Related papers: SimSIMS: Simulation-based Supernova Ia Model Selec…

Technological advances in genotyping have given rise to hypothesis-based association studies of increasing scope. As a result, the scientific hypotheses addressed by these studies have become more complex and more difficult to address using…

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…

We discuss the extent to which photometric measurements alone can be used to identify Type Ia supernovae (SNIa) and to determine redshift and other parameters of interest for cosmological studies. We fit the light curve data of the type…

Type Ia supernovae (SNe Ia) are currently the best probes of the dark energy in the universe. To constrain the nature of dark energy in a model-independent manner, we allow the density of dark energy, $\rho_X(z)$, to be an arbitrary…

Type Ia supernovae (SNe Ia) are one of the major tools to determine the cosmological parameters. Utilizing them as distance indicators, it is possible to geometrically survey the universe. To this end, the intrinsic scatter in the…

We present a fully consistent catalog of local and global properties of host galaxies of 882 Type Ia supernov\ae\ (SNIa) that were selected based on their light-curve properties, spanning the redshift range $0.01 < z < 1.\text{}$ This…

Simulation-Grounded Neural Networks (SGNNs) are predictive models trained entirely on synthetic data from mechanistic simulations. They have achieved state-of-the-art performance in domains where real-world labels are limited or unobserved,…

Type Ia supernovae (SNe Ia) are standardizable candles that must be modeled empirically to yield cosmological constraints. To understand the robustness of this modeling to variations in the model training procedure, we build an end-to-end…

We consider the problem of characterisation of burst sources detected with the Laser Interferometer Space Antenna (LISA) using the multi-modal nested sampling algorithm, MultiNest. We use MultiNest as a tool to search for modelled bursts…

Type Ia supernovae (SNe Ia) were instrumental in establishing the acceleration of the universe's expansion. By virtue of their combination of distance reach, precision, and prevalence, they continue to provide key cosmological constraints,…

In the last few years, there has been significant progress in the development of machine learning methods tailored to astrophysics and cosmology. We have recently applied one of these, namely, the neural network bundle method, to the…

We use the new SNe Ia discovered by the SDSS-II Supernova Survey together with additional supernova datasets as well as observations of the cosmic microwave background and baryon acoustic oscillations to constrain cosmological models. This…

We report constraints on a variety of non-standard cosmological models using the full 5-year photometrically-classified type Ia supernova sample from the Dark Energy Survey (DES-SN5YR). Both Akaike Information Criterion (AIC) and…

How do we compare between hypotheses that are entirely consistent with observations? The marginal likelihood (aka Bayesian evidence), which represents the probability of generating our observations from a prior, provides a distinctive…

We present detailed investigation of a specific $i$-band light-curve feature in Type Ia supernovae (SNe Ia) using the rapid cadence and high signal-to-noise ratio light-curves obtained by the Carnegie Supernova Project. The feature is…

We study the relation of Type Ia Supernovae (SNe Ia) with host galaxies from a large low redshift sample. By examining the Hubble residuals of the entire sample from the best-fit cosmology, we show that SNe Ia in passive hosts are brighter…

Noting the erroneous proclivity of information-theoretic approaches, like the Akaike information criterion (AIC), to select simpler models while performing model selection with a small sample size, we address the problem of new physics…

We present volumetric rates and luminosity functions (LFs) of Type Ia supernovae (SNe Ia) from the All-Sky Automated Survey for Supernovae (ASAS-SN), covering the 11-year period from 2014 to 2024. By combining the 2014--2017 $V$-band sample…

Detecting symmetry from data is a fundamental problem in signal analysis, providing insight into underlying structure and constraints. When data emerge as trajectories of dynamical systems, symmetries encode structural properties of the…

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…