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Hubert Lampeitl

Publications and source records attributed to Hubert Lampeitl.

At least 19 recordsLinked to original sources

Host Galaxy Spectra and Consequences for SN Typing From The SDSS SN Survey

We present the spectroscopy from 5254 galaxies that hosted supernovae (SNe) or other transient events in the Sloan Digital Sky Survey II (SDSS-II). Obtained during SDSS-I, SDSS-II, and the Baryon Oscillation Spectroscopic Survey (BOSS), this sample represents the largest systematic, unbiased, magnitude limited spectroscopic survey of supernova (SN) host galaxies. Using the host galaxy redshifts, we test the impact of photometric SN classification based on SDSS imaging data with and without using spectroscopic redshifts of the host galaxies. Following our suggested scheme, there are a total of 1166 photometrically classified SNe Ia when using a flat redshift prior and 1126 SNe Ia when the host spectroscopic redshift is assumed. For 1024 (87.8%) candidates classified as likely SNe Ia without redshift information, we find that the classification is unchanged when adding the host galaxy redshift. Using photometry from SDSS imaging data and the host galaxy spectra, we also report host galaxy properties for use in future nalysis of SN astrophysics. Finally, we investigate the differences in the interpretation of the light curve properties with and without knowledge of the redshift. When using the SALT2 light curve fitter, we find a 21% increase in the number of fits that converge when using the spectroscopic redshift. Without host galaxy redshifts, we find that SALT2 light curve fits are systematically biased towards lower photometric redshift estimates and redder colors in the limit of low signal-to-noise data. The general improvements in performance of the light curve fitter and the increased diversity of the host galaxy sample highlights the importance of host galaxy spectroscopy for current photometric SN surveys such as the Dark Energy Survey and future surveys such as the Large Synoptic Survey Telescope.

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Cosmology with Photometrically-Classified Type Ia Supernovae from the SDSS-II Supernova Survey

We present the cosmological analysis of 752 photometrically-classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). Our photometric-classification method is based on the SN typing technique of Sako et al. (2011), aided by host galaxy redshifts (0.05<z<0.55). SNANA simulations of our methodology estimate that we have a SN Ia typing efficiency of 70.8%, with only 3.9% contamination from core-collapse (non-Ia) SNe. We demonstrate that this level of contamination has no effect on our cosmological constraints. We quantify and correct for our selection effects (e.g., Malmquist bias) using simulations. When fitting to a flat LambdaCDM cosmological model, we find that our photometric sample alone gives omega_m=0.24+0.07-0.05 (statistical errors only). If we relax the constraint on flatness, then our sample provides competitive joint statistical constraints on omega_m and omega_lambda, comparable to those derived from the spectroscopically-confirmed three-year Supernova Legacy Survey (SNLS3). Using only our data, the statistics-only result favors an accelerating universe at 99.96% confidence. Assuming a constant wCDM cosmological model, and combining with H0, CMB and LRG data, we obtain w=-0.96+0.10-0.10, omega_m=0.29+0.02-0.02 and omega_k=0.00+0.03-0.02 (statistical errors only), which is competitive with similar spectroscopically confirmed SNe Ia analyses. Overall this comparison is re-assuring, considering the lower redshift leverage of the SDSS-II SN sample (z<0.55) and the lack of spectroscopic confirmation used herein. These results demonstrate the potential of photometrically-classified SNe Ia samples in improving cosmological constraints.

