Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift ...samples (z< 0.1), all three seasons from the SDSS-II (0.05 <z< 0.4), and three years from SNLS (0.2 <z< 1), and it totals 740 spectroscopically confirmed type Ia supernovae with high-quality light curves. Methods. We followed the methods and assumptions of the SNLS three-year data analysis except for the following important improvements: 1) the addition of the full SDSS-II spectroscopically-confirmed SN Ia sample in both the training of the SALT2 light-curve model and in the Hubble diagram analysis (374 SNe); 2) intercalibration of the SNLS and SDSS surveys and reduced systematic uncertainties in the photometric calibration, performed blindly with respect to the cosmology analysis; and 3) a thorough investigation of systematic errors associated with the SALT2 modeling of SN Ia light curves. Results. We produce recalibrated SN Ia light curves and associated distances for the SDSS-II and SNLS samples. The large SDSS-II sample provides an effective, independent, low-z anchor for the Hubble diagram and reduces the systematic error from calibration systematics in the low-z SN sample. For a flat ΛCDM cosmology, we find Ωm =0.295 ± 0.034 (stat+sys), a value consistent with the most recent cosmic microwave background (CMB) measurement from the Planck and WMAP experiments. Our result is 1.8σ (stat+sys) different than the previously published result of SNLS three-year data. The change is due primarily to improvements in the SNLS photometric calibration. When combined with CMB constraints, we measure a constant dark-energy equation of state parameter w =−1.018 ± 0.057 (stat+sys) for a flat universe. Adding baryon acoustic oscillation distance measurements gives similar constraints: w =−1.027 ± 0.055. Our supernova measurements provide the most stringent constraints to date on the nature of dark energy.
We present photometric and spectroscopic observations of SN 2007if, an overluminous (M_V = -20.4), red (B-V = 0.16 at B-band maximum), slow-rising (t_rise = 24 days) type Ia supernova in a very faint ...(M_g = -14.10) host galaxy. A spectrum at 5 days past B-band maximum light is a direct match to the super-Chandrasekhar-mass candidate SN Ia 2003fg, showing Si II and C II at ~9000 km/s. A high signal-to-noise co-addition of the SN spectral time series reveals no Na I D absorption, suggesting negligible reddening in the host galaxy, and the late-time color evolution has the same slope as the Lira relation for normal SNe Ia. The ejecta appear to be well mixed, with no strong maximum in I-band and a diversity of iron-peak lines appearing in near-maximum-light spectra. SN2007 if also displays a plateau in the Si II velocity extending as late as +10 days, which we interpret as evidence for an overdense shell in the SN ejecta. We calculate the bolometric light curve of the SN and use it and the \ion{Si}{2} velocity evolution to constrain the mass of the shell and the underlying SN ejecta, and demonstrate that SN2007 if is strongly inconsistent with a Chandrasekhar-mass scenario. Within the context of a "tamped detonation" model appropriate for double-degenerate mergers, and assuming no host extinction, we estimate the total mass of the system to be 2.4 +/- 0.2 solar masses, with 1.6 +/- 0.1 solar masses of nickel-56 and with 0.3-0.5 solar masses in the form of an envelope of unburned carbon/oxygen. Our modeling demonstrates that the kinematics of shell entrainment provide a more efficient mechanism than incomplete nuclear burning for producing the low velocities typical of super-Chandrasekhar-mass SNeIa.
Abstract
We show how spectra of Type Ia supernovae (SNe Ia) at maximum light can be used to improve cosmological distance estimates. In a companion article, we used manifold learning to build a ...three-dimensional parameterization of the intrinsic diversity of SNe Ia at maximum light that we call the “Twins Embedding.” In this article, we discuss how the Twins Embedding can be used to improve the standardization of SNe Ia. With a single spectrophotometrically calibrated spectrum near maximum light, we can standardize our sample of SNe Ia with an rms of 0.101 ± 0.007 mag, which corresponds to 0.084 ± 0.009 mag if peculiar velocity contributions are removed and to 0.073 ± 0.008 mag if a larger reference sample were obtained. Our techniques can standardize the full range of SNe Ia, including those typically labeled as peculiar and often rejected from other analyses. We find that traditional light-curve width + color standardization such as SALT2 is not sufficient. The Twins Embedding identifies a subset of SNe Ia, including, but not limited to, 91T-like SNe Ia whose SALT2 distance estimates are biased by 0.229 ± 0.045 mag. Standardization using the Twins Embedding also significantly decreases host-galaxy correlations. We recover a host mass step of 0.040 ± 0.020 mag compared to 0.092 ± 0.026 mag for SALT2 standardization on the same sample of SNe Ia. These biases in traditional standardization methods could significantly impact future cosmology analyses if not properly taken into account.
