, should have almost completely independent selection eﬀects (such as extinction from dust) from methods relying on galaxy population statistics.

3.4.5. Selection eﬀects

One of the primary potential sources of incompleteness in using type II SNe environments to trace the metallicity distribution of global star formation will of course be the selection of the sample of type II SNe. An ideal sample for this purpose would be a complete, volume-limited sample, monitoring a ﬁxed region of sky for a ﬁxed period of time, and then eliminating events outside the complete sample.

Up until very recently, most supernova surveys have monitored large, luminous galaxies rather than regions of the sky, a methodology which has the potential to miss any SNe in the very lowest end of the galaxy luminosity function. The Palomar

Transient Factory survey is areal, which removes the potential bias against extremely low-mass host galaxies of targeted surveys. Because the survey selection is not yet published, however, we are unable to correct for any biases in our source sample to get a distribution for a volume-limited sample. With the current rapid growth in depth and breadth of supernova surveys, there is great potential for this method of determining the metallicity distribution of star formation.

67 Another potential source of bias for this method is any dependence of the likelihood of a massive star resulting in a type II SN on metallicity. Type II-P

SNe make up around 70% of all type II SNe in the LOSS survey, which focuses on relatively luminous galaxies (Li et al. 2011). The relative frequency of type II SNe has not yet been found to depend on metallicity, unlike type Ib and Ic SNe. We examine only the distribution of type II hosts, for which we have good statistics.

The type II distribution is consistent with the distribution of the nine type Ib/Ic/IIb hosts we have measured with a K-S probability of 23%, and the distribution of the hosts of all CCSNe we have measured (including all the type II hosts) is consistent with the type II distribution with a K-S probability of 99%, shown in Figure 3.11

(left). Completeness corrections to translate between type II hosts and all CCSN hosts may depend slightly on metallicity.

Several types of CCSNe are known to vary in frequency with metallicity, as discussed in 3.1. Our very small sample of spectroscopic metallicity measurements § of hosts of type IIb/Ib/Ic SNe is statistically consistent with these previous results (Modjaz et al. 2011; Anderson et al. 2010; Kelly & Kirshner 2012), but not at very high significance. Using the host galaxy properties we fit from SDSS photometry gives us slightly better statistics, shown in Figure 3.11 (right). The overrepresentation of types IIb and Ic-BL in low-mass hosts is consistent with the results of Arcavi et al. (2010) of this same sample based on host galaxy luminosities, but again, not at very high significance. The relative distributions of type II, IIb, Ib,

68 Ic, and Ic-BL in photometrically calculated host mass are consistent with the recent results of Kelly & Kirshner (2012).

3.4.6. Strong-line metallicity diagnostics

There is a substantial literature on the merits and disadvantages of each commonly- used method of determining metallicities based on ﬂuxes of strong emission lines (e.g.

Berg et al. 2011; Kewley & Ellison 2008, and references therein). These methods all rely on simplifying assumptions about the H II regions being examined: uniformity of electron density, cooling dominated by oxygen (implying that other cooling species have abundances that vary in lockstep with oxygen), and ionization-bounded H II regions (e.g. Pagel et al. 1979). The methods can be classiﬁed into rough categories: direct methods, which rely on estimates of the electron temperature and require measurements of faint auroral lines such as [O III]λ4363A,˚ empirical (e.g. Pettini

& Pagel 2004), theoretical (e.g. Kobulnicky & Kewley 2004), and a combination of empirical and theoretical (e.g. Denicol´oet al. 2002). All these methods are based on high S/N measurements of the line ratios of diﬀerent combinations of emission lines from ions present in the optical region of the spectrum ( 3700 6800A:˚ [O II], ≃ − [O III], [N II], [S II], Hα, and Hβ).

The slope and intercept of the galaxy mass–metallicity relationship is diﬀerent for each diagnostic (Kewley & Ellison 2008), which is a relatively straightforward way to demonstrate that they are not all directly measuring some platonic ideal of a

69 fundamental oxygen abundance measurement. (This problem is independent of the separate question of the exact value of the solar oxygen abundance, which also affects how measured gas-phase abundances map to stellar abundances.) Instead, each technique measures a different quantity that correlates well with oxygen abundance, but does not directly map to it. The simplifying assumptions that allow us to use each strong-line indicator to estimate oxygen abundance are not perfect for all H II regions. Rigorously selecting the best diagnostic for a given situation requires better data than are achievable for distant and faint targets, and doing a case-by-case selection of strong-line method on insufficient data would introduce its own biases.

The primary advantage of the strong-line techniques is that they are possible with fewer photons, and are therefore feasible to perform on large samples for good population statistics. Given the scale diﬀerences between methods, however, it is crucially important to ensure that all metallicities one is comparing are on the same scale. Where possible, we do this by natively determining the metallicities in a common scale. Where impossible, we convert a metallicity determined on another scale using the empirical conversions of Kewley & Ellison (2008) (but see

Appendix A). We emphasize that any meaningful conclusion should not be aﬀected by a change in the metallicity calibration.

The primary scale we choose for this study is the N2 diagnostic of Pettini &

Pagel (2004), which depends solely on [N II]λ6584/Hαλ6563. Three disadvantages of the method are that it shows a larger dispersion compared to the direct (Te) method

70 than most other strong line diagnostics, that it has a dependence on the ionization parameter that becomes important at low metallicities (PP04N2 12+log(O/H)

< 8.0) (L´opez-S´anchez et al. 2012), and that it loses sensitivity and saturates at high metallicities (PP04N2 12+log(O/H) > 8.86). There are a number of key advantages of this diagnostic, however. The ratio is very insensitive to reddening due to the close wavelength proximity of the lines. It is monotonic with oxygen abundance.

It depends on lines with relatively high fluxes in star forming environments, which means that good metallicity estimates can be achieved at relatively low observational expense. Yin et al. (2007) find it is more consistent with Te methods than the O3N2 diagnostic of Pettini & Pagel (2004), and Bresolin et al. (2009) compare a variety of strong-line abundance estimators and find that PP04N2 is the closest match in both slope and normalization to the oxygen abundance gradient in NGC 300 measured with the Te method and measured with stellar metallicity (blue supergiants).

3.4.7. Iron abundances

Iron is more important than oxygen for the late-stage evolution of massive stars, because iron provides much of the opacity for radiation-driven stellar winds (e.g.

Pauldrach et al. 1986; Vink & de Koter 2005). Unfortunately, gas-phase iron abundances are diﬃcult to measure, and the fraction of iron depleted onto grains is highly variable. Even within our own galaxy, measuring iron abundances is challenging (e.g. Rodr´ıguez 2002; Jensen & Snow 2007; Okada et al. 2008). We

71 measure gas-phase oxygen abundances instead as a proxy for metallicity because it is observationally feasible.

Because iron is the dominant opacity source, we would like to estimate the iron abundances implied by the observed oxygen abundances. To do this we make use of three correlations: ﬁrst, the PP04N2 diagnostic, which uses a function of the ﬂux ratio of the Hα λ6563 and [N II]λ6584 lines that correlates well with direct method

(Te) measures of gas-phase oxygen abundance; second, the tight correlation of direct method measures of gas-phase oxygen abundance with stellar oxygen abundance; third, the correlation between stellar oxygen and stellar iron abundances.

Bresolin et al. (2009) ﬁnd that PP04N2 is the closest match in both slope and normalization to the oxygen abundance gradient in NGC 300 measured with Te methods, with an average error of approximately 0.1 dex. Although PP04N2 is a good match to direct-method gas-phase oxygen abundance, it is not perfect, and a small correction to slope and zero point could be made. For the purpose of this paper, however, we assume that PP04N2 maps precisely to direct-method oxygen abundances. Bresolin et al. also ﬁnd that the gas-phase oxygen abundance gradient measured with Te methods correlates very tightly with the stellar oxygen abundance gradient, with an error of no more than about 0.03 dex. For the purposes of this paper, we assume that the correspondence between Te gas-phase oxygen abundance and stellar oxygen abundance is one-to-one, which is a very good assumption.

72 Stars that have low oxygen abundances have even lower iron abundances. At low metallicity, α-elements like oxygen are enhanced relative to iron compared to the solar mixture. In the galactic disk and halo, at [Fe/H] > 1, [O/Fe] is approximately − inversely proportional to [Fe/H], as can be seen in the left panel of Figure 3.12, while below [Fe/H] = 1, [O/Fe] may ﬂatten out at a constant (and lower) relative iron − abundance (e.g Tinsley 1979; McWilliam 1997; Johnson et al. 2007; Epstein et al.

2010) (but see e.g. Israelian et al. 1998).

To estimate the conversion between stellar oxygen and iron abundance, we compared [O/H] to [Fe/H] over a wide range in metallicities using stellar abundance measurements from the Milky Way bulge, disk, and halo, from Fulbright et al.

(2007); Rich et al. (2007); Rich & Origlia (2005); Lecureur et al. (2007); Reddy et al. (2003, 2006); Bensby et al. (2004); Chen et al. (2003). We ﬁt the relationship between [O/H] and [Fe/H] with an unweighted linear ﬁt, as seen in Figure 3.12, and

ﬁnd

[Fe/H] = c1 + c2([O/H]), (3.1)

where c1 = 0.34 0.01 and c2 = 1.25 0.05. Although the eye is drawn to a − ± ± steeper trend at higher metallicity than the formal fit shown, this is misleading and based on relatively few points; the fit is driven to be flatter by a dense concentration of points with 0.2 < [O/H] < 0.2 and 0.5 < [Fe/H] < 0. The relationship does − − not differ substantially between bulge stars (shown in red) and halo and disk stars

73 (shown in blue). If [O/Fe] flattens out below [Fe/H] = 1, the linear relationship − we choose to fit may not extend to lower metallicities. We would expect to find a slightly steeper relationship were we to exclude points below [Fe/H] = 1, which − would mean that type II SN progenitors in low oxygen abundance host regions have even lower iron abundance than we find here. Because there is finite scatter in the measured relationship, however, imposing a strict cut at [Fe/H] = 1 actually drives − the fit to be slightly flatter by biasing the points with lowest [O/H] to higher [Fe/H].

The exact value of the slope is not critically important; the main point is that low oxygen abundances imply even lower iron abundances, as can easily be seen in the right side of Figure 3.12. This is equivalent to saying that at low metallicities, alpha elements are enhanced relative to iron.

Stellar abundances are measured relative to solar, while gas-phase abundances are (nominally) absolute. Applying the ﬁt to a direct conversion between

12+log(O/H) and [Fe/H] therefore requires assuming a solar oxygen abundance.

There is currently some dispute over the solar abundance because results from atmospheric and interiors methods diﬀer. For a given solar oxygen abundance O⊙,

[Fe/H] = c1 c2O⊙ + c2(12 + log(O/H)). (3.2) −

Using a solar oxygen abundance of O⊙ = 8.86 (Delahaye & Pinsonneault 2006), the conversion is [Fe/H] = 11.4 + 1.25(12 + log(O/H)), while using O⊙ = 8.69 (Asplund − et al. 2009), the conversion is [Fe/H] = 11.2 + 1.25(12 + log(O/H)). − 74 We apply this ﬁt to transform our oxygen abundance distribution of type II progenitors into an iron abundance distribution, shown in Figure 3.13. The median value of [Fe/H] is 0.60 using the solar value of Delahaye & Pinsonneault (2006). If − a diﬀerent solar oxygen abundance O⊙ is assumed, the calculated iron value shifts by

c2(8.86 O⊙), (3.3) −

so using O⊙ = 8.69 (Asplund et al. 2009), for example, the median value of [Fe/H] is

0.39. −

The most important result here is that the gap between the iron and oxygen abundances is 50% wider at the low-metallicity end of the distribution than at the high-metallicity end, even though this sample spans less than a dex in abundance.

