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Open Access Publications from the University of California
About Saul Perlmutter, UC Berkeley/Lawrence Berkeley Lab (Nobel Prize in Physics, 2011): TODO
Cover page of Redshift evolution of the underlying type Ia supernova stretch distribution

Redshift evolution of the underlying type Ia supernova stretch distribution

(2021)

The detailed nature of type Ia supernovae (SNe Ia) remains uncertain, and as survey statistics increase, the question of astrophysical systematic uncertainties arises, notably that of the evolution of SN Ia populations. We study the dependence on redshift of the SN Ia SALT2.4 light-curve stretch, which is a purely intrinsic SN property, to probe its potential redshift drift. The SN stretch has been shown to be strongly correlated with the SN environment, notably with stellar age tracers. We modeled the underlying stretch distribution as a function of redshift, using the evolution of the fraction of young and old SNe Ia as predicted using the SNfactory dataset, and assuming a constant underlying stretch distribution for each age population consisting of Gaussian mixtures. We tested our prediction against published samples that were cut to have marginal magnitude selection effects, so that any observed change is indeed astrophysical and not observational in origin. In this first study, there are indications that the underlying SN Ia stretch distribution evolves as a function of redshift, and that the age drifting model is a better description of the data than any time-constant model, including the sample-based asymmetric distributions that are often used to correct Malmquist bias at a significance higher than 5σ. The favored underlying stretch model is a bimodal one, composed of a high-stretch mode shared by both young and old environments, and a low-stretch mode that is exclusive to old environments. The precise effect of the redshift evolution of the intrinsic properties of a SN Ia population on cosmology remains to be studied. The astrophysical drift of the SN stretch distribution does affect current Malmquist bias corrections, however, and thereby the distances that are derived based on SN that are affected by observational selection effects. We highlight that this bias will increase with surveys covering increasingly larger redshift ranges, which is particularly important for the Large Synoptic Survey Telescope.

Cover page of Strong dependence of Type Ia supernova standardization on the local specific star formation rate

Strong dependence of Type Ia supernova standardization on the local specific star formation rate

(2020)

As part of an on-going effort to identify, understand and correct for astrophysics biases in the standardization of Type Ia supernovae (SN Ia) for cosmology, we have statistically classified a large sample of nearby SNe Ia into those that are located in predominantly younger or older environments. This classification is based on the specific star formation rate measured within a projected distance of 1 kpc from each SN location (LsSFR). This is an important refinement compared to using the local star formation rate directly, as it provides a normalization for relative numbers of available SN progenitors and is more robust against extinction by dust. We find that the SNe Ia in predominantly younger environments are ΔY = 0.163 ± 0.029 mag (5.7σ) fainter than those in predominantly older environments after conventional light-curve standardization. This is the strongest standardized SN Ia brightness systematic connected to the host-galaxy environment measured to date. The well-established step in standardized brightnesses between SNe Ia in hosts with lower or higher total stellar masses is smaller, at ΔM = 0.119 ± 0.032 mag (4.5σ), for the same set of SNe Ia. When fit simultaneously, the environment-age offset remains very significant, with ΔY = 0.129 ± 0.032 mag (4.0σ), while the global stellar mass step is reduced to ΔM = 0.064 ± 0.029 mag (2.2σ). Thus, approximately 70% of the variance from the stellar mass step is due to an underlying dependence on environment-based progenitor age. Also, we verify that using the local star formation rate alone is not as powerful as LsSFR at sorting SNe Ia into brighter and fainter subsets. Standardization that only uses the SNe Ia in younger environments reduces the total dispersion from 0.142 ± 0.008 mag to 0.120 ± 0.010 mag. We show that as environment-ages evolve with redshift, a strong bias, especially on the measurement of the derivative of the dark energy equation of state, can develop. Fortunately, data that measure and correct for this effect using our local specific star formation rate indicator, are likely to be available for many next-generation SN Ia cosmology experiments.