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SNe Ia host galaxy properties from Sloan Digital Sky Survey-II spectroscopy

We study the stellar populations of SNe Ia host galaxies using SDSS-II spectroscopy. We focus on the relationships of SNe Ia properties with stellar velocity dispersion and the stellar population parameters age, metallicity and element abundance ratios derived by fitting absorption line indices to stellar population models. We concentrate on a sub-sample of 84 SNe Ia from the SDSS-II Supernova Survey. In agreement with previous findings, we find that SALT2 stretch factor values show the strongest dependence on stellar population age. Hence, SNe Ia peak-luminosity is closely related to the age of the stellar progenitor systems, where more luminous SNe Ia appear in younger stellar populations. We find no statistically significant trends in the Hubble residual with any of the stellar population parameters studied, including age and metallicity contrary to the literature, as well as with stellar velocity dispersion. Moreover, we find that the method of stellar mass derivation is affecting the Hubble residual-mass relationship when lower number statistics are used. We extend the sample to also include SNe Ia with available SDSS host galaxy photometry only. For this larger sample (247 objects) the reported Hubble residual-mass relation is strongly dependent on the stellar mass range studied and behaves as a step function. In the high mass regime, probed by our host spectroscopy sample, the relation between Hubble residual and stellar mass is flat. Below a stellar mass of ~2x10^10 Msun, i.e. close to the evolutionary transition mass of low-redshift galaxies reported in the literature, the trend changes dramatically such that lower mass galaxies possess lower luminosity SNe Ia after light-curve corrections. This non-linear behaviour of the Hubble residual-mass relationship should be accounted for when using stellar mass as a further parameter for minimising the Hubble residuals.

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A Mismatch in the Ultraviolet Spectra between Low-Redshift and Intermediate-Redshift Type Ia Supernovae as a Possible Systematic Uncertainty for Supernova Cosmology

We present Keck high-quality rest-frame ultraviolet (UV) through optical spectra of 21 Type Ia supernovae (SNe Ia) in the redshift range 0.11 < z < 0.37 and a mean redshift of 0.22 that were discovered during the Sloan Digital Sky Survey-II (SDSS-II) SN Survey. Using the broad-band photometry of the SDSS survey, we are able to reconstruct the SN host-galaxy spectral energy distributions (SEDs), allowing for a correction for the host-galaxy contamination in the SN Ia spectra. Comparison of composite spectra constructed from a subsample of 17 high-quality spectra to those created from a low-redshift sample with otherwise similar properties shows that the Keck/SDSS SNe Ia have, on average, extremely similar rest-frame optical spectra but show a UV flux excess. This observation is confirmed by comparing synthesized broad-band colors of the individual spectra, showing a difference in mean colors at the 2.4 - 4.4 sigma level for various UV colors. We further see a slight difference in the UV spectral shape between SNe with low-mass and high-mass host galaxies. Additionally, we detect a relationship between the flux ratio at 2770 and 2900 A and peak luminosity that differs from that observed at low redshift. We find that changing the UV SED of an SN Ia within the observed dispersion can change the inferred distance moduli by ~0.1 mag. This effect only occurs when the data probe the rest-frame UV. We suggest that this discrepancy could be due to differences in the host-galaxy population of the two SN samples or to small-sample statistics.

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The SDSS Coadd: 275 deg^2 of Deep SDSS Imaging on Stripe 82

We present details of the construction and characterization of the coaddition of the Sloan Digital Sky Survey Stripe 82 \ugriz\ imaging data. This survey consists of 275 deg$^2$ of repeated scanning by the SDSS camera of $2.5\arcdeg$ of $δ$ over $-50\arcdeg \le α\le 60\arcdeg$ centered on the Celestial Equator. Each piece of sky has $\sim 20$ runs contributing and thus reaches $\sim2$ magnitudes fainter than the SDSS single pass data, i.e. to $r\sim 23.5$ for galaxies. We discuss the image processing of the coaddition, the modeling of the PSF, the calibration, and the production of standard SDSS catalogs. The data have $r$-band median seeing of 1.1\arcsec, and are calibrated to $\le 1%$. Star color-color, number counts, and psf size vs modelled size plots show the modelling of the PSF is good enough for precision 5-band photometry. Structure in the psf-model vs magnitude plot show minor psf mis-modelling that leads to a region where stars are being mis-classified as galaxies, and this is verified using VVDS spectroscopy. As this is a wide area deep survey there are a variety of uses for the data, including galactic structure, photometric redshift computation, cluster finding and cross wavelength measurements, weak lensing cluster mass calibrations, and cosmic shear measurements.