Were we to assume that [O/Fe] flattens out below [Fe/H] = 1 instead of remaining − linear, the slope we fit would be steeper and the difference would be even greater.

A striking outcome of this translation is that all of the type II SN progenitors in this sample appear to have sub-solar iron abundances. Although this is notable, it is not entirely surprising; the sun is more enhanced in iron than most Milky Way stars at its oxygen abundance, and all of the host galaxies in this sample are smaller than the Milky Way. It is, however, not a secure result. Given the scatter in the correlations we use to deﬁne this relation, we caution that conversions of average values are uncertain at the level of at least 0.15 dex, and conversions of individual values are uncertain at the level of approximately 0.3 dex.

75 The type II progenitors all have iron abundances greater than [Fe/H] = 1.5, − putting them squarely in the regime where winds are primarily driven by iron

−3 opacity. For the most metal-poor stars (Z/Z⊙ < 10 ), non-iron elements such as carbon dominate the radiative driving (Vink & de Koter 2005), but in the metallicity range of these type II SN progenitors, iron abundance should still be the dominant factor which determines wind strength and mass loss.

3.5. Conclusions

The primary result of this paper is a new progenitor region metallicity distribution for a uniform (though not completely unbiased) sample of type II SNe. Understanding the underlying distribution is important for understanding any possible metallicity dependences of diﬀerent types of events associated with massive stars, and it can serve as a probe of the metallicity distribution of star formation.

The host galaxies of our type II sample appear to trace galaxies from the

MPA/JHU value-added catalog in mass and metallicity, showing a slight bias towards higher star formation rates.

We ﬁnd a similarity between the existing host metallicity distributions for heterogeneous type II supernova samples and the metallicity distribution we derive.

Because the existing host metallicity distributions are based on supernova samples that are drawn predominantly from galaxy-targeted supernova searches, one might

76 naively expect these previous distributions might be biased towards higher mass and therefore higher metallicity galaxies. We do not ﬁnd such a trend.

Comparing to the metallicity distribution of star formation rather than to the metallicity distribution of galaxies as a function of mass is the correct way to evaluate a possible metallicity dependence of a transient population associated with young stars. We point out that using CCSNe to trace star formation leads to an almost entirely independent way of probing the metallicity distribution of star formation from methods involving galaxy population statistics, and we compare the metallicity distribution we derive to one of these.

Finally, we present our host metallicity distribution in terms of iron abundance, by converting our oxygen abundance distribution to an iron abundance distribution using the α/Fe relationship observed in Milky Way bulge, disk, and halo stars, noting that iron is more important than oxygen for the late-stage evolution of massive stars.

We show that even though these hosts span less than a dex in oxygen abundance, the gap between their iron abundance and oxygen abundance nearly doubles at the low-metallicity end compared to the high-metallicity end. We estimate that

1.2 < [Fe/H] < 0 for these type II SN progenitors. Though all may have sub-solar − iron abundance, none are metal-poor enough that elements other than iron will dominate the wind-driving opacity of the progenitor star.

77 Future improvements to this estimate of the metallicity distribution of type II

SNe can be made by performing completeness corrections for any selection or followup biases in the source survey. If the peak luminosity of type II SNe is found to depend on host galaxy metallicity, there may also be Malmquist-like biases to correct.

78 Fig. 3.1.— Nine type II SN hosts, spanning the metallicity and mass range, arranged so mass increases toward the right and oxygen abundance increases toward the top. Each panel is scaled to a physical size of 50 kpc (H0, Ωm, ΩΛ = 70, 0.3, 0.7) and is centered on the position of the supernova. All images are from SDSS and were taken before the supernovae.

79 Fig. 3.2.— Cumulative distribution of local gas-phase oxygen abundances for type II SNe. As type II SNe trace young stellar populations, this traces the metallicity distribution of star formation at low redshift. The solid black distribution uses the N2 diagnostic of Pettini & Pagel (2004, PP04) we adopt as our standard. Subsequent ﬁgures only use these measurements. The other distributions show how the result would change for other strong-line metallicity diagnostics, based on the empirical conversions of Kewley & Ellison (2008). The dotted orange curve uses the PP04 O3N2 diagnostic, the short-dashed green curve uses Denicol´oet al. (2002), the long- dashed blue curve uses Tremonti et al. (2004), and the dot-dashed purple curve uses Zaritsky et al. (1994). We do not show conversions to the scales of Kobulnicky & Kewley (2004), McGaugh (1991), or Kewley & Dopita (2002), which require external branch information.

80 Fig. 3.3.— The two methods of estimating SFR are consistent. SFR estimates based on u-band photometry (Salim et al. 2007) are plotted against SFR estimates based on FAST for the full sample of PTF type II SN hosts (open red points) and the metallicity subsample (solid black points). The blue dashed line represents a 1:1 correspondence.

81 Fig. 3.4.— The distributions of the full PTF type II sample (dotted red) and those for which we obtained metallicities (solid black). Section 3.3.2 describes how the host properties were estimated. The subsample for which we have spectroscopic metallicity measurements is quite representative of the full sample, with K-S test probabilities of 99.9% (mass) and 57.8% (age) that they are drawn from the same distribution.

Fig. 3.5.— The subsample of type II hosts for which we have measured metallicities (solid black) is representative of the full sample of PTF type II SN hosts (dotted red) in SFR based on u-band photometry (Salim et al. 2007, left) and FAST (right).

82 Fig. 3.6.— The metallicity distribution of the type II hosts separated into two redshift bins of seventeen hosts each (left). The blue solid (red dotted) line is the lower (higher) redshift half of the sample, 0.019 z 0.035 (0.037 z 0.11). On the right, the distribution is expanded and metallicities≤ ≤ are plotted agai≤ nst≤ redshift. Here the lower redshift half of the sample is blue solid points, and the higher redshift is red open points. A K-S test shows the two are consistent with being drawn from the same distribution in metallicity at the 19% level. There do not appear to be any major selection eﬀects with redshift.

83 Fig. 3.7.— Measured host metallicities (black points) as a function of galaxy mass (left) or SFR (right), overlaid on the distribution in the MPA/JHU value-added catalog in the same redshift range (blue contours). Our hosts are slightly oﬀset to higher star formation rates, as we would expect if type II SNe trace star formation.

84 Fig. 3.8.— Host mass and SFR for our type II SN hosts with Z measurements (large black points), and all ﬁrst-year PTF type II SNe hosts in the SDSS photometric footprint (small red points), overlaid on the distribution in the MPA/JHU catalog (blue contours).

85 Fig. 3.9.— Distribution of local gas-phase oxygen abundance of our sample of type II SNe (black solid line) compared with existing SN host metallicity samples. The dotted orange curve shows the heterogeneous type II subsample of Anderson et al. (2010). The K-S probability of this sample being drawn from the same underlying distribution as our PTF type II sample is 11%. Similarly, the green long-dash curve shows the distribution of type II, II-P, and IIn SNe from Prieto et al. (2008) (K-S probability 87%) and the purple dash-dot curve shows the type II sample from Kelly & Kirshner (2012) (K-S probability 45%). In the latter two cases we converted their T04 scale metallicity to the PP04N2 scale using the conversion given in Appendix A.

86 Fig. 3.10.— The distribution of star formation as a function of metallicity from galaxy population statistics from Stanek et al. (2006) (green dotted line) and Niino (2011) with (blue long-dashed line) and without (red short-dashed line) incorporating the star-formation-rate dependence of the mass–metallicity relationship as Mannucci et al. (2010), compared to our distribution of site metallicities of type II CCSNe (black solid line). On the left, the metallicity conversions were made with Kewley & Ellison (2008). The non-monotonicity of the conversions from T04 and KK04 to PP04N2 results in the non-physical double-values. On the right, conversions are done by inverting the reverse conversions, as described in Appendix A. This maintains the physicality of the distribution function, but may increase inaccuracy at higher metallicities.

87 Fig. 3.11.— The metallicity distribution (left) of type II SN hosts (solid black), all other CCSN hosts (dashed orange) and all CCSN hosts in Table 3.2 (dotted green). With this small sample of other subtypes, the type II distribution is statistically consistent with the other CCSNe (K-S probability 23%) and with all CCSNe (K- S probability 99%). Increasing the sample size, the distribution in photometrically calculated host galaxy mass (right) of type II SN hosts (black solid), Ic-BL (pink dotted), IIb (green short-dashed), Ib (blue long-dashed), and Ic (red dot-dashed). These distributions are consistent with the overrepresentation of IIb and Ic-BL at low host luminosity found by Arcavi et al. (2010) and with the distributions in host mass found by Kelly & Kirshner (2012).

88 Fig. 3.12.— To translate oxygen abundances into iron abundances, we fit a linear relation (black) to the iron and oxygen abundances of Milky Way bulge, disk, and halo stars. At low metallicity, α-elements like oxygen are enhanced relative to iron compared to the solar mixture. On the left is the well-known relationship between [O/Fe] and [Fe/H]. The red open points are iron and oxygen abundances of bulge stars (Fulbright et al. 2007; Lecureur et al. 2007; Rich & Origlia 2005; Rich et al. 2007). The blue solid points are halo and disk stars (Bensby et al. 2004; Chen et al. 2003; Reddy et al. 2003, 2006). On the right we express this in terms of [O/H] and [Fe/H] and fit the relation. Although the eye is drawn to a steeper trend at higher metallicity than the formal fit, this is an illusion based on relatively few points; the fit is driven to be flatter by a dense concentration of points with 0.2 < [O/H] < 0.2 and 0.5 < [Fe/H] < 0. The blue dashed line has a slope of 1 to− guide the eye and intersects− with the fit at [O/H] = 0, showing that iron varies more steeply than oxygen. If the slope is steeper than we have fit, this conclusion strengthens. Equation 3.2 can be used to conservatively convert a measured gas-phase oxygen abundance to iron abundance for a given solar oxygen abundance (modulo the uncertainties of equating gas-phase strong-line oxygen abundance indicators to stellar oxygen abundances). This conversion is necessary because at low metallicities, alpha elements such as oxygen are enhanced relative to iron, and iron is the main opacity source for line- driven winds and thus may drive mass loss for supernova progenitors.

89 Fig. 3.13.— The estimated [Fe/H] distribution of the type II SN sites. The red dashed line is the [O/H] distribution, and the blue solid line is the [Fe/H] distribution assuming the solar oxygen abundance is 8.86 (Delahaye & Pinsonneault 2006). The arrows indicate the shift corresponding to assuming a solar oxygen abundance of 8.69 (Asplund et al. 2009). The green dotted line is the oxygen abundance shifted left by 0.35 to line up with the iron at the high-metallicity end. Notice that the gap between the iron and the oxygen abundance is 50% wider at the low-metallicity end of the distribution than at the high-metallicity end. Iron varies more steeply than oxygen, and a galaxy that has low oxygen abundance has even lower iron abundance.