Cover page of Carnegie Supernova Project II: The Slowest Rising Type Ia Supernova LSQ14fmg and Clues to the Origin of Super-Chandrasekhar/03fg-like Events

Carnegie Supernova Project II: The Slowest Rising Type Ia Supernova LSQ14fmg and Clues to the Origin of Super-Chandrasekhar/03fg-like Events

(2020)

The Type Ia supernova (SN Ia) LSQ14fmg exhibits exaggerated properties that may help to reveal the origin of the "super-Chandrasekhar"(or 03fg-like) group. The optical spectrum is typical of a 03fg-like SN Ia, but the light curves are unlike those of any SNe Ia observed. The light curves of LSQ14fmg rise extremely slowly. At -23 rest-frame days relative to B-band maximum, LSQ14fmg is already brighter than MV = -19 mag before host extinction correction. The observed color curves show a flat evolution from the earliest observation to approximately 1 week after maximum. The near-infrared light curves peak brighter than -20.5 mag in the J and H bands, far more luminous than any 03fg-like SNe Ia with near-infrared observations. At 1 month past maximum, the optical light curves decline rapidly. The early, slow rise and flat color evolution are interpreted to result from an additional excess flux from a power source other than the radioactive decay of the synthesized 56Ni. The excess flux matches the interaction with a typical superwind of an asymptotic giant branch (AGB) star in density structure, mass-loss rate, and duration. The rapid decline starting at around 1 month past B-band maximum may be an indication of rapid cooling by active carbon monoxide (CO) formation, which requires a low-temperature and high-density environment. These peculiarities point to an AGB progenitor near the end of its evolution and the core degenerate scenario as the likely explosion mechanism for LSQ14fmg.

Cover page of SUGAR: An improved empirical model of Type Ia supernovae based on spectral features

SUGAR: An improved empirical model of Type Ia supernovae based on spectral features

(2020)

© P.-F. Léget et al. 2020. Context. Type Ia supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. Aims. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. Methods. This model was constructed from SNe Ia spectral properties and spectrophotometric data from the Nearby Supernova Factory collaboration. In a first step, a principal component analysis-like method was used on spectral features measured at maximum light, which allowed us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties were used to extract the average extinction curve. Third, an interpolation using Gaussian processes facilitated using data taken at different epochs during the lifetime of an SN Ia and then projecting the data on a fixed time grid. Finally, the three steps were combined to build the SED model as a function of time and wavelength. This is the SUGAR model. Results. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity and the second correlates with their calcium lines. The addition of these parameters, as well as the high quality of the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. Conclusions. The performance of this model makes it an excellent SED model for experiments like the Zwicky Transient Facility, the Large Synoptic Survey Telescope, or the Wide Field Infrared Survey Telescope.

Cover page of Precise Mass Determination of SPT-CL J2106-5844, the Most Massive Cluster at z > 1

Precise Mass Determination of SPT-CL J2106-5844, the Most Massive Cluster at z > 1

(2019)

© 2019. The American Astronomical Society. All rights reserved.. We present a detailed high-resolution weak-lensing study of SPT-CL J2106-5844 at z = 1.132, claimed to be the most massive system discovered at z > 1 in the South Pole Telescope Sunyaev-Zel'dovich survey. Based on the deep imaging data from the Advanced Camera for Surveys and Wide Field Camera 3 on board the Hubble Space Telescope, we find that the cluster mass distribution is asymmetric, composed of a main clump and a subclump ∼640 kpc west thereof. The central clump is further resolved into two smaller northwestern and southeastern substructures separated by ∼150 kpc. We show that this rather complex mass distribution is more consistent with the cluster galaxy distribution than a unimodal distribution as previously presented. The northwestern substructure coincides with the brightest cluster galaxy and the X-ray peak while the southeastern one agrees with the location of the peak in number density. These morphological features and the comparison with the X-ray emission suggest that the cluster might be a merging system. We estimate the virial mass of the cluster to be M200c =(10.4-3.0+3.3 ± 1.0) × 1014 M⊙, where the second error bar is the systematic uncertainty. Our result confirms that the cluster SPT-CL J2106-5844 is indeed the most massive cluster at z > 1 known to date. We demonstrate the robustness of this mass estimate by performing a number of tests with different assumptions on the centroids, mass-concentration relations, and sample variance.