We use simulated type Ia supernova (SN Ia) samples, including both photometry and spectra, to perform the first direct validation of cosmology analysis using the SALT-II light curve model. This ...validation includes residuals from the light curve training process, systematic biases in SN Ia distance measurements, and a bias on the dark energy equation of state parameter w. Using the SN-analysis package SNANA, we simulate and analyze realistic samples corresponding to the data samples used in the SNLS3 analysis: ~120 low-redshift (z < 0.1) SNe Ia, ~255 Sloan Digital Sky Survey SNe Ia (z < 0.4), and ~290 SNLS SNe Ia (z < or =, slant 1). To probe systematic uncertainties in detail, we vary the input spectral model, the model of intrinsic scatter, and the smoothing (i.e., regularization) parameters used during the SALT-II model training. Using realistic intrinsic scatter models results in a slight bias in the ultraviolet portion of the trained SALT-II model, and w biases (w sub(input) - w sub(recovered)) ranging from -0.005 + or - 0.012 to -0.024 + or - 0.010. These biases are indistinguishable from each other within the uncertainty; the average bias on w is -0.014 + or - 0.007.
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 Δ
Y
= 0.163 ± 0.029 mag (5.7
σ
) 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 Δ
M
= 0.119 ± 0.032 mag (4.5
σ
), for the same set of SNe Ia. When fit simultaneously, the environment-age offset remains very significant, with Δ
Y
= 0.129 ± 0.032 mag (4.0
σ
), while the global stellar mass step is reduced to Δ
M
= 0.064 ± 0.029 mag (2.2
σ
). 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.
Abstract
We study the spectral diversity of Type Ia supernovae (SNe Ia) at maximum light using high signal-to-noise spectrophotometry of 173 SNe Ia from the Nearby Supernova Factory. We decompose the ...diversity of these spectra into different extrinsic and intrinsic components, and we construct a nonlinear parameterization of the intrinsic diversity of SNe Ia that preserves pairings of “twin” SNe Ia. We call this parameterization the “Twins Embedding.” Our methodology naturally handles highly nonlinear variability in spectra, such as changes in the photosphere expansion velocity, and uses the full spectrum rather than being limited to specific spectral line strengths, ratios, or velocities. We find that the time evolution of SNe Ia near maximum light is remarkably similar, with 84.6% of the variance in common to all SNe Ia. After correcting for brightness and color, the intrinsic variability of SNe Ia is mostly restricted to specific spectral lines, and we find intrinsic dispersions as low as ∼0.02 mag between 6600 and 7200 Å. With a nonlinear three-dimensional model plus one dimension for color, we can explain 89.2% of the intrinsic diversity in our sample of SNe Ia, which includes several different kinds of “peculiar” SNe Ia. A linear model requires seven dimensions to explain a comparable fraction of the intrinsic diversity. We show how a wide range of previously established indicators of diversity in SNe Ia can be recovered from the Twins Embedding. In a companion article, we discuss how these results can be applied to the standardization of SNe Ia for cosmology.
Context. The Public European Southern Observatory Spectroscopic Survey of Transient Objects (PESSTO) began as a public spectroscopic survey in April 2012. PESSTO classifies transients from publicly ...available sources and wide-field surveys, and selects science targets for detailed spectroscopic and photometric follow-up. PESSTO runs for nine months of the year, January – April and August – December inclusive, and typically has allocations of 10 nights per month. Aims. We describe the data reduction strategy and data products that are publicly available through the ESO archive as the Spectroscopic Survey data release 1 (SSDR1). Methods. PESSTO uses the New Technology Telescope with the instruments EFOSC2 and SOFI to provide optical and NIR spectroscopy and imaging. We target supernovae and optical transients brighter than 20.5m for classification. Science targets are selected for follow-up based on the PESSTO science goal of extending knowledge of the extremes of the supernova population. We use standard EFOSC2 set-ups providing spectra with resolutions of 13–18 Å between 3345−9995 Å. A subset of the brighter science targets are selected for SOFI spectroscopy with the blue and red grisms (0.935−2.53 μm and resolutions 23−33 Å) and imaging with broadband JHKs filters. Results. This first data release (SSDR1) contains flux calibrated spectra from the first year (April 2012–2013). A total of 221 confirmed supernovae were classified, and we released calibrated optical spectra and classifications publicly within 24 h of the data being taken (via WISeREP). The data in SSDR1 replace those released spectra. They have more reliable and quantifiable flux calibrations, correction for telluric absorption, and are made available in standard ESO Phase 3 formats. We estimate the absolute accuracy of the flux calibrations for EFOSC2 across the whole survey in SSDR1 to be typically ~15%, although a number of spectra will have less reliable absolute flux calibration because of weather and slit losses. Acquisition images for each spectrum are available which, in principle, can allow the user to refine the absolute flux calibration. The standard NIR reduction process does not produce high accuracy absolute spectrophotometry but synthetic photometry with accompanying JHKs imaging can improve this. Whenever possible, reduced SOFI images are provided to allow this. Conclusions. Future data releases will focus on improving the automated flux calibration of the data products. The rapid turnaround between discovery and classification and access to reliable pipeline processed data products has allowed early science papers in the first few months of the survey.