90 Telescope Instrument slit width ruling λ coverage resolution (arcsec) (lines/mm) (A)˚ (A)˚

APO DIS 1.5 B400/R300 (gratings) 3500–9800 7 91 du Pont WFCCD 1.7 400 (blue grism) 3700–9200 7 Hiltner OSMOS 1.2 704 (grism) 3960–6870 3 SDSS ﬁber spectrograph 3 (dia) B640/R440 (grisms) 3800–9200 3

Table 3.1. Observation properties SN Name z type Hα λ6563a [N II]λ6584a 12+log(O/H) [Fe/H] SN radiusb SN radiusc Source (PP04N2) (8.86) (arcsec) (kpc)

PTF09awk 0.0620 Ib 717 3 126 3 8.42 0.03 0.90 *0.10 0.11 SDSS ± ± ± − PTF09bce 0.0230 II 117 3 45 4 8.72 0.10 0.51 5.93 2.76 OSMOS ± ± ± − PTF09bcl 0.0620 II 117 9 50 8 8.77 0.21 0.45 d d APO ± ± ± − ··· ··· PTF09bgf 0.0310 II 643 10 38 11 8.18 0.34 1.19 1.17 0.72 duPont ± ± ± − PTF09cjq 0.0190 II 2982 25 1107 25 8.70 0.03 0.54 14.24 5.49 APO ± ± ± −

92 PTF09cu 0.0570 II 60 3 20 3 8.66 0.20 0.59 9.14 10.10 APO ± ± ± − PTF09dah 0.0238 IIb 207 4 31 4 8.37 0.15 0.95 2.32 1.12 duPont ± ± ± − PTF09dfk 0.0160 Ib 601 4 65 4 8.30 0.07 1.05 1.25 0.41 duPont ± ± ± − PTF09dra 0.0770 II 145 3 55 3 8.72 0.07 0.52 *3.63 5.29 SDSS ± ± ± − PTF09due 0.0290 II e e 8.77 0.05 0.45 9.91 5.76 APOgrad ··· ··· ± − PTF09dxv 0.0330 IIb 277 4 88 4 8.63 0.06 0.63 6.26 4.12 duPont ± ± ± − PTF09ebq 0.0235 II 3304 14 1175 13 8.68 0.01 0.56 0.69 0.33 duPont ± ± ± − PTF09ecm 0.0285 II 149 3 55 4 8.70 0.08 0.54 5.17 2.96 duPont ± ± ± −

Table 3.2. Measured host metallicities (cont’d) Table 3.2—Continued

SN Name z type Hα λ6563a [N II]λ6584a 12+log(O/H) [Fe/H] SN radiusb SN radiusc Source (PP04N2) (8.86) (arcsec) (kpc)

PTF09fbf 0.0210 II 571 3 85 3 8.37 0.04 0.95 4.55 1.93 duPont ± ± ± − PTF09fma 0.0310 II 183 5 57 6 8.62 0.13 0.64 d d duPont ± ± ± − ··· ··· PTF09fmk 0.0631 II 451 7 166 8 8.70 0.06 0.54 3.68 4.47 duPont ± ± ± − PTF09fqa 0.0300 II 43 1 7 1 8.37 0.22 0.95 10.76 6.46 duPont ± ± ± − PTF09fsr 0.0079 Ib 296 4 93 4 8.63 0.06 0.63 64.61 10.55 duPont ± ± ± −

93 PTF09g 0.0400 II 187 3 62 3 8.65 0.06 0.60 *3.83 3.03 SDSS ± ± ± − PTF09gof 0.1030 II 62 3 17 3 8.56 0.20 0.72 1.90 3.59 duPont ± ± ± − PTF09iex 0.0200 II 278 7 18 8 8.20 0.56 1.17 4.43 1.79 duPont ± ± ± − PTF09ige 0.0640 II 322 2 97 2 8.61 0.02 0.65 *5.10 6.28 SDSS ± ± ± − PTF09igz 0.0860 II 72 3 18 3 8.53 0.19 0.75 1.69 2.72 APO ± ± ± − PTF09ism 0.0290 II 100 2 33 2 8.65 0.08 0.61 *7.47 4.34 SDSS ± ± ± − PTF09q 0.0900 Ic 201 4 92 4 8.81 0.06 0.40 *2.88 4.84 SDSS ± ± ± − PTF09r 0.0270 II 63 3 22 3 8.68 0.18 0.57 0.79 0.43 OSMOS ± ± ± − PTF09sh 0.0377 II 362 9 90 9 8.53 0.12 0.75 9.73 7.27 APO ± ± ± −

(cont’d) Table 3.2—Continued

SN Name z type Hα λ6563a [N II]λ6584a 12+log(O/H) [Fe/H] SN radiusb SN radiusc Source (PP04N2) (8.86) (arcsec) (kpc)

PTF09sk 0.0355 Ic-BL 717 3 73 2 8.28 0.04 1.06 *2.77 1.95 SDSS ± ± ± − PTF09t 0.0390 II 775 9 114 9 8.37 0.10 0.96 5.84 4.51 duPont ± ± ± − PTF09tm 0.0350 II 232 4 109 4 8.83 0.04 0.38 *3.64 2.53 SDSS ± ± ± − PTF09uj 0.0651 II 85 2 26 2 8.61 0.10 0.65 *2.72 3.40 SDSS ± ± ± − PTF10bau 0.0260 II e e 8.73 0.04 0.50 6.18 3.23 grad ··· e ··· e ± − 94 PTF10bgl 0.0300 II 8.63 0.06 0.63 8.19 4.92 OSMgrad ··· ··· ± − PTF10bhu 0.0360 Ic 152 2 36 2 8.51 0.08 0.78 *1.62 1.16 SDSS ± ± ± − PTF10bip 0.0510 Ic 229 6 24 5 8.29 0.25 1.05 1.24 1.23 duPont ± ± ± − PTF10con 0.0330 II 132 5 44 5 8.65 0.14 0.60 1.72 1.13 duPont ± ± ± − PTF10cqh 0.0410 II 318 7 141 7 8.80 0.06 0.42 9.17 7.43 duPont ± ± ± − PTF10cwx 0.0730 II 237 4 31 5 8.34 0.19 0.99 2.56 3.55 duPont ± ± ± − PTF10cxq 0.0470 II 286 5 31 5 8.30 0.20 1.05 1.53 1.41 duPont ± ± ± − PTF10cxx 0.0340 II 759 4 326 4 8.78 0.02 0.44 *1.84 1.24 SDSS ± ± ± − PTF10czn 0.0450 II 611 13 132 13 8.48 0.12 0.81 14.98 13.25 duPont ± ± ± −

(cont’d) Table 3.2—Continued

SN Name z type Hα λ6563a [N II]λ6584a 12+log(O/H) [Fe/H] SN radiusb SN radiusc Source (PP04N2) (8.86) (arcsec) (kpc)

PTF10hv 0.0518 II 139 2 35 2 8.54 0.06 0.74 *5.59 5.65 SDSS ± ± ± − PTF10s 0.0510 II 271 2 90 2 8.65 0.03 0.60 *1.05 1.04 SDSS ± ± ± −

a10−17 erg cm−2 s−1 A˚−1 bSN radius from the center of the galaxy. Note that all targets are observed at the SN location

95 or equivalent galactocentric radius except the 12 targets for which we use archival SDSS spectra (*starred). For these 12 targets, the distance in this column is the distance between the SN location and the fiber center, which is half an arcsec or less away from the center of the galaxy in all cases. Note that for these SDSS spectra, most SN locations are within the fiber diameter, and only one is more than 2 fiber diameters away. c Projected physical radius calculated (Wright 2006) assuming H0 = 70, Ωm = 0.3, and ΩΛ = 0.7. dPTF09bcl and PTF09fma are outside the SDSS photometry, so we do not determine the coordinates of their host galaxy centers. eFor PTF09due, PTF10bau, and PTF10bgl, the metallicity at the SN location is approximated by fitting a metallicity gradient to points at other galactocentric radii (see Table 3.3 for line fluxes and locations) and extrapolating or interpolating the metallicity at the radius of the SN. SN Name Source Radiusa Hα λ6563b [N II]λ6584b 12+log(O/H) (arcsec) (PP04N2)

PTF09due APO 13.0 276.0 1.9 105.2 2.0 8.72 0.02 ± ± ± PTF09due APO 25.0 202.2 1.6 46.6 1.5 8.51 0.04 ± ± ± PTF10bau SDSS 0.0 988.5 6.9 379.5 7.3 8.72 0.02 ± ± ± PTF10bau OSMOS 3.1 522.3 1.8 209.8 2.4 8.74 0.01 ± ± ± PTF10bau OSMOS 4.8 314.7 2.1 124.8 2.3 8.74 0.02 ± ± ± PTF10bau SDSS 10.5 565.2 1.9 215.7 2.0 8.72 0.01 ± ± ± PTF10bau OSMOS 10.6 122.8 1.8 57.1 1.9 8.82 0.04 ± ± ± PTF10bau OSMOS 12.9 152.7 1.6 60.9 1.6 8.74 0.03 ± ± ± PTF10bau OSMOS 19.8 35.9 1.2 6.6 0.7 8.43 0.14 ± ± ± PTF10bgl OSMOS 1.5 268.9 4.4 107.7 4.7 8.74 0.06 ± ± ± PTF10bgl OSMOS 5.9 164.3 3.1 61.8 3.4 8.71 0.07 ± ± ± PTF10bgl OSMOS 9.1 394.4 2.9 116.3 3.1 8.60 0.03 ± ± ± PTF10bgl OSMOS 13.7 851.3 3.0 218.5 3.2 8.54 0.02 ± ± ± PTF10bgl OSMOS 21.9 68.7 2.6 21.8 2.5 8.63 0.14 ± ± ±

aGalactocentric radius of the spectrum

b10−17 erg cm−2 s−1 A˚−1

Table 3.3. Measured line ﬂuxes and metallicities at non-supernova locations within the galaxy

96 a a a a a SN Gal RA Gal Dec fu fg fr fi fz (PTF) (J2000.0) (J2000.0) (Jy) (Jy) (Jy) (Jy) (Jy)

