Related papers: Artificial Neural Network for Constructing Type Ia…
Over the past years type Ia supernovae (SNe Ia) have become a major tool to determine the expansion history of the Universe, and considerable attention has been given to, both, observations and models of these events. However, until now,…
Type Ia supernovae (SNe Ia) are thermonuclear exploding stars that can be used to put constraints on the nature of our universe. One challenge with population analyses of SNe Ia is Malmquist bias, where we preferentially observe the…
We develop an approach to efficiently grow neural networks, within which parameterization and optimization strategies are designed by considering their effects on the training dynamics. Unlike existing growing methods, which follow simple…
In this paper, we propose a new theoretical approach to Explainable AI. Following the Scientific Method, this approach consists in formulating on the basis of empirical evidence, a mathematical model to explain and predict the behaviors of…
Many aspects of the explosion mechanism of Type Ia supernovae (SN Ia) still remain unclear -- causing uncertainties in the derived cosmological parameters. Realistic models of the generation and transport of radiation in the ejecta are…
Gamma-ray bursts (GRBs) detected at high redshift can be used to trace the cosmic expansion history. However, the calibration of their luminosity distances is not an easy task in comparison to Type Ia Supernovae (SNeIa). To calibrate these…
We introduce a reversible deep learning model for 13C NMR that uses a single conditional invertible neural network for both directions between molecular structures and spectra. The network is built from i-RevNet style bijective blocks, so…
We present the analysis of the first set of low-redshift Type Ia supernovae (SNe Ia) by the Carnegie Supernova Project. Well-sampled, high-precision optical (ugriBV) and near-infrared (NIR; YJHKs) light curves obtained in a well-understood…
Although various linear log-distance path loss models have been developed, advanced models are requiring to more accurately and flexibly represent the path loss for complex environments such as the urban area. This letter proposes an…
Despite their prominent role in cosmography, little is yet known about the nature of type-Ia supernovae (SNe Ia), from the identity of their progenitor systems, through the evolution of those systems up to ignition and explosion, and to the…
This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are…
In recent years, deep learning has achieved great success in many computer vision applications. Convolutional neural networks (CNNs) have lately emerged as a major approach to image classification. Most research on CNNs thus far has focused…
A study of the time sequence of optical colours of a large sample of nearby Type Ia supernovae (SNe Ia) is presented. We study the dependence of the colour time evolution with respect to the lightcurve shape, parametrized by the stretch…
It has been widely accepted that Type Ia supernovae (SNe Ia) are thermonuclear explosions of a CO white dwarf. However, the natures of the progenitor system(s) and explosion mechanism(s) are still unclarified. Thanks to the recent…
We have developed a quantitative, empirical method for estimating the age of Type Ia supernovae (SNe Ia) from a single spectral epoch. The technique examines the goodness of fit of spectral features as a function of the temporal evolution…
Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected.…
The progenitors of Type Ia supernovae (SNe Ia) are debated, particularly the evolutionary state of the binary companion that donates mass to the exploding carbon-oxygen white dwarf. In previous work, we presented hydrodynamic models and…
Neuroevolution is a powerful method of applying an evolutionary algorithm to refine the performance of artificial neural networks through natural selection; however, the fitness evaluation of these networks can be time-consuming and…
Based on detailed models for the explosions, light curves and NLTE-spectra, evolutionary effects of Type Ia Supernovae (SNe Ia) with redshift have been studied to evaluate their size on cosmological time scales,how the effects can be…
Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications. On the other hand, shallow representation learning with component analysis is associated with rich…