Related papers: Artificial Neural Network for Constructing Type Ia…
Most deep learning models are limited to specific datasets or tasks because of network structures using fixed layers. In this paper, we discuss the differences between existing neural networks and real human neurons, propose association…
Single Degenerate model is the most widely accepted progenitor model of type Ia supernovae (SNe Ia), in which a carbon-oxygen white dwarf (CO WD) accretes hydrogen-rich material from a main sequence or a slightly evolved star (WD +MS) to…
Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for…
New generation large-aperture telescopes, multi-object spectrographs, and large format detectors are making it possible to acquire very large samples of stellar spectra rapidly. In this context, traditional star-by-star spectroscopic…
Near-infrared spectra of the subluminous Type Ia SN1999by are presented which cover the time evolution from about 4 days before to 2 weeks after maximum. Analysis of these data was accomplished through the construction of an extended set of…
The influence of the initial composition and structure of the exploding white dwarf on the nucleosynthesis and structure of Type Ia Supernovae has been studied. The progenitor structures are based on detailed stellar evolutionary tracks for…
A method that allows us to give a different treatment to any neuron inside feedforward neural networks is presented. The algorithm has been implemented with two very different learning methods: a standard Back-propagation (BP) procedure and…
We present the current status of our construction of synthetic spectra for type Ia supernovae. These properly take into account the effects of NLTE and an adequate representation of line blocking and blanketing. The models are based on a…
Aims: Spectroscopic observations of Type Ia supernovae obtained at the New Technology Telescope (NTT) and the Nordic Optical Telescope (NOT), in conjunction with the SDSS-II Supernova Survey, are analysed. We use spectral indicators…
Modeling type Ia supernova (SN Ia) explosions in three dimensions allows to eliminate any undetermined parameters and provides predictive power to simulations. This is necessary to improve the understanding of the explosion mechanism and to…
Artificial neurons with arbitrarily complex internal structure are introduced. The neurons can be described in terms of a set of internal variables, a set activation functions which describe the time evolution of these variables and a set…
Neural Networks sequentially build high-level features through their successive layers. We propose here a new neural network model where each layer is associated with a set of candidate mappings. When an input is processed, at each layer,…
The backpropagation algorithm is an invaluable tool for training artificial neural networks; however, because of a weight sharing requirement, it does not provide a plausible model of brain function. Here, in the context of a two-layer…
There are now hundreds of publicly available supernova spectral time series. Radiative transfer modeling of this data gives insights into the physical properties of these explosions such as the composition, the density structure, or the…
We present an empirical method which measures the distance to a Type Ia supernova (SN Ia) with a precision of ~ 10% from a single night's data. This method measures the supernova's age and luminosity/light-curve parameter from a spectrum,…
The time delay between the formation of a population of stars and the onset of type Ia supernovae (SNe Ia) sets important limits on the masses and nature of SN Ia progenitors. Here we use a new observational technique to measure this time…
We use physics informed neural networks (PINNs) to solve the radiative transfer equation and calculate a synthetic spectrum for a Type Ia supernova (SN~Ia) SN 2011fe. The calculation is based on local thermodynamic equilibrium (LTE) and 9…
Fast Neural Architecture Construction (NAC) is a method to construct deep network architectures by pruning and expansion of a base network. In recent years, several automated search methods for neural network architectures have been…
We present new algorithms for adaptively learning artificial neural networks. Our algorithms (AdaNet) adaptively learn both the structure of the network and its weights. They are based on a solid theoretical analysis, including…
Self-replication is a key aspect of biological life that has been largely overlooked in Artificial Intelligence systems. Here we describe how to build and train self-replicating neural networks. The network replicates itself by learning to…