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Photometric Supernova Cosmology with BEAMS and SDSS-II

Supernova cosmology without spectroscopic confirmation is an exciting new frontier which we address here with the Bayesian Estimation Applied to Multiple Species (BEAMS) algorithm and the full three years of data from the Sloan Digital Sky Survey II Supernova Survey (SDSS-II SN). BEAMS is a Bayesian framework for using data from multiple species in statistical inference when one has the probability that each data point belongs to a given species, corresponding in this context to different types of supernovae with their probabilities derived from their multi-band lightcurves. We run the BEAMS algorithm on both Gaussian and more realistic SNANA simulations with of order 10^4 supernovae, testing the algorithm against various pitfalls one might expect in the new and somewhat uncharted territory of photometric supernova cosmology. We compare the performance of BEAMS to that of both mock spectroscopic surveys and photometric samples which have been cut using typical selection criteria. The latter typically are either biased due to contamination or have significantly larger contours in the cosmological parameters due to small data-sets. We then apply BEAMS to the 792 SDSS-II photometric supernovae with host spectroscopic redshifts. In this case, BEAMS reduces the area of the (Ω_m,Ω_Λ) contours by a factor of three relative to the case where only spectroscopically confirmed data are used (297 supernovae). In the case of flatness, the constraints obtained on the matter density applying BEAMS to the photometric SDSS-II data are Ω_m(BEAMS)=0.194\pm0.07. This illustrates the potential power of BEAMS for future large photometric supernova surveys such as LSST.

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Spectroscopic Properties of Star-Forming Host Galaxies and Type Ia Supernova Hubble Residuals in a Nearly Unbiased Sample

We examine the correlation between supernova host galaxy properties and their residuals on the Hubble diagram. We use supernovae discovered during the Sloan Digital Sky Survey II - Supernova Survey, and focus on objects at a redshift of z < 0.15, where the selection effects of the survey are known to yield a complete Type Ia supernova sample. To minimize the bias in our analysis with respect to measured host-galaxy properties, spectra were obtained for nearly all hosts, spanning a range in magnitude of -23 < M_r < -17. In contrast to previous works that use photometric estimates of host mass as a proxy for global metallicity, we analyze host-galaxy spectra to obtain gas-phase metallicities and star-formation rates from host galaxies with active star formation. From a final sample of ~ 40 emission-line galaxies, we find that light-curve corrected Type Ia supernovae are ~ 0.1 magnitudes brighter in high-metallicity hosts than in low-metallicity hosts. We also find a significant (> 3σ) correlation between the Hubble residuals of Type Ia supernovae and the specific star-formation rate of the host galaxy. We comment on the importance of supernova/host-galaxy correlations as a source of systematic bias in future deep supernova surveys.

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The SDSS-II Supernova Survey: Parameterizing the Type Ia Supernova Rate as a Function of Host Galaxy Properties

Using data from the Sloan Digital Sky Supernova Survey-II, we measure the rate of Type Ia Supernovae (SNe Ia) as a function of galaxy properties at intermediate redshift. A sample of 342 SNe Ia with 0.05<z<0.25 is constructed. Using broad-band photometry we use the PEGASE spectral energy distributions (SEDs) to estimate host galaxy stellar masses and recent star-formation rates. We find that the rate of SNe Ia per unit stellar mass is significantly higher (by a factor of ~30) in highly star-forming galaxies compared to passive galaxies. When parameterizing the SN Ia rate (SNR_Ia) based on host galaxy properties, we find that the rate of SNe Ia in passive galaxies is not linearly proportional to the stellar mass, instead a SNR_Ia proportional to M^0.68 is favored. However, such a parameterization does not describe the observed SN Ia rate in star-forming galaxies. The SN Ia rate in star-forming galaxies is well fit by SNR_Ia = 1.05\pm0.16x10^{-10} M ^{0.68\pm0.01} + 1.01\pm0.09x10^{-3} SFR^{1.00\pm0.05} (statistical errors only), where M is the host galaxy mass and SFR is the star-formation rate. These results are insensitive to the selection criteria used, redshift limit considered and the inclusion of non-spectroscopically confirmed SNe Ia. We also show there is a dependence between the distribution of the MLCS light-curve decline rate parameter, Δ, and host galaxy type. Passive galaxies host less luminous SNe Ia than seen in moderately and highly star-forming galaxies, although a population of luminous SNe is observed in passive galaxies, contradicting previous assertions that these SNe Ia are only observed in younger stellar systems. The MLCS extinction parameter, A_V, is similar in passive and moderately star-forming galaxies, but we find indications that it is smaller, on average, in highly star-forming galaxies. We confirm these results using the SALT2 light-curve fitter.

