Related papers: Application of Bayesian graphs to SN Ia data analy…

Constraints on dark energy from current observational data are sensitive to how distances are measured from Type Ia supernova (SN Ia) data. We find that flux-averaging of SNe Ia can be used to test the presence of unknown systematic…

In this talk, I argue that the method of analysing the m-z data of Type Ia Supernovae (SNe Ia) by assuming exotic energy densities with strange equations of state is misleading and the reasonable remaining option is to make a…

We examine the basic physics of type Ia supernova (SNe Ia) light curves with a view toward interpreting the relations between peak luminosity, peak width, and late-time slope in terms of the properties of the underlying explosion models. We…

Type Ia Supernovae hold great promise to measure the cosmic deceleration. The diversity observed among these explosions, however, complicates their ability to measure cosmological parameters considerably. The comparison of near and distant…

We present bolometric light curves constructed from multi-wavelength photometry of Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project and the CfA Supernova Group, using near-infrared observations to provide robust constraints…

Type Ia supernovae have light curves that have widths and magnitudes that can be used for testing cosmologies and they provide one of the few direct measurements of time dilation. It is shown that the standard analysis that calibrates the…

I introduce a general, Bayesian method for modelling univariate time series data assumed to be drawn from a continuous, stochastic process. The method accommodates arbitrary temporal sampling, and takes into account measurement…

We have obtained 1087 NIR (JHKs) measurements of 21 SNe Ia using PAIRITEL, nearly doubling the number of well-sampled NIR SN Ia light curves. These data strengthen the evidence that SNe Ia are excellent standard candles in the NIR, even…

We introduce SuperNNova, an open source supernova photometric classification framework which leverages recent advances in deep neural networks. Our core algorithm is a recurrent neural network (RNN) that is trained to classify light-curves…

Type Ia supernovae (SNe Ia) have been intensively investigated due to its great homogeneity and high luminosity, which make it possible to use them as standardizable candles for the determination of cosmological parameters. In 2011, the…

In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there is an abundance of auxiliary information, which often gets…

Because of flux conservation, flux-averaging {\it justifies} the use of the distance-redshift relation for a smooth universe in the analysis of type Ia supernova (SN Ia) data. We have combined the SN Ia data from the High-$z$ SN Search and…

We examine whether a comparison between wCDM and R_h=ct using merged Type Ia SN catalogs produces results consistent with those based on a single homogeneous sample. Using the Betoule et al. (2014) joint lightcurve analysis (JLA) of a…

The Pan-STARRS (PS1) Medium Deep Survey discovered over 5,000 likely supernovae (SNe) but obtained spectral classifications for just 10% of its SN candidates. We measured spectroscopic host galaxy redshifts for 3,147 of these likely SNe and…

Redshift measurements, primarily obtained from host galaxies, are essential for inferring cosmological parameters from type Ia supernovae (SNe Ia). Matching SNe to host galaxies using images is non-trivial, resulting in a subset of SNe with…

The Foundation Supernova Survey aims to provide a large, high-fidelity, homogeneous, and precisely-calibrated low-redshift Type Ia supernova (SN Ia) sample for cosmology. The calibration of the current low-redshift SN sample is the largest…

In order to investigate non-uniformity in the luminosity evolution of Type Ia supernovae, we fit the lightcurves with a multi-parameter empirical model. The model provides a quantitative method of analyzing the lightcurves of Type I…

We explore the systematic uncertainties of using Type Ia supernovae (SNe Ia) as cosmological probes, using the Supernova Legacy Survey Three Year data (SNLS3). We focus on studying the possible evolution of the stretch-luminosity parameter…

Brighter Type Ia supernovae (SNe Ia) have broader, more slowly declining B-band light curves than dimmer SNe Ia. We study the physical origin of this width-luminosity relation (WLR) using detailed radiative transfer calculations of…

Machine learning has become widely used in astronomy. Gaussian Process (GP) regression in particular has been employed a number of times to fit or re-sample supernova (SN) light-curves, however by their nature typical GP models are not…