arXiv subjects

Bruce Bassett

Publications and source records attributed to Bruce Bassett.

At least 19 recordsLinked to original sources

arXiv2026

Supernovae Unite: Combining Pantheon+ and DES-SN5YR

We present the Hubble diagram and cosmological constraints resulting from the combination of the Pantheon+ and DES-SN5YR supernova (SN) samples, which we refer to as Unite. Unite updates Pantheon+, including methodology improvements that the DES sample enabled, and adds the new SN from the DES survey. This represents the most comprehensive and internally consistent SN Ia dataset available, consisting of 2884 likely type Ia supernovae. This work features consistent SN Ia modelling, sample selection, and bias corrections. We also redetermine host-galaxy stellar masses for over 98% of the sample using a consistent framework. Using Unite alone, for a flat universe with a cosmological constant (Flat-$\Lambda$CDM), we find $\Omega_{\rm m} = 0.310^{+0.012}_{-0.011}$. We further present constraints using the Unite sample combined with Cosmic Microwave Background and both DES and DESI Baryon Acoustic Oscillation data. We find strong evidence of tension between CMB measurements and the combined BAO and supernova datasets when fitting a model with constant dark energy equation of state ($w$), which is alleviated when allowing $w$ to vary with time. For a flat universe with dark energy that has a time-evolving equation of state parametrized by $w_0$ and $w_a$ we find $(\Omega_{\rm m},w_0,w_a) = (0.305\pm0.004,-0.861^{+0.044}_{-0.042},-0.60^{+0.17}_{-0.19})$ with a dark energy Figure of Merit of 315. This corresponds to a $\sim30\%$ reduction in the area of the $w_0-w_a$ confidence region relative to previous constraints from the combination of DES Y6, DESI-DR2, and CMB data (arXiv:2605.27221). The Bayesian evidence indicates only weak preference for time evolving dark energy, whereas frequentist analyses yield a preference over Flat-$\Lambda$CDM at a significance of $3.3\sigma$ ($3.1\sigma$) when using the maximum $\textit{a posteriori}$ probability (maximum likelihood).

astro-ph.CO↗

arXiv2026

Inferring Affective Consciousness in an Artificial Agent: A Case Study

Creatures that display 'hedonic place preference behaviour' are thought by many scientists to experience feelings, on the assumption that their attraction to pleasure-producing substances which lack nutritional value (e.g. cocaine, morphine) cannot easily be attributed to unconscious instinctual behaviour. In this paper, we discuss how a simple artificial agent that instantiates attributes of an affective system engaging in felt uncertainty about its intrinsic needs in relation to environmental resources can similarly display hedonic place preference behaviour -- through an apparently subjective form of information processing -- while simultaneously being entirely deter-ministic. We outline some implications of this artificially engineered behaviour for our understanding of the physical basis of consciousness and the experience of free will.

cs.AI↗

arXiv2023

Sophisticated Learning: A novel algorithm for active learning during model-based planning

We introduce Sophisticated Learning (SL), a planning-to-learn algorithm that embeds active parameter learning inside the Sophisticated Inference (SI) tree-search framework of Active Inference. Unlike SI -- which optimizes beliefs about hidden states -- SL also updates beliefs about model parameters within each simulated branch, enabling counterfactual reasoning about how future observations would improve subsequent planning. We compared SL with Bayes-adaptive Reinforcement Learning (BARL) agents as well as with its parent algorithm, SI. Using a biologically inspired seasonal foraging task in which resources shift probabilistically over a 10x10 grid, we designed experiments that forced agents to balance probabilistic reward harvesting against information gathering. In early trials, where rapid learning is vital, SL agents survive, on average, 8.2% longer than SI and 35% longer than Bayes-adaptive Reinforcement Learning. While both SL and SI showed equal convergence performance, SL reached this convergence 40% faster than SI. Additionally, SL showed robust out-performance of other algorithms in altered environment configurations. Our results show that incorporating active learning into multi-step planning materially improves decision making under radical uncertainty, and reinforces the broader utility of Active Inference for modeling biologically relevant behavior.

cs.AI↗

arXiv2022

Nature and Evolution of UHF and L-band Radio Frequency Interference at the MeerKAT Radio Telescope

Radio Frequency Interference (RFI) is unwanted noise that swamps the desired astronomical signal. Radio astronomers have always had to deal with RFI detection and excision around telescope sites, but little has been done to understand the full scope, nature and evolution of RFI in a unified way. We undertake this for the MeerKAT array using a probabilistic multidimensional framework approach focussing on UHF-band and L-band data. In the UHF- band, RFI is dominated by the allocated Global System for Mobile (GSM) Communications, flight Distance Measuring Equipment (DME), and UHF-TV bands. The L-band suffers from known RFI sources such as DMEs, GSM, and the Global Positioning System (GPS) satellites. In the "clean" MeerKAT band, we noticed the RFI occupancy changing with time and direction for both the L-band and UHF band. For example, we saw a significant increase (300% increase) in the fraction of L-band flagged data in November 2018 compared to June 2018. This increase seems to correlate with construction activity on site. In the UHF-band, we found that the early morning is least impacted by RFI and other outliers. We also found a dramatic decrease in DME RFI during the hard lockdown due to the COVID-19 pandemic. The work presented here allows us to characterise the evolution of RFI at the MeerKAT site. Any observatory can adopt it to understand the behaviour of RFI within its surroundings.