09aux 16:09:15 +29:17:37 1.42E-04 9E-06 5.87E-04 1E-05 1.15E-03 2E-05 1.57E-03 3E-05 1.99E-03 6E-05 ± ± ± ± ± 09awk 13:37:56 +22:55:04 8.41E-05 5E-06 1.96E-04 4E-06 2.72E-04 6E-06 3.58E-04 8E-06 3.89E-04 2E-05 ± ± ± ± ± 09axc 14:53:13 +22:14:32 2.51E-05 3E-06 9.85E-05 2E-06 1.88E-04 4E-06 2.40E-04 5E-06 2.78E-04 1E-05 ± ± ± ± ± 09axi 14:12:40 +31:04:03 3.67E-05 6E-06 1.09E-04 3E-06 1.79E-04 5E-06 1.90E-04 6E-06 1.74E-04 2E-05 ± ± ± ± ± 09bce 16:35:18 +55:38:03 1.02E-03 5E-05 4.05E-03 8E-05 8.23E-03 2E-04 1.20E-02 2E-04 1.55E-02 5E-04 ± ± ± ± ± 09bgf 14:41:38 +19:21:43 5.69E-05 5E-06 1.29E-04 3E-06 1.65E-04 4E-06 1.89E-04 6E-06 2.08E-04 2E-05 ± ± ± ± ± 97 09bw 15:05:02 +48:40:03 1.85E-06 2E-06 1.35E-05 1E-06 2.80E-05 1E-06 3.52E-05 2E-06 3.54E-05 8E-06 ± ± ± ± ± 09cjq 21:16:27 -00:49:35 2.97E-03 1E-04 1.05E-02 2E-04 2.10E-02 4E-04 2.96E-02 6E-04 3.93E-02 1E-03 ± ± ± ± ± 09ct 11:42:13 +10:38:53 1.03E-05 2E-06 2.68E-05 1E-06 5.51E-05 2E-06 7.46E-05 4E-06 9.48E-05 1E-05 ± ± ± ± ± 09cu 13:15:23 +46:25:13 3.85E-04 2E-05 1.05E-03 2E-05 1.77E-03 4E-05 2.38E-03 5E-05 2.83E-03 9E-05 ± ± ± ± ± 09cvi 21:47:09 +08:18:35 -1.11E-06 1E-06 4.06E-06 4E-07 4.63E-06 7E-07 4.97E-06 1E-06 9.74E-06 4E-06 ± ± ± ± ± 09dah 22:45:17 +21:49:17 1.80E-04 1E-05 3.79E-04 8E-06 5.11E-04 1E-05 6.63E-04 2E-05 7.64E-04 4E-05 ± ± ± ± ± 09dfk 23:09:13 +07:48:16 1.28E-04 8E-06 3.53E-04 7E-06 5.40E-04 1E-05 6.87E-04 2E-05 8.28E-04 3E-05 ± ± ± ± ± 09djl 16:33:55 +30:14:16 4.05E-06 2E-06 1.76E-05 8E-07 4.63E-05 1E-06 5.80E-05 2E-06 8.44E-05 1E-05 ± ± ± ± ±

Table 3.4. Host properties measured from SDSS photometry (cont’d) Table 3.4—Continued

a a a a a SN Gal RA Gal Dec fu fg fr fi fz (PTF) (J2000.0) (J2000.0) (Jy) (Jy) (Jy) (Jy) (Jy)

09dra 15:48:11 +41:13:31 2.40E-04 1E-05 5.98E-04 1E-05 9.95E-04 2E-05 1.34E-03 3E-05 1.63E-03 6E-05 ± ± ± ± ± 09due 16:26:53 +51:33:18 2.35E-03 1E-04 6.12E-03 1E-04 9.42E-03 2E-04 1.17E-02 2E-04 1.41E-02 4E-04 ± ± ± ± ± 09dxv 23:08:34 +18:56:19 3.99E-04 3E-05 1.17E-03 2E-05 1.94E-03 4E-05 2.77E-03 6E-05 3.55E-03 1E-04 ± ± ± ± ± 09dzt 16:03:03 +21:01:47 9.55E-05 9E-06 2.00E-04 5E-06 3.32E-04 9E-06 4.26E-04 1E-05 5.39E-04 4E-05 ± ± ± ± ± 09ebq 00:14:01 +29:25:58 5.79E-04 3E-05 1.44E-03 3E-05 2.40E-03 5E-05 3.15E-03 6E-05 4.01E-03 1E-04 ± ± ± ± ±

98 09ecm 01:06:43 -06:22:46 3.60E-04 2E-05 1.08E-03 2E-05 1.83E-03 4E-05 2.26E-03 5E-05 2.86E-03 1E-04 ± ± ± ± ± 09ejz 00:55:07 -06:57:04 5.63E-05 8E-06 1.86E-04 4E-06 4.32E-04 9E-06 6.28E-04 1E-05 8.24E-04 3E-05 ± ± ± ± ± 09fae 17:26:20 +72:56:28 2.23E-05 3E-06 3.59E-05 1E-06 3.76E-05 2E-06 4.74E-05 3E-06 8.00E-05 1E-05 ± ± ± ± ± 09fbf 21:20:38 +01:02:49 1.59E-03 8E-05 4.36E-03 9E-05 6.76E-03 1E-04 8.65E-03 2E-04 1.06E-02 3E-04 ± ± ± ± ± 09fmk 23:57:46 +11:58:44 1.65E-04 1E-05 4.44E-04 1E-05 7.74E-04 2E-05 1.21E-03 3E-05 1.40E-03 6E-05 ± ± ± ± ± 09foy 23:17:10 +17:15:04 1.99E-04 2E-05 5.79E-04 1E-05 9.82E-04 2E-05 1.34E-03 3E-05 1.50E-03 7E-05 ± ± ± ± ± 09fqa 22:25:33 +18:59:44 4.48E-04 3E-05 1.30E-03 3E-05 2.11E-03 4E-05 2.71E-03 6E-05 3.16E-03 1E-04 ± ± ± ± ± 09fsr 23:04:56 +12:19:21 1.61E-02 8E-04 6.23E-02 1E-03 1.24E-01 2E-03 1.73E-01 4E-03 2.13E-01 6E-03 ± ± ± ± ± 09g 15:16:31 +54:27:31 4.12E-04 2E-05 9.85E-04 2E-05 1.40E-03 3E-05 1.66E-03 3E-05 1.87E-03 7E-05 ± ± ± ± ±

(cont’d) Table 3.4—Continued

a a a a a SN Gal RA Gal Dec fu fg fr fi fz (PTF) (J2000.0) (J2000.0) (Jy) (Jy) (Jy) (Jy) (Jy)

09gof 01:22:25 +03:38:08 7.57E-05 8E-06 1.91E-04 5E-06 3.21E-04 8E-06 3.74E-04 1E-05 4.07E-04 3E-05 ± ± ± ± ± 09gtt 02:20:37 +02:24:24 4.74E-05 9E-06 1.39E-04 4E-06 1.78E-04 6E-06 2.73E-04 1E-05 2.85E-04 4E-05 ± ± ± ± ± 09hdo 00:15:22 +30:43:16 4.55E-04 2E-05 1.59E-03 3E-05 3.17E-03 6E-05 4.60E-03 9E-05 6.01E-03 2E-04 ± ± ± ± ± 09hzg 11:50:56 +21:11:50 3.64E-04 2E-05 1.38E-03 3E-05 2.97E-03 6E-05 4.45E-03 9E-05 6.25E-03 2E-04 ± ± ± ± ± 09iex 12:02:46 +02:24:02 8.43E-05 9E-06 1.59E-04 5E-06 1.94E-04 7E-06 2.28E-04 1E-05 3.01E-04 4E-05 ± ± ± ± ±

99 09ige 08:55:34 +32:40:01 1.39E-04 8E-06 3.49E-04 7E-06 4.98E-04 1E-05 6.26E-04 1E-05 6.35E-04 3E-05 ± ± ± ± ± 09igz 08:53:56 +33:40:10 3.04E-05 4E-06 7.07E-05 2E-06 1.04E-04 3E-06 1.29E-04 5E-06 1.42E-04 2E-05 ± ± ± ± ± 09ism 11:44:35 +10:12:46 1.98E-04 2E-05 4.85E-04 1E-05 7.06E-04 2E-05 8.73E-04 2E-05 9.28E-04 6E-05 ± ± ± ± ± 09ps 16:14:08 +55:41:41 2.18E-05 3E-06 4.35E-05 1E-06 6.19E-05 2E-06 7.92E-05 3E-06 6.72E-05 1E-05 ± ± ± ± ± 09q 12:24:50 +08:26:01 1.49E-04 1E-05 4.47E-04 1E-05 8.69E-04 2E-05 1.24E-03 3E-05 1.51E-03 5E-05 ± ± ± ± ± 09r 14:18:58 +35:23:15 4.01E-05 4E-06 1.37E-04 3E-06 2.54E-04 5E-06 3.32E-04 8E-06 3.98E-04 2E-05 ± ± ± ± ± 09sh 16:13:58 +39:31:50 6.53E-04 4E-05 1.46E-03 3E-05 2.34E-03 5E-05 3.04E-03 6E-05 2.83E-03 1E-04 ± ± ± ± ± 09sk 13:30:51 +30:20:02 1.15E-04 7E-06 2.52E-04 6E-06 3.19E-04 7E-06 3.87E-04 9E-06 4.14E-04 2E-05 ± ± ± ± ± 09t 14:15:42 +16:12:00 5.39E-04 3E-05 1.24E-03 3E-05 1.67E-03 3E-05 1.99E-03 4E-05 2.14E-03 8E-05 ± ± ± ± ±

(cont’d) Table 3.4—Continued

a a a a a SN Gal RA Gal Dec fu fg fr fi fz (PTF) (J2000.0) (J2000.0) (Jy) (Jy) (Jy) (Jy) (Jy)

09tm 13:46:55 +61:33:17 4.21E-04 2E-05 1.41E-03 3E-05 2.68E-03 5E-05 3.76E-03 8E-05 4.84E-03 2E-04 ± ± ± ± ± 09uj 14:20:10 +53:33:42 1.14E-04 8E-06 2.86E-04 6E-06 4.33E-04 1E-05 5.28E-04 1E-05 5.38E-04 3E-05 ± ± ± ± ± 10bau 09:16:21 +17:43:38 2.47E-03 1E-04 6.20E-03 1E-04 1.02E-02 2E-04 1.28E-02 3E-04 1.56E-02 5E-04 ± ± ± ± ± 10bfz 12:54:41 +15:24:16 1.80E-06 1E-06 4.07E-06 5E-07 6.37E-06 8E-07 4.20E-06 1E-06 1.20E-06 5E-06 ± ± ± ± ± 10bgl 10:19:05 +46:27:16 3.40E-03 2E-04 8.80E-03 2E-04 1.27E-02 3E-04 1.57E-02 3E-04 1.77E-02 5E-04 ± ± ± ± ± 100 10bhu 12:55:28 +53:34:30 2.17E-04 1E-05 5.53E-04 1E-05 8.10E-04 2E-05 9.83E-04 2E-05 1.17E-03 5E-05 ± ± ± ± ± 10bip 12:34:10 +08:21:49 2.75E-05 3E-06 9.00E-05 2E-06 1.16E-04 3E-06 1.46E-04 4E-06 1.74E-04 1E-05 ± ± ± ± ± 10bzf 11:44:02 +55:41:22 3.89E-05 5E-06 5.33E-05 2E-06 8.66E-05 3E-06 1.09E-04 5E-06 1.37E-04 2E-05 ± ± ± ± ± 10cd 03:00:33 +36:15:25 2.11E-05 5E-06 5.21E-05 2E-06 9.28E-05 4E-06 8.22E-05 5E-06 1.02E-04 2E-05 ± ± ± ± ± 10con 16:11:39 +00:52:31 2.36E-04 2E-05 6.06E-04 1E-05 1.61E-03 3E-05 3.58E-03 8E-05 2.85E-03 1E-04 ± ± ± ± ± 10cqh 16:10:36 -01:43:01 5.53E-04 3E-05 1.98E-03 4E-05 3.91E-03 8E-05 5.67E-03 1E-04 7.45E-03 2E-04 ± ± ± ± ± 10cwx 12:33:16 -00:03:12 4.72E-05 4E-06 9.72E-05 3E-06 1.23E-04 4E-06 1.60E-04 5E-06 1.73E-04 2E-05 ± ± ± ± ± 10cxq 13:48:19 +13:28:57 1.03E-04 7E-06 2.23E-04 5E-06 2.77E-04 6E-06 3.22E-04 8E-06 3.25E-04 2E-05 ± ± ± ± ± 10cxx 14:47:27 +01:55:05 2.79E-04 2E-05 9.62E-04 2E-05 1.81E-03 4E-05 2.48E-03 5E-05 3.31E-03 1E-04 ± ± ± ± ±