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Improved Constraints on Type Ia Supernova Host Galaxy Properties using Multi-Wavelength Photometry and their Correlations with Supernova Properties

We improve estimates of stellar mass and mass-weighted average age of Type Ia supernova (SN Ia) host galaxies by combining UV and near-IR photometry with optical photometry in our analysis. Using 206 SNe Ia drawn from the full three-year SDSS-II Supernova Survey (median redshift of z {\approx} 0.2) and multi-wavelength host-galaxy photometry from SDSS, GALEX, and UKIDSS, we present evidence of a correlation (1.9σ confidence level) between the residuals of SNe Ia about the best-fit Hubble relation and the mass-weighted average age of their host galaxies. The trend is such that older galaxies host SNe Ia that are brighter than average after standard light-curve corrections are made. We also confirm, at the 3.0σ level, the trend seen by previous studies that more massive galaxies often host brighter SNe Ia after light-curve correction.

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Photometric SN Ia Candidates from the Three-Year SDSS-II SN Survey Data

We analyze the three-year SDSS-II Superernova (SN) Survey data and identify a sample of 1070 photometric SN Ia candidates based on their multi-band light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured, while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with ~91% efficiency and with a contamination of ~6%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only ~20 - 40% larger than that of the spectroscopically-confirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibit biases that require further investigation for precision cosmology.

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A More General Model for the Intrinsic Scatter in Type Ia Supernova Distance Moduli

We describe a new formalism to fit the parameters $α$ and $β$ that are used in the SALT2 model to determine the standard magnitudes of Type Ia supernovae. The new formalism describes the intrinsic scatter in Type Ia supernovae by a covariance matrix in place of the single parameter normally used. We have applied this formalism to the Sloan Digital Sky Survey Supernova Survey (SDSS-II) data and conclude that the data are best described by $α=0.135_{-.017}^{+.033}$ and $β=3.19_{-0.24}^{+0.14}$, where the error is dominated by the uncertainty in the form of the intrinsic scatter matrix. Our result depends on the introduction of a more general form for the intrinsic scatter of the distance moduli of Type Ia supernovae than is conventional, resulting in a larger value of $β$ and a larger uncertainty than the conventional approach. Although this analysis results in a larger value of $β$ and a larger error, the SDSS data differ (at a 98% confidence level) with $β=4.1$, the value expected for extinction by the type of dust found in the Milky Way. We have modeled the distribution of supernovae Ia in terms of their color and conclude that there is strong evidence that variation in color is a significant contributor to the scatter of supernovae Ia around their standard candle magnitude.