Cover page of The Massive and Distant Clusters of WISE Survey. I. Survey Overview and a Catalog of >2000 Galaxy Clusters at z ≃ 1

The Massive and Distant Clusters of WISE Survey. I. Survey Overview and a Catalog of >2000 Galaxy Clusters at z ≃ 1

(2019)

© 2019. The American Astronomical Society. All rights reserved. We present the Massive and Distant Clusters of WISE Survey (MaDCoWS), a search for galaxy clusters at 0.7 ≲ z ≲ 1.5 based upon data from the Wide-field Infrared Survey Explorer (WISE) mission. MaDCoWS is the first cluster survey capable of discovering massive clusters at these redshifts over the full extragalactic sky. The search is divided into two regions - the region of the extragalactic sky covered by Pan-STARRS (δ > -30°) and the remainder of the southern extragalactic sky at δ < -30° for which shallower optical data from the SuperCOSMOS Sky Survey is available. In this paper, we describe the search algorithm, characterize the sample, and present the first MaDCoWS data release - catalogs of the 2433 highest amplitude detections in the WISE-Pan-STARRS region and the 250 highest amplitude detections in the WISE-SuperCOSMOS region. A total of 1723 of the detections from the WISE-Pan-STARRS sample have also been observed with the Spitzer Space Telescope, providing photometric redshifts and richnesses, and an additional 64 detections within the WISE-SuperCOSMOS region also have photometric redshifts and richnesses. Spectroscopic redshifts for 38 MaDCoWS clusters with IRAC photometry demonstrate that the photometric redshifts have an uncertainty of σ z /(1 + z) ≃ 0.036. Combining the richness measurements with Sunyaev-Zel'dovich observations of MaDCoWS clusters, we also present a preliminary mass-richness relation that can be used to infer the approximate mass distribution of the full sample. The estimated median mass for the WISE-Pan-STARRS catalog is , with the Sunyaev-Zel'dovich data confirming that we detect clusters with masses up to M 500 ∼ 5 ×10 14 M (M 200 ∼ 10 15 M ).

Carnegie supernova project-II: Extending the near-infrared hubble diagram for type ia supernovae to z∼0.1

(2019)

The Carnegie Supernova Project-II (CSP-II) was an NSF-funded, four-year program to obtain optical and near-infrared observations of a “Cosmology” sample of ∼100 TypeIa supernovae located in the smooth Hubble flow (0.03<z<0.10). Light curves were also obtained of a “Physics” sample composed of 90 nearby TypeIa supernovae at z≤0.04 selected for near-infrared spectroscopic timeseries observations. The primary emphasis of the CSP-II is to use the combination of optical and near-infrared photometry to achieve a distance precision of better than 5%. In this paper, details of the supernova sample, the observational strategy, and the characteristics of the photometric data are provided. In a companion paper, the near-infrared spectroscopy component of the project is presented.