(cont’d) Table 3.4—Continued

a a a a a SN Gal RA Gal Dec fu fg fr fi fz (PTF) (J2000.0) (J2000.0) (Jy) (Jy) (Jy) (Jy) (Jy)

10czn 14:51:17 +15:26:46 8.36E-04 4E-05 2.63E-03 5E-05 4.19E-03 8E-05 5.38E-03 1E-04 6.08E-03 2E-04 ± ± ± ± ± 10dk 05:08:21 +00:12:42 1.11E-06 2E-06 5.16E-06 6E-07 9.23E-06 1E-06 9.73E-06 2E-06 9.92E-06 8E-06 ± ± ± ± ± 10dvb 17:16:10 +31:47:32 1.81E-03 9E-05 4.49E-03 9E-05 7.00E-03 1E-04 8.64E-03 2E-04 1.06E-02 3E-04 101 ± ± ± ± ± 10hv 14:03:56 +54:27:27 1.51E-04 1E-05 4.23E-04 9E-06 5.92E-04 1E-05 7.55E-04 2E-05 8.11E-04 4E-05 ± ± ± ± ± 10in 07:50:00 +33:06:27 2.64E-06 2E-06 7.66E-06 7E-07 9.70E-06 1E-06 1.63E-05 2E-06 1.16E-05 6E-06 ± ± ± ± ± 10s 10:37:16 +38:06:23 8.95E-05 6E-06 2.37E-04 5E-06 3.67E-04 8E-06 4.55E-04 1E-05 5.21E-04 2E-05 ± ± ± ± ± 10ts 12:33:55 +13:55:08 1.70E-04 1E-05 4.53E-04 1E-05 7.26E-04 2E-05 9.44E-04 2E-05 1.06E-03 5E-05 ± ± ± ± ± 10u 10:09:58 +46:00:33 6.48E-07 1E-06 1.62E-06 5E-07 6.87E-06 8E-07 9.58E-06 1E-06 1.43E-05 4E-06 ± ± ± ± ±

a 2 3σ upper limits for the ﬂuxes of PTF09be and PTF09gyp are calculated as 3σsky√πr , where r is set to 5 kpc (projected) at the distance of the supernova. a b b b b b b SN type SN RA SN Dec z µ Mu Mg Mr Mi Mz MB (PTF) (J2000.0) (J2000.0)

09aux Ic/Ia 16:09:15.84 +29:17:36.7 0.047 36.60 18.43 19.85 20.49 20.79 21.03 19.48 − − − − − − 09awk Ib 13:37:56.36 +22:55:04.8 0.062 37.22 18.37 19.18 19.50 19.72 19.84 18.90 − − − − − − 09axc II 14:53:13.06 +22:14:32.2 0.115 38.64 18.70 20.07 20.59 20.80 20.94 19.75 − − − − − − 09axi II 14:12:40.82 +31:04:03.3 0.064 37.29 17.55 18.60 19.07 19.11 19.02 18.35 − − − − − − 09bce II 16:35:17.66 +55:37:59.1 0.023 35.01 18.76 20.22 20.95 21.34 21.62 19.85 − − − − − − 09bgf II 14:41:38.28 +19:21:43.8 0.031 35.67 16.39 17.15 17.38 17.50 17.59 16.91

102 − − − − − − 09bw II 15:05:02.04 +48:40:01.9 0.150 39.27 16.47 18.58 19.11 19.32 19.32 18.25 − − − − − − 09cjq II 21:16:28.48 -00:49:39.7 0.019 34.58 19.76 21.03 21.70 22.01 22.29 20.67 − − − − − − 09ct II 11:42:13.80 +10:38:53.9 0.150 39.27 18.35 19.38 19.90 20.17 20.42 19.07 − − − − − − 09cu II 13:15:23.14 +46:25:08.6 0.057 37.03 19.80 20.82 21.33 21.61 21.81 20.51 − − − − − − 09cvi II 21:47:09.80 +08:18:35.6 0.030 35.59 99.99 13.51 13.56 13.58 14.27 13.18 − − − − − 09dah IIb 22:45:17.05 +21:49:15.2 0.024 35.08 17.17 17.85 18.12 18.35 18.48 17.60 − − − − − − 09dfk Ib 23:09:13.42 +07:48:15.4 0.016 34.21 15.90 16.89 17.29 17.51 17.68 16.60 − − − − − − 09djl II 16:33:55.94 +30:14:16.3 0.184 39.75 17.92 19.52 20.20 20.39 20.79 19.20 − − − − − −

Table 3.5. Host properties derived from SDSS photometry (cont’d) Table 3.5—Continued

a b b b b b b SN type SN RA SN Dec z µ Mu Mg Mr Mi Mz MB (PTF) (J2000.0) (J2000.0)

09dra II 15:48:11.47 +41:13:28.2 0.077 37.71 19.98 20.93 21.41 21.68 21.91 20.63 − − − − − − 09due II 16:26:52.36 +51:33:23.9 0.029 35.52 20.23 21.18 21.62 21.83 22.03 20.90 − − − − − − 09dxv IIb 23:08:34.73 +18:56:13.7 0.033 35.81 19.38 20.24 20.61 20.86 21.04 19.97 − − − − − − 09dzt Ic 16:03:04.20 +21:01:47.2 0.087 38.00 19.62 20.30 20.72 20.85 21.09 20.05 − − − − − − 09ebq II 00:14:01.69 +29:25:58.5 0.024 35.05 18.32 19.23 19.74 19.99 20.23 18.93 − − − − − − 103 09ecm II 01:06:43.16 -06:22:40.9 0.029 35.48 18.82 19.74 20.15 20.27 20.45 19.47 − − − − − − 09ejz Ic/Ia 00:55:07.29 -06:57:05.4 0.110 38.54 19.72 20.92 21.59 21.88 22.13 20.57 − − − − − − 09fae IIb 17:26:20.33 +72:56:30.6 0.067 37.40 17.07 17.52 17.59 17.68 18.29 17.29 − − − − − − 09fbf II 21:20:38.44 +01:02:52.9 0.021 34.80 19.36 20.30 20.69 20.90 21.08 20.02 − − − − − − 09fmk II 23:57:46.19 +11:58:45.3 0.063 37.26 19.49 20.40 20.95 21.24 21.41 20.08 − − − − − − 09foy II 23:17:10.58 +17:15:03.2 0.060 37.15 19.39 20.41 20.89 21.16 21.28 20.11 − − − − − − 09fqa II 22:25:32.33 +18:59:41.4 0.030 35.59 18.66 19.69 20.15 20.38 20.52 19.39 − − − − − − 09fsr Ib 23:04:52.98 +12:19:59.0 0.008 32.67 19.89 21.19 21.81 22.09 22.25 20.84 − − − − − − 09g II 15:16:31.48 +54:27:34.7 0.040 36.23 19.07 19.90 20.25 20.41 20.54 19.65 − − − − − −

(cont’d) Table 3.5—Continued

a b b b b b b SN type SN RA SN Dec z µ Mu Mg Mr Mi Mz MB (PTF) (J2000.0) (J2000.0)

09gof II 01:22:25.60 +03:38:08.4 0.103 38.38 19.56 20.43 20.85 20.97 21.06 20.18 − − − − − − 09gtt II 02:20:37.70 +02:24:13.2 0.041 36.29 16.92 17.92 18.14 18.56 18.59 17.62 − − − − − − 09hdo II 00:15:23.20 +30:43:19.3 0.047 36.60 19.83 21.06 21.69 22.02 22.29 20.71 − − − − − − 09hzg II 11:50:57.74 +21:11:49.4 0.028 35.44 18.21 19.62 20.37 20.77 21.12 19.25 − − − − − − 09iex II 12:02:46.86 +02:24:06.8 0.020 34.70 15.79 16.40 16.60 16.74 17.03 16.16 − − − − − − 104 09ige II 08:55:34.24 +32:39:57.0 0.064 37.29 19.07 19.91 20.22 20.43 20.44 19.66 − − − − − − 09igz II 08:53:56.70 +33:40:11.5 0.086 37.97 18.09 18.89 19.21 19.38 19.49 18.63 − − − − − − 09ism II 11:44:35.87 +10:12:43.7 0.029 35.52 17.82 18.62 18.95 19.12 19.15 18.37 − − − − − − 09ps Ic 16:14:08.62 +55:41:41.4 0.106 38.46 18.09 18.79 19.11 19.25 19.14 18.54 − − − − − − 09q Ic 12:24:50.11 +08:25:58.8 0.090 38.07 19.92 21.07 21.67 21.98 22.21 20.73 − − − − − − 09r II 14:18:58.63 +35:23:16.0 0.027 35.36 15.63 16.90 17.53 17.80 18.00 16.57 − − − − − − 09sh II 16:13:58.08 +39:31:58.1 0.038 36.10 19.36 20.18 20.68 20.92 20.86 19.92 − − − − − − 09sk Ic-BL 13:30:51.15 +30:20:04.9 0.035 35.97 17.38 18.13 18.36 18.55 18.62 17.88 − − − − − − 09t II 14:15:43.29 +16:11:59.1 0.039 36.18 19.30 20.08 20.38 20.54 20.63 19.84 − − − − − −

(cont’d) Table 3.5—Continued

a b b b b b b SN type SN RA SN Dec z µ Mu Mg Mr Mi Mz MB (PTF) (J2000.0) (J2000.0)

09tm II 13:46:55.94 +61:33:15.6 0.035 35.94 18.78 20.05 20.69 21.03 21.30 19.69 − − − − − − 09uj II 14:20:11.15 +53:33:41.0 0.065 37.33 18.80 19.68 20.07 20.25 20.28 19.42 − − − − − − 10bau II 09:16:21.29 +17:43:40.2 0.026 35.28 20.08 21.00 21.50 21.71 21.92 20.71 − − − − − − 10bfz Ic-BL 12:54:41.27 +15:24:17.0 0.150 39.27 16.29 17.09 17.34 16.87 15.52 16.82 − − − − − − 10bgl II 10:19:04.70 +46:27:23.3 0.030 35.59 20.67 21.62 22.00 22.20 22.33 21.34 − − − − − − 105 10bhu Ic 12:55:28.44 +53:34:28.7 0.036 36.00 18.12 19.03 19.42 19.60 19.79 18.75 − − − − − − 10bip Ic 12:34:10.52 +08:21:48.5 0.051 36.78 16.72 17.87 18.10 18.32 18.51 17.56 − − − − − − 10bzf Ic-BL 11:44:02.99 +55:41:27.6 0.050 36.73 16.81 17.21 17.77 17.94 18.22 16.97 − − − − − − 10cd II 03:00:32.93 +36:15:25.4 0.045 36.52 17.11 17.71 18.13 17.81 17.94 17.51 − − − − − − 10con II 16:11:39.09 +00:52:33.3 0.033 35.81 18.55 19.41 20.35 21.07 20.77 19.08 − − − − − − 10cqh II 16:10:37.60 -01:43:00.7 0.041 36.29 20.13 21.25 21.81 22.09 22.31 20.93 − − − − − − 10cwx II 12:33:16.53 -00:03:10.6 0.073 37.59 18.12 18.79 19.05 19.18 19.32 18.53 − − − − − − 10cxq II 13:48:19.32 +13:28:58.8 0.047 36.60 17.96 18.67 18.86 18.98 18.99 18.43 − − − − − − 10cxx II 14:47:27.78 +01:55:03.8 0.034 35.87 18.40 19.65 20.26 20.56 20.86 19.30 − − − − − −