astro-ph.IM↗

arXiv2014

The Data Release of the Sloan Digital Sky Survey-II Supernova Survey

This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg2 area along the celestial equator. This data release is comprised of all transient sources brighter than r~22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star-formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1443 SN Ia with spectroscopic redshifts as well as photometric redshifts for a further 677 purely-photometric SN Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN Ia sample and assuming a flat Lambda-CDM cosmology, we determine Omega_M = 0.315 +/- 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 sigmas.

astro-ph.CO↗

arXiv2013

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.

astro-ph.CO↗

arXiv2013

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.

astro-ph.CO↗

arXiv2012

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.

astro-ph.CO↗

arXiv2011

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.

astro-ph.CO↗

arXiv2011

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.

astro-ph.CO↗

arXiv2011

The effect of peculiar velocities on supernova cosmology

We present an analysis of peculiar velocities and their effect on supernova cosmology. In particular, we study (a) the corrections due to our own motion, (b) the effects of correlations in peculiar velocities induced by large-scale structure, and (c) uncertainties arising from a possible local under- or over-density. For all of these effects we present a case study of their impact on the cosmology derived by the Sloan Digital Sky Survey-II Supernova Survey (SDSS-II SN Survey). Correcting supernova redshifts for the CMB dipole slightly over-corrects nearby supernovae that share some of our local motion. We show that while neglecting the CMB dipole would cause a shift in the derived equation of state of Delta w ~ 0.04 (at fixed matter density) the additional local-motion correction is currently negligible (Delta w<0.01). We use a covariance-matrix approach to statistically account for correlated peculiar velocities. This down-weights nearby supernovae and effectively acts as a graduated version of the usual sharp low-redshift cut. Neglecting coherent velocities in the current sample causes a systematic shift of ~2% in the preferred value of w and will therefore have to be considered carefully when future surveys aim for percent-level accuracy. Finally, we perform n-body simulations to estimate the likely magnitude of any local density fluctuation (monopole) and estimate the impact as a function of the low-redshift cutoff. We see that for this aspect the low-z cutoff of z=0.02 is well-justified theoretically, but that living in a putative local density fluctuation leaves an indelible imprint on the magnitude-redshift relation.

astro-ph.CO↗

arXiv2011

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.

astro-ph.CO↗

arXiv2011

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.

astro-ph.CO↗

arXiv2010

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.

astro-ph.CO↗

arXiv2010

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.

astro-ph.CO↗

arXiv2010

The Subluminous Supernova 2007qd: A Missing Link in a Family of Low-Luminosity Type Ia Supernovae

We present multi-band photometry and multi-epoch spectroscopy of the peculiar Type Ia supernova (SN Ia) 2007qd, discovered by the SDSS-II Supernova Survey. It possesses physical properties intermediate to those of the peculiar SN 2002cx and the extremely low-luminosity SN 2008ha. Optical photometry indicates that it had an extraordinarily fast rise time of <= 10 days and a peak absolute B magnitude of -15.4 +/- 0.2 at most, making it one of the most subluminous SN Ia ever observed. Follow-up spectroscopy of SN 2007qd near maximum brightness unambiguously shows the presence of intermediate-mass elements which are likely caused by carbon/oxygen nuclear burning. Near maximum brightness, SN 2007qd had a photospheric velocity of only 2800 km/s, similar to that of SN 2008ha but about 4000 and 7000 km/s less than that of SN 2002cx and normal SN Ia, respectively. We show that the peak luminosities of SN 2002cx-like objects are highly correlated with both their light-curve stretch and photospheric velocities. Its strong apparent connection to other SN 2002cx-like events suggests that SN 2007qd is also a pure deflagration of a white dwarf, although other mechanisms cannot be ruled out. It may be a critical link between SN 2008ha and the other members of the SN 2002cx-like class of objects.

astro-ph.CO↗

arXiv2010

Photometric Estimates of Redshifts and Distance Moduli for Type Ia Supernovae

Large planned photometric surveys will discover hundreds of thousands of supernovae (SNe), outstripping the resources available for spectroscopic follow-up and necessitating the development of purely photometric methods to exploit these events for cosmological study. We present a light-curve fitting technique for SN Ia photometric redshift (photo-z) estimation in which the redshift is determined simultaneously with the other fit parameters. We implement this "LCFIT+Z" technique within the frameworks of the MLCS2k2 and SALT-II light-curve fit methods and determine the precision on the redshift and distance modulus. This method is applied to a spectroscopically confirmed sample of 296 SNe Ia from the SDSS-II Supernova Survey and 37 publicly available SNe Ia from the Supernova Legacy Survey (SNLS). We have also applied the method to a large suite of realistic simulated light curves for existing and planned surveys, including SDSS, SNLS, and LSST. When intrinsic SN color fluctuations are included, the photo-z precision for the simulation is consistent with that in the data. Finally, we compare the LCFIT+Z photo-z precision with previous results using color-based SN photo-z estimates.

astro-ph.CO↗

arXiv2010

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.

astro-ph.CO↗