Cover page of SN 2012dn from early to late times: 09dc-like supernovae reassessed

SN 2012dn from early to late times: 09dc-like supernovae reassessed

(2019)

As a candidate 'super-Chandrasekhar' or 09dc-like Type Ia supernova (SN Ia), SN 2012dn shares many characteristics with other members of this remarkable class of objects but lacks their extraordinary luminosity. Here, we present and discuss the most comprehensive optical data set of this SN to date, comprised of a densely sampled series of early-time spectra obtained within the Nearby Supernova Factory project, plus photometry and spectroscopy obtained at the Very Large Telescope about 1 yr after the explosion. The light curves, colour curves, spectral time series, and ejecta velocities of SN 2012dn are compared with those of other 09dc-like and normal SNe Ia, the overall variety within the class of 09dc-like SNe Ia is discussed, and new criteria for 09dc-likeness are proposed. Particular attention is directed to additional insight that the late-phase data provide. The nebular spectra show forbidden lines of oxygen and calcium, elements that are usually not seen in late-time spectra of SNe Ia, while the ionization state of the emitting iron plasma is low, pointing to low ejecta temperatures and high densities. The optical light curves are characterized by an enhanced fading starting ∼60 d after maximum and very low luminosities in the nebular phase, which is most readily explained by unusually early formation of clumpy dust in the ejecta. Taken together, these effects suggest a strongly perturbed ejecta density profile, which might lend support to the idea that 09dc-like characteristics arise from a brief episode of interaction with a hydrogen-deficient envelope during the first hours or days after the explosion.

Carnegie supernova project-II: The near-infrared spectroscopy program

(2019)

Shifting the focus of Type Ia supernova (SN Ia) cosmology to the near infrared (NIR) is a promising way to significantly reduce the systematic errors, as the strategy minimizes our reliance on the empirical width-luminosity relation and uncertain dust laws. Observations in the NIR are also crucial for our understanding of the origins and evolution of these events, further improving their cosmological utility. Any future experiments in the rest-frame NIR will require knowledge of the SN Ia NIR spectroscopic diversity, which is currently based on a small sample of observed spectra. Along with the accompanying paper, Phillips et al., we introduce the Carnegie Supernova Project-II (CSP-II), to follow-up nearby SNe Ia in both the optical and the NIR. In particular, this paper focuses on the CSP-II NIR spectroscopy program, describing the survey strategy, instrumental setups, data reduction, sample characteristics, and future analyses on the data set. In collaboration with the Harvard-Smithsonian Center for Astrophysics (CfA) Supernova Group, we obtained 661 NIR spectra of 157 SNe Ia. Within this sample, 451 NIR spectra of 90 SNe Ia have corresponding CSP-II follow-up light curves. Such a sample will allow detailed studies of the NIR spectroscopic properties of SNe Ia, providing a different perspective on the properties of the unburned material; the radioactive and stable nickel produced; progenitor magnetic fields; and searches for possible signatures of companion stars.

Cover page of SNEMO: Improved Empirical Models for Type Ia Supernovae

SNEMO: Improved Empirical Models for Type Ia Supernovae

(2018)

SN Ia cosmology depends on the ability to fit and standardize observations of supernova magnitudes with an empirical model. We present here a series of new models of SN Ia spectral time series that capture a greater amount of supernova diversity than is possible with the models that are currently customary. These are entitled SuperNova Empirical MOdels (SNEMO; https://snfactory.lbl.gov/snemo). The models are constructed using spectrophotometric time series from 172 individual supernovae from the Nearby Supernova Factory, comprising more than 2000 spectra. Using the available observations, Gaussian processes are used to predict a full spectral time series for each supernova. A matrix is constructed from the spectral time series of all the supernovae, and Expectation Maximization Factor Analysis is used to calculate the principal components of the data. K-fold cross-validation then determines the selection of model parameters and accounts for color variation in the data. Based on this process, the final models are trained on supernovae that have been dereddened using the Fitzpatrick and Massa extinction relation. Three final models are presented here: SNEMO2, a two-component model for comparison with current Type Ia models; SNEMO7, a seven-component model chosen for standardizing supernova magnitudes, which results in a total dispersion of 0.100 mag for a validation set of supernovae, of which 0.087 mag is unexplained (a total dispersion of 0.113 mag with an unexplained dispersion of 0.097 mag is found for the total set of training and validation supernovae); and SNEMO15, a comprehensive 15-component model that maximizes the amount of spectral time-series behavior captured.