(cont’d) Table 3.5—Continued

a b b b b b b SN type SN RA SN Dec z µ Mu Mg Mr Mi Mz MB (PTF) (J2000.0) (J2000.0)

10czn II 14:51:16.23 +15:26:43.6 0.045 36.50 20.23 21.33 21.78 22.00 22.13 21.02 − − − − − − 10dk II 05:08:21.54 +00:12:42.9 0.074 37.62 14.43 15.89 16.37 16.35 16.34 15.63 − − − − − − 10dvb II 17:16:12.25 +31:47:36.0 0.023 35.00 19.50 20.40 20.83 21.02 21.22 20.11

106 − − − − − − 10hv II 14:03:56.18 +54:27:31.1 0.052 36.81 18.56 19.56 19.88 20.12 20.21 19.27 − − − − − − 10in IIb 07:50:01.24 +33:06:23.8 0.070 37.50 15.08 16.08 16.27 16.73 16.36 15.80 − − − − − − 10s II 10:37:16.30 +38:06:23.2 0.051 36.78 18.00 18.94 19.36 19.56 19.71 18.66 − − − − − − 10ts II 12:33:56.40 +13:55:08.3 0.046 36.55 18.52 19.47 19.92 20.16 20.28 19.18 − − − − − − 10u II 10:09:58.42 +46:00:35.2 0.150 39.27 15.69 16.48 17.68 17.96 18.38 16.38 − − − − − −

a Assuming H0 = 70, Ωm = 0.3, and ΩΛ = 0.7

bMagnitudes are corrected for galactic extinction but not intrinsic extinction. SNName type M SFR(FAST) SFR(u) SSFR Age −1 log M⊙ log M⊙/yr log M⊙/yr log yr log yr

+0.03 +0.43 +0.45 +0.02 PTF09aux Ic/Ia 10.43−0.07 0.59−0.00 0.06 11.03−0.00 9.70−0.16 +0.18 − +0.35 − − +0.47 +0.56 PTF09awk Ib 9.45−0.14 0.61−0.75 0.44 8.85−0.83 8.80−0.42 +0.01 +0.33 − +0.36 +0.01 PTF09axc II 10.33−0.09 0.25−0.00 0.04 10.58−0.00 9.60−0.14 +0.09 − +0.03 − +0.00 +0.11 PTF09axi II 9.35−0.04 0.33−0.16 0.31 9.68−0.24 9.30−0.06 +0.08 − +0.83 − − +0.90 +0.20 PTF09bce II 10.68−0.09 0.11−0.95 0.36 10.58−1.01 9.60−0.37 +0.06 +0.74 − +0.87 +0.06 PTF09bgf II 8.59−0.19 0.88−0.00 0.80 9.47−0.00 9.20−0.68 +0.02 − +0.34 − − +0.36 +0.02 PTF09bw II 9.75−0.15 0.83−0.00 0.77 10.58−0.00 9.60−0.19 +0.05 − +0.98 − − +1.09 +0.15 PTF09cjq II 10.84−0.14 0.62−0.37 0.71 10.22−0.36 9.50−0.54 +0.10 +1.46 − +1.62 +0.10 PTF09ct II 10.00−0.19 0.22−0.27 0.00 10.22−0.36 9.50−0.95 +0.09 − +0.89 − − +1.08 +0.22 PTF09cu II 10.48−0.19 0.81−0.51 0.77 9.68−0.54 9.30−0.71 +0.17 +0.38 − +0.41 +0.34 PTF09cvi II 7.07−0.15 < 2.06−0.42 1.63 9.13−0.55 9.00−0.34 +0.38 − +0.02 − − +0.00 +2.10 PTF09dah IIb 8.50−0.13 1.70−1.72 0.83 6.80−2.05 6.80−0.08 +0.07 +0.72 − +0.83 +0.13 PTF09dfk Ib 8.77−0.14 0.91−0.24 0.80 9.68−0.24 9.30−0.52 +0.09 − +0.06 − − +0.00 +0.10 PTF09djl II 10.22−0.06 0.35−0.36 0.16 10.58−0.45 9.60−0.09 +0.22 − +0.32 − − +0.45 +0.80 PTF09dra II 10.34−0.14 1.62−1.28 1.33 8.72−1.50 8.70−0.41 +0.07 +0.82 − +0.96 +0.14 PTF09due II 10.51−0.18 0.83−0.25 0.79 9.68−0.24 9.30−0.64 +0.19 +0.26 − +0.34 +0.70 PTF09dxv IIb 9.93−0.11 1.20−1.01 0.95 8.72−1.20 8.70−0.31 +0.36 +0.13 − +0.09 +1.61 PTF09dzt Ic 9.48−0.10 2.58−1.24 1.70 6.89−1.60 6.90−0.12 +0.24 +0.30 − +0.43 +0.81 PTF09ebq II 9.59−0.15 0.99−1.11 0.74 8.60−1.32 8.60−0.41 +0.06 +0.50 − +0.55 +0.11 PTF09ecm II 9.83−0.09 0.15−0.19 0.18 9.68−0.24 9.30−0.36 +0.05 +0.85 − +0.90 +0.05 PTF09ejz Ic/Ia 10.79−0.08 0.22−0.06 0.50 10.58−0.00 9.60−0.33 +0.23 +0.33 − +0.38 +0.63 PTF09fae IIb 8.03−0.13 0.56−0.41 0.06 7.47−0.59 7.50−0.42 +0.10 +0.63 − +0.75 +0.22 PTF09fbf II 10.08− 0.61− 0.54 9.47− 9.20− 0.13 0.40 − 0.45 0.51 Table 3.6. Host galaxy properties ﬁt from SDSS photometry (cont’d)

107 Table 3.6—Continued

SNName type M SFR(FAST) SFR(u) SSFR Age −1 log M⊙ log M⊙/yr log M⊙/yr log yr log yr

+0.23 +0.31 +0.43 +0.84 PTF09fmk II 10.12−0.16 1.52−1.14 1.22 8.60−1.32 8.60−0.44 +0.07 +0.70 − +0.79 +0.11 PTF09foy II 10.30−0.11 0.38−0.26 0.44 9.92−0.30 9.40−0.40 +0.07 +0.56 − +0.63 +0.10 PTF09fqa II 9.97−0.10 0.05−0.23 0.12 9.92−0.30 9.40−0.36 +0.03 +0.03 − +0.00 +0.01 PTF09fsr Ib 10.85−0.02 0.27−0.02 0.50 10.58−0.00 9.60−0.08 +0.05 +0.82 − +0.96 +0.04 PTF09g II 9.85−0.16 0.17−0.02 0.19 9.68−0.00 9.30−0.61 +0.03 +0.40 − +0.45 +0.01 PTF09gof II 10.17−0.10 0.25−0.00 0.35 9.92−0.00 9.40−0.25 +0.13 +0.43 − +0.58 +0.51 PTF09gtt II 9.03−0.20 0.18−0.73 0.03 8.85−0.83 8.80−0.55 +0.07 +0.83 − +0.94 +0.22 PTF09hdo II 10.80−0.14 0.88−0.65 0.87 9.92−0.66 9.40−0.58 +0.09 +1.02 − +1.20 +0.28 PTF09hzg II 10.42−0.19 0.50−0.65 0.51 9.92−0.66 9.40−0.70 +0.21 +1.67 − +1.67 +0.61 PTF09iex II 8.00−0.41 0.27−0.53 0.48 8.27−0.71 8.30−1.71 +0.04 − +0.50 − − +0.55 +0.03 PTF09ige II 9.83−0.09 0.15−0.01 0.18 9.68−0.00 9.30−0.32 +0.10 +2.35 − +2.88 +0.10 PTF09igz II 9.45−0.58 0.23−0.15 0.14 9.68−0.24 9.30−2.51 +0.09 − +0.65 − − +0.75 +0.13 PTF09ism II 9.26−0.14 0.21−0.15 0.19 9.47−0.21 9.20−0.54 +0.54 − +0.15 − − +0.10 +2.52 PTF09ps Ic 8.86−0.11 2.06−2.37 1.16 6.80−2.88 6.80−0.12 +0.05 +1.00 − +1.09 +0.11 PTF09q Ic 10.77−0.14 0.55−0.35 0.67 10.22−0.36 9.50−0.55 +0.05 +0.82 − +0.90 +0.05 PTF09r II 9.14−0.09 1.44−0.03 1.03 10.58−0.00 9.60−0.30 +0.08 − +0.35 − − +0.39 +0.11 PTF09sh II 10.05−0.07 0.37−0.18 0.37 9.68−0.24 9.30−0.24 +0.08 +2.10 − +2.49 +0.17 PTF09sk Ic-BL 8.99−0.42 0.30−0.17 0.31 9.29−0.18 9.10−2.31 +0.08 − +0.64 − − +0.75 +0.11 PTF09t II 9.83−0.14 0.36−0.15 0.33 9.47−0.21 9.20−0.54 +0.08 +0.83 − +0.94 +0.25 PTF09tm II 10.42−0.15 0.50−0.66 0.52 9.92−0.66 9.40−0.57 +0.08 +0.21 − +0.21 +0.10 PTF09uj II 9.79−0.05 0.12−0.17 0.15 9.68−0.24 9.30−0.19 +0.07 +0.99 − +1.19 +0.16 PTF10bau II 10.47−0.24 0.79−0.25 0.75 9.68−0.24 9.30−0.82 +0.20 +3.08 − +3.03 +0.21 PTF10bfz Ic-BL 8.34− 0.79− 0.82 9.13− 9.00− 0.53 − 0.14 − − 0.34 3.00 (cont’d)

108 Table 3.6—Continued

SNName type M SFR(FAST) SFR(u) SSFR Age −1 log M⊙ log M⊙/yr log M⊙/yr log yr log yr

