Related papers: Characterization of Type Ia Supernova Light Curves…

In this Letter we present evidence for a spectral sequence among Type Ia supernovae (SNe Ia). The sequence is based on the systematic variation of several features seen in the near-maximum light spectrum. This sequence is analogous to the…

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…

We present multi-band photometry of 185 type-Ia supernovae (SN Ia), with over 11500 observations. These were acquired between 2001 and 2008 at the F. L. Whipple Observatory of the Harvard-Smithsonian Center for Astrophysics (CfA). This…

Forthcoming time-domain surveys, such as the Rubin Observatory Legacy Survey of Space and Time, will vastly increase samples of supernovae (SNe) and other optical transients, requiring new data-driven techniques to analyse their photometric…

Shifting the focus of Type Ia supernova (SN Ia) cosmology to the near-infrared (NIR) is a promising way to significantly reduce the systematic errors, as the strategy minimizes our reliance on the empirical width-luminosity relation and…

We analyze the standardizability of Type Ia supernovae (SNe Ia) in the near-infrared (NIR) by investigating the correlation between observed peak NIR absolute magnitude and post-maximum B-band decline rate. A sample of 27 low-redshift SNe…

The use of Type Ia Supernovae (SNe Ia) as cosmological tools has motivated significant effort to: understand what drives the intrinsic scatter of SN Ia distance modulus residuals after standardization, characterize the distribution of SN Ia…

Type Ia supernovae provide direct evidence for an accelerating universe, and for the existence of "dark energy" driving this expansion. The Supernova Legacy Survey (SNLS) will deliver many hundreds of SNIa detections, and well-sampled…

We investigate a new empirical fitting method for the optical light curves of Type Ia supernovae (SNe~Ia) that is able to estimate the first-light time of SNe~Ia, even when they are not discovered extremely early. With an improved ability…

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…

Type Ia Supernovae are in many aspects still enigmatic objects. Recent years have witnessed a bonanza of supernova observations. The increased samples from dedicated searches have allowed the statistical investigation of Type Ia Supernovae…

We use a sample of 58 low-redshift (z <= 0.03) Type Ia supernovae (SNe Ia) having well-sampled light curves and spectra near maximum light to examine the behaviour of high-velocity features (HVFs) in SN Ia spectra. We take advantage of the…

Functional principal component analysis (FPCA) is a widely used technique in functional data analysis for identifying the primary sources of variation in a sample of random curves. The eigenfunctions obtained from standard FPCA typically…

We study how future Type-Ia supernovae (SNIa) standard candles detected by the Vera C. Rubin Observatory (LSST) can constrain some cosmological models. We use a realistic three-year SNIa simulated dataset generated by the LSST Dark Energy…

Spectroscopic and photometric properties of low and high-z supernovae Ia (SNe Ia) have been analyzed in order to achieve a better understanding of their diversity and to identify possible SN Ia sub-types. We use wavelet transformed spectra…

Functional principal component analysis (FPCA) is an important technique for dimension reduction in functional data analysis (FDA). Classical FPCA method is based on the Karhunen-Lo\`{e}ve expansion, which assumes a linear structure of the…

The amount of $^{56}$Ni produced in type Ia supernova (SN Ia) explosion is probably the most important physical parameter underlying the observed correlation of SN Ia luminosities with their light curves. Based on an empirical relation…

We present ugriz light curves for 146 spectroscopically confirmed or spectroscopically probable Type Ia supernovae from the 2005 season of the SDSS-II Supernova survey. The light curves have been constructed using a photometric technique…

We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased…

We explore the physics of SN Ia light curves and spectra using the 1-D non-LTE time-dependent radiative-transfer code CMFGEN. Rather than adjusting ejecta properties to match observations, we select as input one "standard" 1-D…