We present a new compilation of Type Ia supernovae (SNe Ia), a new data set of low-redshift nearby-Hubble-flow SNe, and new analysis procedures to work with these heterogeneous compilations. This ...'Union' compilation of 414 SNe Ia, which reduces to 307 SNe after selection cuts, includes the recent large samples of SNe Ia from the Supernova Legacy Survey and ESSENCE Survey, the older data sets, as well as the recently extended data set of distant supernovae observed with the Hubble Space Telescope (HST). A single, consistent, and blind analysis procedure is used for all the various SN Ia subsamples, and a new procedure is implemented that consistently weights the heterogeneous data sets and rejects outliers. We present the latest results from this Union compilation and discuss the cosmological constraints from this new compilation and its combination with other cosmological measurements (CMB and BAO). The constraint we obtain from supernovae on the dark energy density is image, for a flat, Lambda CDM universe. Assuming a constant equation of state parameter, w, the combined constraints from SNe, BAO, and CMB give image. While our results are consistent with a cosmological constant, we obtain only relatively weak constraints on a w that varies with redshift. In particular, the current SN data do not yet significantly constrain w at image. With the addition of our new nearby Hubble-flow SNe Ia, these resulting cosmological constraints are currently the tightest available.
We present a measurement of the volumetric rate of superluminous supernovae (SLSNe) at z ~ 1.0, measured using archival data from the first four years of the Canada-France-Hawaii Telescope Supernova ...Legacy Survey (SNLS). We develop a method for the photometric classification of SLSNe to construct our sample. Our sample includes two previously spectroscopically identified objects, and a further new candidate selected using our classification technique. We use the point-source recovery efficiencies from Perrett et al. and a Monte Carlo approach to calculate the rate based on our SLSN sample. We find that the three identified SLSNe from SNLS give a rate of 91... SNe yr super( -1) Gpc super( -3) at a volume-weighted redshift of z = 1.13. This is equivalent to 2.2...x10 super( -4) of the volumetric core-collapse supernova rate at the same redshift. When combined with other rate measurements from the literature, we show that the rate of SLSNe increases with redshift in a manner consistent with that of the cosmic star formation history. We also estimate the rate of ultra-long gamma-ray bursts based on the events discovered by the Swift satellite, and show that it is comparable to the rate of SLSNe, providing further evidence of a possible connection between these two classes of events. We also examine the host galaxies of the SLSNe discovered in SNLS, and find them to be consistent with the stellar-mass distribution of other published samples of SLSNe. (ProQuest: ... denotes formulae/symbols omitted.)
We present a new atmospheric extinction curve for Mauna Kea spanning 3200–9700 Å. It is the most comprehensive to date, being based on some 4285 standard star spectra obtained on 478 nights spread ...over a period of 7 years obtained by the Nearby SuperNova Factory using the SuperNova Integral Field Spectrograph. This mean curve and its dispersion can be used as an aid in calibrating spectroscopic or imaging data from Mauna Kea, and in estimating the calibration uncertainty associated with the use of a mean extinction curve. Our method for decomposing the extinction curve into physical components, and the ability to determine the chromatic portion of the extinction even on cloudy nights, is described and verified over the wide range of conditions sampled by our large dataset. We demonstrate good agreement with atmospheric science data obtain at nearby Mauna Loa Observatory, and with previously published measurements of the extinction above Mauna Kea.
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