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A 3% Solution: Determination of the Hubble Constant with the Hubble Space Telescope and Wide Field Camera 3

We use the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope to determine the Hubble constant (H0) from optical and infrared observations of over 600 Cepheid variables in the host galaxies of 8 recent Type Ia supernovae (SNe Ia), providing the calibration for a mag-z relation of 253 SNe Ia. Increased precision over past measurements comes from: (1) more than doubling the number of infrared observations of Cepheids in nearby SN hosts; (2) increasing the sample of ideal SN Ia calibrators from six to eight; (3) increasing by 20% the number of Cepheids with infrared observations in the megamaser host NGC 4258; (4) reducing the difference in the mean metallicity of the Cepheid comparison samples from Δlog [O/H] = 0.08 to 0.05; and (5) calibrating all optical Cepheid colors with one camera, WFC3, to remove cross-instrument zero-point errors. Uncertainty in H0 from beyond the 1st rung of the distance ladder is reduced from 3.5% to 2.3%. The measurement of H0 via the geometric distance to NGC 4258 is 74.8 \pm 3.1 km s- 1 Mpc-1, a 4.1% measurement including systematics. Better precision independent of NGC 4258 comes from two alternative Cepheid absolute calibrations: (1) 13 Milky Way Cepheids with parallaxes and (2) 92 Cepheids in the Large Magellanic Cloud with multiple eclipsing binary distances, yielding 74.4 \pm 2.5 km s- 1 Mpc-1, a 3.4% uncertainty with systematics. Our best estimate uses all three calibrations but a larger uncertainty afforded from any two: H0 = 73.8 \pm 2.4 km s- 1 Mpc-1 including systematics, a 3.3% uncertainty. The improvement in H0, combined with WMAP7yr data, results in a constraint on the EOS parameter of dark energy of w = -1.08 \pm 0.10 and Neff = 4.2 \pm 0.7 for the number of relativistic species in the early universe. It also rules out the best-fitting gigaparsec-scale void models, posited as an alternative to dark energy. (abridged)

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The Eighth Data Release of the Sloan Digital Sky Survey: First Data from SDSS-III

The Sloan Digital Sky Survey (SDSS) started a new phase in August 2008, with new instrumentation and new surveys focused on Galactic structure and chemical evolution, measurements of the baryon oscillation feature in the clustering of galaxies and the quasar Ly alpha forest, and a radial velocity search for planets around ~8000 stars. This paper describes the first data release of SDSS-III (and the eighth counting from the beginning of the SDSS). The release includes five-band imaging of roughly 5200 deg^2 in the Southern Galactic Cap, bringing the total footprint of the SDSS imaging to 14,555 deg^2, or over a third of the Celestial Sphere. All the imaging data have been reprocessed with an improved sky-subtraction algorithm and a final, self-consistent photometric recalibration and flat-field determination. This release also includes all data from the second phase of the Sloan Extension for Galactic Understanding and Evolution (SEGUE-2), consisting of spectroscopy of approximately 118,000 stars at both high and low Galactic latitudes. All the more than half a million stellar spectra obtained with the SDSS spectrograph have been reprocessed through an improved stellar parameters pipeline, which has better determination of metallicity for high metallicity stars.

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Results from the Supernova Photometric Classification Challenge

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo-z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo-z for each SN, and 9 entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo-z estimators, we have released updated simulations with improvements based on our experience from the SNPCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer keys so that developers can evaluate their own analysis.

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Single or Double Degenerate Progenitors? Searching for Shock Emission in the SDSS-II Type Ia Supernovae

From the set of nearly 500 spectroscopically confirmed type~Ia supernovae and around 10,000 unconfirmed candidates from SDSS-II, we select a subset of 108 confirmed SNe Ia with well-observed early-time light curves to search for signatures from shock interaction of the supernova with a companion star. No evidence for shock emission is seen; however, the cadence and photometric noise could hide a weak shock signal. We simulate shocked light curves using SN Ia templates and a simple, Gaussian shock model to emulate the noise properties of the SDSS-II sample and estimate the detectability of the shock interaction signal as a function of shock amplitude, shock width, and shock fraction. We find no direct evidence for shock interaction in the rest-frame $B$-band, but place an upper limit on the shock amplitude at 9% of supernova peak flux ($M_B > -16.6$ mag). If the single degenerate channel dominates type~Ia progenitors, this result constrains the companion stars to be less than about 6 $M_{\odot}$ on the main sequence, and strongly disfavors red giant companions.