+0.07 +0.62 +0.70 +0.11 PTF10bgl II 10.61−0.11 0.93−0.20 0.87 9.68−0.24 9.30−0.45 +0.07 +0.82 − +0.96 +0.12 PTF10bhu Ic 9.59−0.18 0.09−0.21 0.04 9.68−0.24 9.30−0.64 +0.13 − +0.24 − − +0.28 +0.41 PTF10bip Ic 9.01−0.08 0.12−0.69 0.23 9.13−0.79 9.00−0.23 +0.25 − +0.29 − − +0.20 +0.89 PTF10bzf Ic-BL 8.23−0.07 1.14−0.58 0.52 7.09−0.77 7.10−0.21 +0.10 +2.17 − +2.43 +0.12 PTF10cd II 8.61−0.32 0.52−0.08 0.53 9.13−0.16 9.00−2.32 +0.07 − +1.42 − − +1.86 +0.21 PTF10con II 10.29−0.49 0.36−0.62 0.51 9.92−0.66 9.40−1.34 +0.06 +0.83 − +0.94 +0.16 PTF10cqh II 10.76−0.15 0.83−0.31 0.84 9.92−0.30 9.40−0.53 +0.17 +1.59 − +1.79 +0.70 PTF10cwx II 9.10−0.41 0.61−0.83 0.38 8.49−0.98 8.50−1.83 +0.09 +0.56 − +0.69 +0.11 PTF10cxq II 9.10−0.12 0.18−0.11 0.20 9.29−0.18 9.10−0.50 +0.08 − +0.82 − − +0.94 +0.22 PTF10cxx II 10.22−0.14 0.30−0.66 0.34 9.92−0.66 9.40−0.53 +0.07 +0.40 − +0.45 +0.11 PTF10czn II 10.63−0.07 0.71−0.24 0.71 9.92−0.30 9.40−0.23 +0.17 +2.50 − +3.12 +0.12 PTF10dk II 8.36−0.62 1.56−0.15 1.51 9.92−0.30 9.40−2.60 +0.11 − +0.80 − − +0.98 +0.22 PTF10dvb II 10.13−0.20 0.66−0.39 0.59 9.47−0.45 9.20−0.70 +0.04 +0.72 − +0.79 +0.01 PTF10hv II 9.82−0.13 0.11−0.00 0.01 9.92−0.00 9.40−0.48 +0.29 − +1.42 − − +1.80 +0.83 PTF10in IIb 8.19−0.60 0.42−1.09 0.59 8.60−1.32 8.60−1.88 +0.05 − +0.83 − − +0.94 +0.03 PTF10s II 9.61−0.13 0.31−0.02 0.18 9.92−0.00 9.40−0.52 +0.04 − +0.81 − − +0.94 +0.04 PTF10ts II 9.86−0.12 0.06−0.05 0.04 9.92−0.00 9.40−0.50 +0.21 − +2.58 − +2.60 +0.00 PTF10u II 9.72− 3.47− 0.86 13.18− 10.00− 0.25 − 0.13 − − 0.00 0.46

109 Criterion Details Samplea

Within redshift range of sample 0.0189995 < z < 0.103 382095 Successful stellar mass estimate Mmed > 2 and not INDEF 363881 Successful SFR estimate SFRavg != 99 and no SFR ﬂag 362395 − 12+log(O/H) metallicity estimate OHmed != 99.9 131203 − N2 ﬂux ratio in valid range -2.5 < N2 < -0.3 130370

aRemaining sample shown. Cuts are sequential. The sample size of the full MPA/JHU value-added catalog is 927552.

Table 3.7. Cuts to MPA/JHU comparison sample

110 Chapter 4: Abnormally luminous core-collapse supernovae are more metal-poor than normal type II supernovae

4.1. Introduction

Although normal core-collapse supernovae (CCSNe) are about two magnitudes less luminous than normal type Ia supernovae (SNe), in the last decade, abnormally luminous CCSNe have begun to be found that are even more luminous than type Ia

SNe.

CCSNe which are abnormally optically luminous appear to occur more often in low-mass, low-luminosity galaxies (Neill et al. 2011). Stoll et al. (2011) made the first spectroscopic abundance measurements confirming that hosts of five such abnormally luminous CCSNe (including data from Young et al. 2010; Koz lowski et al. 2010) are metal-poor compared to the SDSS galaxy sample from the MPA/JHU value-added catalog (Kauffmann et al. 2003; Tremonti et al. 2004; Brinchmann et al. 2004; Salim et al. 2007), indicating this is likely due to metallicity. These abnormally luminous

CCSNe are predominantly SNe Ic or IIn. Subsequent spectroscopic measurements of the host of the luminous SN 2008am (Chatzopoulos et al. 2011) and the luminous

SN 2010ay (Prieto & Filippenko 2010; Modjaz et al. 2010; Sanders et al. 2012),

111 and photometric limits on the hosts of the luminous SNe PS1-10awh and PS1-10ky

(Chomiuk et al. 2011) reinforce this conclusion.

The example of research on GRB mechanisms by examining the host properties oﬀers many instructive parallels for luminous CCSNe, both positive and negative.

Many studies (e.g. Neill et al. 2009; Sanders et al. 2012; Stoll et al. 2011; Campisi et al. 2011; Vergani et al. 2011; Chen et al. 2013) compare host galaxies of SNe or GRBs to the overall galaxy population. This is a good way to put the host metallicity results in context. It is not, however, an unambiguous way to evaluate whether metallicity is a key parameter governing whether a supernova has a given spectral type or luminosity. Supernovae trace star formation rather than overall stellar mass, and star formation is not evenly distributed among galaxies of a given mass. Using galaxy samples as standards of comparison has led to suggestions of separate luminosity-metallicity relationships for host galaxies of, for example, GRBs

(Levesque et al. 2010b; Han et al. 2010). No such oﬀset has been observed for type

Ia SNe (Neill et al. 2009). This question of an offset, while interesting, requires extremely precise control of sample selection effects to rigorously explore. It has also unfortunately led to confusion in many subsequent discussions of potential metallicity cutoffs. For example, Mannucci et al. (2011) found that GRB hosts lie on the M–Z–SFR relation, and concluded that they had disproved a metallicity cutoff. In fact, what that had shown is that despite appearing to have a shifted M-Z relation, the GRB hosts are perfectly consistent with other star forming galaxies of

112 the same mass and star formation rate. The hosts do not, however, sample the top of the mass and metallicity range. They are perfectly consistent with a metallicity cutoﬀ, and not at all consistent with tracing star formation.

Only by comparing the metallicity distribution of a SN variety to the overall metallicity distribution of star formation can one rigorously test for a metallicity dependence. The Stoll et al. (2012) metallicity distribution of type II SN hosts can serve as a reasonable proxy for tracing star formation, and is a good ﬁrst-order standard of comparison for evaluating how rare classes of SNe, such as abnormally luminous CCSNe, depend on progenitor metallicity.

4.2. Data and Analysis

We obtained host galaxy spectra of SNe with peak optical magnitudes brighter than

20 using the Multi-Object Double Spectrograph (Pogge et al. 2010, MODS) at the − Large Binocular Telescope (LBT). We choose to count events with peak absolute magnitudes brighter than 20 as abnormally luminous. Similar studies have settled − on a cutoﬀ at 21 (e.g. Gal-Yam 2012), and we further discuss this decision in − 4.4.2. Our spectroscopic observations were made at the supernova position. Each § host galaxy is essentially unresolved or marginally resolved in normal seeing. The extraction aperture was matched to the seeing, and is roughly twice the FWHM.

The extraction apertures and their corresponding projected physical distances are listed in Table 4.1.

113 We processed the MODS longslit data using the MODS reduction code under development (Croxall et al. in prep.). Relative flux calibration was done with observations of spectrophotometric standard stars. The PP04N2 metallicity diagnostic is extremely insensitive to reddening because it depends on the flux ratio of two very close lines, so intrinsic extinction corrections were not applied. The blue and red channels of five spectra of luminous CCSNe and their underlying host galaxies taken during normal MODS science operations are shown in Figure 4.1.

The red channel of three luminous CCSN host galaxy spectra taken during MODS commissioning (Pogge et al. 2012) during bright time are shown in Figure 4.2.

4.2.1. Oxygen abundance determination

In order to make valid comparisons, we must consistently use the scale of a single strong-line method (Modjaz et al. 2008). We consider host metallicities determined with the N2 diagnostic of Pettini & Pagel (2004), which we directly measure for each of our targets. This diagnostic depends exclusively on [N II]λ6584/Hαλ6563, and is extremely insensitive to reddening, though it has a larger intrinsic scatter than other strong-line diagnostics based on the physical conditions of the H II regions.

Several techniques are used to estimate the oxygen abundances of H II regions in star-forming galaxies, and a substantial literature exists discussing their various merits and drawbacks (e.g. Kewley & Ellison 2008, and references therein). A full recapitulation of this is outside the scope of this paper.

114 We include in our sample the host metallicities of luminous CCSNe found in the literature, as listed in Table 4.2. To maintain consistency, we convert all literature values to the N2 scale of Pettini & Pagel (2004).

4.2.2. Comparison sample

We bypass the impossible task of constructing an ab initio comparison galaxy sample with the same selection function as the super-luminous supernovae (SLSNe), and instead use a comparison sample of the host galaxies of normal type II supernovae

(Stoll et al. 2012) from the Palomar Transient Factory (PTF) survey. This is a reasonable proxy for tracing star formation. While not itself completely unbiased, this normal core-collapse supernova host metallicity sample avoids many previous sources of bias. It is drawn from a single source survey, which is areal rather than galaxy-targeted. The metallicities are measured at the SN location or at similar galactocentric radii, rather than exclusively at the galaxy centers. The selection function of PTF is not yet published, but followup strategy currently makes sure to obtain spectroscopic classiﬁcation of SNe in low-luminosity hosts. If any bias exists in the normal type II metallicity distribution from the PTF sample, it is likely to be somewhat skewed towards lower metallicity, due to a relatively complete sampling of low-luminosity hosts. If the hosts of luminous CCSNe are metal-poor compared to the normal-luminosity PTF type II SNe, then it is a reasonably safe conclusion that they are metal-poor compared to all normal-luminosity SNe.

115 4.3. Results

In Table 4.1 we list the measured Hα λ6563 and [N II]λ6584 line ﬂuxes and the resulting oxygen abundance using the N2 strong line method of Pettini & Pagel

(2004). We also list the inferred iron abundance using the conversion of Stoll et al.

(2012).

As shown in Figure 4.3, the hosts of luminous CCSNe are dramatically diﬀerent as a population from the hosts of normal type II SNe. Luminous CCSNe come from distinctly lower metallicity regions than normal SNe II, with a K-S test probability of being drawn from the same underlying distribution of only 0.04%. The PP04N2 metallicity distribution of the hosts of 12 luminous CCSNe (blue solid) is from the eight measurements presented in this paper in Table 4.1 and the four measurements from the literature in Table 4.2. We exclude the two literature measurements that duplicate our own measurements in order to maintain the maximum consistency of method. The PP04N2 metallicity distribution of the hosts of 34 normal type II SNe is from Stoll et al. (2012), and is a representative subsample of the hosts of type II

SNe from the PTF ﬁrst-year sample (Arcavi et al. 2010).

4.4. Discussion

Iron is a key source of opacity for massive star winds, and at lower metallicity,

α-elements like oxygen are enhanced relative to iron compared to the solar mixture.

The progenitors of luminous CCSNe are much poorer in iron than the progenitors

116 of normal type II SNe, suggesting mass loss is a key factor for abnormally high luminosity.

The luminous CCSN progenitors all have iron abundances greater than

[Fe/H] = 2.0, where winds are still primarily driven by iron opacity. For the − −3 most metal-poor stars (Z/Z⊙ < 10 ), non-iron elements such as carbon dominate the radiative driving (Vink & de Koter 2005), but in the metallicity range of these luminous CCSN progenitors, iron abundance should still be the dominant factor which determines wind strength and mass loss.

Leloudas et al. (2013) simulate spectra of SNe underneath strong circumstellar interaction and ﬁnd that the faintest type IIn SN that can hide a type Ia SN has

M = 20.1, and that type IIn SNe fainter than M = 17.2 are unable to mask − − type II-P SNe brighter than M = 15. Note that Khan et al. (2011) found that − super-Chandrasekhar type Ia SNe predominantly appear in the outskirts of galaxies, in regions expected to be metal-poor. It is possible that our abnormally luminous

“core-collapse” sample includes some type IIn SNe that are in fact core-collapse impostors.

