Cosmological inference becomes increasingly difficult when complex data-generating processes cannot be modeled by simple probability distributions. With the everincreasing size of data sets in cosmology, there is increasing burden placed on adequate modeling; systematic errors in the model will dominate where previously these were swamped by statistical errors. For example, Gaussian distributions are an insufficient representation for errors in quantities like photometric redshifts. Likewise, it can be difficult to quantify analytically the distribution of errors that are introduced in complex fitting codes. Without a simple form for these distributions, it becomes difficult to accurately construct a likelihood function for the data as a function of parameters of interest. Approximate Bayesian computation (ABC) provides a means of probing the posterior distribution when direct calculation of a sufficiently accurate likelihood is intractable. ABC allows one to bypass direct calculation of the likelihood but instead relies upon the ability to simulate the forward process that generated the data. These simulations can naturally incorporate priors placed on nuisance parameters, and hence these can be marginalized in a natural way. We present and discuss ABC methods in the context of supernova cosmology using data from the SDSS-II Supernova Survey. Assuming a flat cosmology and constant dark energy equation of state we demonstrate that ABC can recover an accurate posterior distribution. Finally we show that ABC can still produce an accurate posterior distribution when we contaminate the sample with Type IIP supernovae.

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We present near infra-red light curves of supernova (SN) 2011fe in M101, including 34 epochs in H band starting fourteen days before maximum brightness in the B-band. The light curve data were obtained with the WIYN High-Resolution Infrared Camera (WHIRC). When the data are calibrated using templates of other Type Ia SNe, we derive an apparent H-band magnitude at the epoch of B-band maximum of 10.85 ± 0.04. This implies a distance modulus for M101 that ranges from 28.86 to 29.17 mag, depending on which absolute calibration for Type Ia SNe is used.

We present 13 Type Ia supernovae (SNe Ia) observed in the rest-frame near-infrared (NIR) from 0.02 < z < 0.09 with the WIYN High-resolution Infrared Camera on the WIYN 3.5 m telescope. With only one to three points per light curve and a prior on the time of maximum from the spectrum used to type the object, we measure an H-band dispersion of spectroscopically normal SNe Ia of 0.164 mag. These observations continue to demonstrate the improved standard brightness of SNe Ia in an H band, even with limited data. Our sample includes two SNe Ia at z ∼ 0.09, which represent the most distant rest-frame NIR H-band observations published to date. This modest sample of 13 NIR SNe Ia represent the pilot sample for "SweetSpot"-a 3 yr NOAO Survey program that will observe 144 SNe Ia in the smooth Hubble flow. By the end of the survey we will have measured the relative distance to a redshift of z ∼ 0.05%-1%. Nearby Type Ia supernova (SN Ia) observations such as these will test the standard nature of SNe Ia in the rest-frame NIR, allow insight into the nature of dust, and provide a critical anchor for future cosmological SN Ia surveys at higher redshift.

We apply statistically rigorous methods of nonparametric risk estimation to the problem of inferring the local peculiar velocity field from nearby supernovae (SNIa). We use two nonparametric methods -Weighted Least Squares (WLS) and Coefficient Unbiased (CU) -both of which employ spherical harmonics to model the field and use the estimated risk to determine at which multipole to truncate the series. We show that if the data are not drawn from a uniform distribution or if there is power beyond the maximum multipole in the regression, a bias is introduced on the coefficients using WLS. CU estimates the coefficients without this bias by including the sampling density making the coefficients more accurate but not necessarily modeling the velocity field more accurately. After applying nonparametric risk estimation to SNIa data, we find that there are not enough data at this time to measure power beyond the dipole. The WLS Local Group bulk flow is moving at 538 ± 86 km s −1 towards (l, b) = (258 • ± 10 • , 36 • ± 11 • ) and the CU bulk flow is moving at 446 ± 101 km s −1 towards (l, b) = (273 • ± 11 • , 46 • ± 8 • ). We find that the magnitude and direction of these measurements are in agreement with each other and previous results in the literature.