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With the advent of large dedicated Type Ia supernova (SN Ia) surveys, K-corrections of SNe Ia and their uncertainties have become especially important in the determination of cosmological parameters. While K-corrections are largely driven…

We study supernova (SN) classification using the machine learning method of the Recurrent Neural Network (RNN) in the Chinese Space Station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey, and explore the improvement of the…

Recent analyses suggest that distance residuals measured from Type Ia supernovae (SNe Ia) are correlated with local host galaxy properties within a few kpc of the SN explosion. However, the well-established correlation with global host…

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…

Given data y(n) and p(n)covariates x(n) one problem in linear regression is to decide which if any of the covariates to include. There are many articles on this problem but all are based on a stochastic model for the data. This paper gives…

We use a sample of 1338 spectroscopically confirmed and photometrically classified Type Ia Supernovae (SNe Ia), sourced from the CSP, CfA, SDSS-II, and SNLS supernova samples, to examine the relationships between SNe Ia and the galaxies…

The case of SN Ia Hubble diagrams from photometrically selected samples using photometric SN host galaxy redshifts is investigated. The host redshift uncertainties and the contamination by core collapse SNe are addressed. As a test, we use…

The peculiar motion of the host galaxies introduces bias in estimating cosmological parameters from supernova data. The coherent component of the peculiar motion is usually corrected for using velocity field reconstruction based on the…

In this paper we study the asymptotics of linear regression in settings with non-Gaussian covariates where the covariates exhibit a linear dependency structure, departing from the standard assumption of independence. We model the covariates…

We use three years of data from the Supernova Legacy Survey (SNLS) to study the general properties of core-collapse and type Ia supernovae. This is the first such study using the "rolling search" technique which guarantees well-sampled SNLS…

Using type Ia supernovae as cosmological probes requires empirical corrections that are correlated with their host environment. Here we present a unified Bayesian hierarchical model designed to infer, from purely photometric observations,…

In nonparametric regression analysis, errors are possibly correlated in practice, and neglecting error correlation can undermine most bandwidth selection methods. When no prior knowledge or parametric form of the correlation structure is…

Motivated by the simultaneous association analysis with the presence of latent confounders, this paper studies the large-scale hypothesis testing problem for the high-dimensional confounded linear models with both non-asymptotic and…

We use a new technique to extract the spectrum of a supernova from that of the contaminating background of its host galaxy, and apply it to the specific case of high-redshift Type Ia supernova (SN Ia) spectroscopy. The algorithm is based on…

Individual-specific, time-constant, random effects are often used to model dependence and/or to account for omitted covariates in regression models for longitudinal responses. Longitudinal studies have known a huge and widespread use in the…

Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…

When scholars study joint distributions of multiple variables, copulas are useful. However, if the variables are not linearly correlated with each other yet are still not independent, most of conventional copulas are not up to the task.…

Nonlinear regression has been extensively employed in many computer vision problems (e.g., crowd counting, age estimation, affective computing). Under the umbrella of deep learning, two common solutions exist i) transforming nonlinear…

We study the properties of Type Ia Supernovae (SNe Ia) as functions of the radial distance from their host galaxy centers. Using a sample of 62 SNe Ia with reliable luminosity, reddening, and decline rate determinations, we find no…

We report a measurement of the supernova (SN) rates (Ia and core-collapse) in galaxy clusters based on the 136 SNe of the sample described in Cappellaro et al. (1999) and Mannucci et al. (2005). Early-type cluster galaxies show a type Ia SN…