The spectra of type IIn events conceal more than they reveal; essentially all we learn from them is that a very energetic event is happening inside a layer of gas that is optically thick, and the energy is being reprocessed by the gas and emitted in an optically thin layer in the direction of the observer. It could be possible that

117 fine-tuning the metallicity and thus the opacity could result in a Goldilocks stellar wind–not too fast, so it is optically thin at the time of the SN explosion, nor too slow, so there is not enough gas to reprocess the kinetic energy of the explosion into light. If a fine-tuned metallicity can achieve this very difficult balance, then perhaps it may also be able to create Goldilocks stellar winds out of single-degenerate interaction that will lead to type Ia SNe masked by a type IIn spectrum.

4.4.1. Diﬀerent standards of comparison

In this work we choose a representative subsample of the type II SNe from the

first-year PTF survey. This is a reasonable standard of comparison, as we discussed in 4.2.2. In the eternal tradeoff between finite telescope time and perfect data, it § is a good compromise. Nevertheless, better comparison samples could be assembled in the future.

We are comparing a sample of local hosts of type II SNe to a sample of hosts at redshifts of around 0.2. The average enrichment in galaxies of a given mass does evolve (e.g. Erb et al. 2006), and there would be some additional merit to choosing a comparison sample matched in redshift, as telluric contamination would have similar eﬀects on the two samples.

We are comparing exclusively to the hosts of type II SNe rather than to the hosts of all CCSNe. It has been shown that the rates of diﬀerent types of CCSNe vary with metallicity (e.g. Prantzos & Boissier 2003; Prieto et al. 2008; Modjaz

118 et al. 2008; Kelly & Kirshner 2012). A representative sample of all CCSNe would be ideal, but is difficult to achieve because different surveys disagree on the relative proportions of CCSN types. The PTF seems to be relatively inefficient at detecting

SNe Ib/c compared to other surveys, and so we believe our results will be most useful to others if we restrict our comparison sample to SNe II rather than applying as-yet uncertain corrections for this diﬀerential selection or requiring future researchers who wish to compare to our results to do so.

Note that a redshift-matched comparison sample of all CCSNe would be ideal, but, in addition to the diﬃculties mentioned above, a matching distant sample does not yet exist; most SN surveys looking for distant SNe are interested in probing cosmology, and concentrate on SNe Ia rather than aiming for complete or representative discovery of CCSNe.

4.4.2. What counts as a super-luminous supernova?

The mean maximum luminosity of all CCSNe found by Richardson et al. (2002) is approximately MB = 17.5, while type Ia SNe deﬁne a ridgeline at MB = 19.5. − −

If one is searching for abnormally luminous (perhaps super-Chandrasekhar) type Ia SNe, Richardson et al. (2002) emphasize, one must choose a magnitude cut-oﬀ suﬃciently above this ridgeline to eliminate contamination due to normal statistical errors.

119 Gal-Yam (2012) chose to set the arbitrary threshold deﬁning a super-luminous supernova (SLSN) at MB = 21 mag, which is luminous enough to be unambiguously − over the type Ia ridgeline. We are interested in CCSNe rather than type Ia, however, and this is needlessly exclusionary. We choose a threshold of M = 20 mag. −

4.5. Conclusions

SNe, suggesting mass loss is a key factor for abnormally high luminosity.

Comparing the strong-line abundance estimate with the Te metallicity measurement for the few hosts for which it can be done will be an important next step towards establishing the utility of our strong-line population abundance estimate. Measuring metallicities with the weak temperature-sensitive [O III]λ4363 auroral lines is not feasible for most of these faint hosts because luminous CCSNe are rare and therefore on average distant. Their host galaxies are both intrinsically faint and far away. It is worth establishing whether the local relationship between the Te measurement and the PP04N2 abundance estimate holds for these extreme galaxies.

120 This paper uses data taken with the MODS spectrographs built with funding from NSF grant AST-9987045 and the NSF Telescope System Instrumentation

Program (TSIP), with additional funds from the Ohio Board of Regents and the

Ohio State University Oﬃce of Research.

121 Fig. 4.1.— Five spectra of luminous CCSNe and their underlying host galaxies are shown with the MODS blue channel in blue and the red channel in red.

122 Fig. 4.2.— Three MODS spectra of the host galaxies of luminous CCSNe. Only the red channel is shown. These spectra were taken during bright time.

123 Fig. 4.3.— Cumulative metallicity distributions for luminous CCSNe (blue solid) and normal SNe II (red dashed). Luminous CCSNe occur in dramatically lower metallicity environments than normal SNe II, with a K-S test probability of being drawn from the same underlying distribution of only 0.04%. Oxygen abundances were estimated using the PP04N2 method. Measurements of the 12 luminous CCSNe are from this paper (8) and the literature (4). The normal SNe II sample is from Stoll et al. (2012).

124 Fig. 4.4.— Cumulative [Fe/H] (blue solid) and [O/H] (red dashed) distributions for luminous CCSNe. At lower metallicity, α-elements like oxygen are enhanced relative to iron compared to the solar mixture. To guide the eye, the green dotted line is the oxygen abundance shifted left by 0.38 to line up with the iron at the high-metallicity end. Notice that even over this fairly small span of metallicities, the gap between the iron and oxygen abundance is much wider at the low-metallicity end of the distribution than it is at the high-metallicity end. Iron abundances were estimated from oxygen abundances following Stoll et al. (2012). Iron varies more steeply than oxygen, and a region that has low oxygen abundance has even lower iron abundance. Iron is a key source of opacity for massive star winds.

125 SN Name Type z slit slit Hα λ6563b [N II]λ6584b 12+log(O/H) [Fe/H] (arcsec) (kpc)a (PP04N2) (8.86)

SN1999bd IIn 0.151 1.4 3.7 58.81 1.33 21.17 1.02 8.69 0.06 -0.55 ± ± ± SN2007va IIn 0.190 2.0 6.3 45.28 2.27 1.84 0.92 8.12 0.60 -1.26 ± ± ± SN2008am IIn 0.234 2.0 7.4 120.03 2.65 21.94 3.19 8.43 0.18 -0.88 ± ± ± PTF11dij IIn 0.143 1.0 2.5 206.84 0.25 2.52 0.25 7.86 0.12 -1.60 ± ± ±

126 PTF11dsf IIn 0.385 1.6 8.4 35.68 0.66 0.35 0.20 7.78 0.67 -1.69 ± ± ± CSS111230 Ic 0.245 1.6 6.2 16.41 0.31 0.36 0.08 8.01 0.26 -1.41 ± ± ± PS1-12fo Ic 0.175 2.0 5.9 83.72 1.25 2.87 1.15 8.09 0.48 -1.30 ± ± ± PTF12dam Ic 0.107 1.6 3.1 2665.59 4.66 77.18 4.99 8.06 0.08 -1.34 ± ± ±

a Projected physical slit width calculated (Wright 2006) assuming H0 = 70, Ωm = 0.3, and ΩΛ = 0.7.

b10−17 erg cm−2 s−1 A˚−1

Table 4.1. Measured host metallicities SN Name RA Dec Type Redshift MB(host) 12+log[O/H] Source (J2000.0) (J2000.0) (mag) (PP04N2)

SN2003ma 05:31:01.9 –70:04:16 IIn 0.29 19.9 8.45 Rest et al. (2011) − SN2007bi 13:19:20.2 +08:55:44 Ic 0.13 16.3 8.18 Young et al. (2010) 127 − SN2007va 14:26:23.2 +35:35:29 IIn? 0.19 16.2 8.19 Koz lowski et al. (2010) − SN2008am 12:28:36.2 +15:34:49 IIn 0.234 19.6 8.38 Chatzopoulos et al. (2011) − SN2010gx 11:25:46.7 –07:49:41 Ic? 0.23 17.2 8.16 Stoll et al. (2011) − SN2010jl 09:42:53.3 +09:29:41.8 IIn 0.01 19.3 8.21 Stoll et al. (2011) −

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135 Appendix A: Metallicity conversions

It is well known that the various strong-line oxygen abundance estimators have diﬀerent scales and zero-points. (For excellent pictorial representations of this, see

Figure 2 of Kewley & Ellison (2008) or Figure 12 of Bresolin et al. (2009).) Because of these diﬀerences in scale and zero-point, it is critically necessary to put diﬀerent estimates on a common scale before comparing abundances. Kewley & Ellison (2008) determined empirical conversions between many of the commonly used scales by

ﬁtting the trend deﬁned by performing a given two diagnostics on a large sample of high S/N galaxy spectra from SDSS. While these conversions have been very useful to the community, there can be problems using them at very low metallicity. The forward and reverse conversions between two methods are not always consistent, or even monotonic, as can be seen by comparing the black solid and red dotted lines in

Figure A.1. The problem appears to be a consequence of the interaction between the vast statistical weight of the abundant high-metallicity galaxies and the third-order polynomials (the inversion of which cannot be precisely expressed as another third-order polynomial) in which the conversions are expressed. The high-metallicity end is tightly pinned, allowing the low-metallicity end of the third-order polynomial

136 to shift dramatically when the forward and reverse conversions are independently ﬁt to the data.

Preserving monotonicity in a conversion is important when converting a continuous distribution, as can be dramatically seen in Figure 3.10. We are also concerned about the compounding errors involved in multiple conversions between different scales, which is perhaps the most problematic negative consequence of the difference between the forward and reverse conversions, in that even the simplest conversion chain (to another scale and back again) results in a different metallicity distribution. For applications involving more than one conversion, invertability can be more important than the extra precision from fitting the forward and reverse conversions independently to the data.

For the purposes of this paper, we need to avoid the double-value of the non-monotonic T04 to PP04N2 conversion at low metallicities in order to convert the metallicity distribution of star-formation. We do so by defining ad hoc conversions from T04 to PP04N2 and from KK04 to PP04N2, as shown in Figure A.1. These conversions are third-order polynomial fits to the conversion from PP04N2 to T04 and from PP04N2 to KK04 given in Kewley & Ellison (2008). These fits are described by

y = a + bx + cx2 + dx3, (A.1)

137 where y = 12+log(O/H)PP04N2. For T04, x = 12+log(O/H)T04, a = 178.248, b = 59.2077, c = 6.80078, and d = 0.257326, and for KK04, x = 12+log(O/H)KK04, − − a = 1657.85, b = 583.307, c = 68.1052, and d = 2.65189. Note that these are fits − − to fits rather than fits to data, and they are equivalent to mathematically inverting the conversion from PP04N2 to T04 or KK04 and approximating it as a third-order polynomial. We define them solely for the purposes of avoiding a double-valued function at low metallicity and matching the inverse conversion from PP04N2 to

T04 and from PP04N2 to KK04.

138 Fig. A.1.— Forward and reverse conversions over the entire valid conversion ranges between the metallicity scale of Kobulnicky & Kewley (2004) and the N2 scale of Pettini & Pagel (2004) (left) and between Tremonti et al. (2004) and the N2 scale of Pettini & Pagel (2004) (right). To KK04 and T04 is the black solid line, while from KK04 and T04 is the red dotted line. The blue dashed line would represent a conversion between two exactly equivalent metallicity scales. The scales are the same as those in Figure 3 in Kewley & Ellison (2008). The green long-dashed lines show our ﬁts to the forward conversions, which we use in Figure 3.10 to convert from KK04 and T04 to PP04N2 in order to ensure monotonic behavior at low metallicities.

139