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Statistical Classification Techniques for Photometric Supernova Typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on lightcurves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20,000 simulated Dark Energy Survey lightcurves. We demonstrate that these methods are comparable to the best template fitting methods currently used, and in particular do not require the redshift of the host galaxy or candidate. However both methods require a training sample that is representative of the full population, so typical spectroscopic supernova subsamples will lead to poor performance. To enable the full potential of such blind methods, we recommend that representative training samples should be used and so specific attention should be given to their creation in the design phase of future photometric surveys.

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The Effect of Host Galaxies on Type Ia Supernovae in the SDSS-II Supernova Survey

We present an analysis of the host galaxy dependencies of Type Ia Supernovae (SNe Ia) from the full three year sample of the SDSS-II Supernova Survey. We rediscover, to high significance, the strong correlation between host galaxy typeand the width of the observed SN light curve, i.e., fainter, quickly declining SNe Ia favor passive host galaxies, while brighter, slowly declining Ia's favor star-forming galaxies. We also find evidence (at between 2 to 3 sigma) that SNe Ia are ~0.1 magnitudes brighter in passive host galaxies, than in star-forming hosts, after the SN Ia light curves have been standardized using the light curve shape and color variations: This difference in brightness is present in both the SALT2 and MCLS2k2 light curve fitting methodologies. We see evidence for differences in the SN Ia color relationship between passive and star-forming host galaxies, e.g., for the MLCS2k2 technique, we see that SNe Ia in passive hosts favor a dust law of R_V ~1, while SNe Ia in star-forming hosts require R_V ~2. The significance of these trends depends on the range of SN colors considered. We demonstrate that these effects can be parameterized using the stellar mass of the host galaxy (with a confidence of >4 sigma) and including this extra parameter provides a better statistical fit to our data. Our results suggest that future cosmological analyses of SN Ia samples should include host galaxy information.

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Measurements of the Rate of Type Ia Supernovae at Redshift z < ~0.3 from the SDSS-II Supernova Survey

We present a measurement of the volumetric Type Ia supernova (SN Ia) rate based on data from the Sloan Digital Sky Survey II (SDSS-II) Supernova Survey. The adopted sample of supernovae (SNe) includes 516 SNe Ia at redshift z \lesssim 0.3, of which 270 (52%) are spectroscopically identified as SNe Ia. The remaining 246 SNe Ia were identified through their light curves; 113 of these objects have spectroscopic redshifts from spectra of their host galaxy, and 133 have photometric redshifts estimated from the SN light curves. Based on consideration of 87 spectroscopically confirmed non-Ia SNe discovered by the SDSS-II SN Survey, we estimate that 2.04+1.61-0.95 % of the photometric SNe Ia may be misidentified. The sample of SNe Ia used in this measurement represents an order of magnitude increase in the statistics for SN Ia rate measurements in the redshift range covered by the SDSS-II Supernova Survey. If we assume a SN Ia rate that is constant at low redshift (z < 0.15), then the SN observations can be used to infer a value of the SN rate of rV = (2.69+0.34+0.21-0.30-0.01) x10^{-5} SNe yr^{-1} Mpc-3 (H0 /(70 km s^{-1} Mpc^{-1}))^{3} at a mean redshift of ~ 0.12, based on 79 SNe Ia of which 72 are spectroscopically confirmed. However, the large sample of SNe Ia included in this study allows us to place constraints on the redshift dependence of the SN Ia rate based on the SDSS-II Supernova Survey data alone. Fitting a power-law model of the SN rate evolution, r_V(z) = A_p x ((1 + z)/(1 + z0))^ν, over the redshift range 0.0 < z < 0.3 with z0 = 0.21, results in A_p = (3.43+0.15-0.15) x 10^{-5} SNe yr^{-1} Mpc-3 (H0 /(70 km s^{-1} Mpc^{-1}))^{3} and ν= 2.04+0.90-0.89.

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