相关论文: Artificial Neural Network for Constructing Type Ia…

In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communication signals from subsampled data. Recent work considered a…

Key information about the progenitor system and the explosion mechanism of Type Ia supernovae (SNe~Ia) can be obtained from early observations, within a few days from explosion. iPTF16abc was discovered as a young SN~Ia with excellent early…

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…

Type Ia supernovae (SNe), thermonuclear explosions of white dwarfs in binary systems, are widely used as standard candles owing to the empirical width-luminosity relation of their light curves. Recent theoretical and observational studies…

Manual fits to spectral times series of Type Ia supernovae have provided a method of reconstructing the explosion from a parametric model but due to lack of information about model uncertainties or parameter degeneracies direct comparison…

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…

For image classification problems, various neural network models are commonly used due to their success in yielding high accuracies. Convolutional Neural Network (CNN) is one of the most frequently used deep learning methods for image…

Supernovae Ia (SNe) can provide a unique window on the large scale structure (LSS) of the Universe at redshifts where few other observations are available, by solving the inversion problem (IP) consisting in reconstructing the LSS from its…

The backpropagation algorithm, despite its widespread use in neural network learning, may not accurately emulate the human cortex's learning process. Alternative strategies, such as the Forward-Forward Algorithm (FFA), offer a closer match…

Type Ia Supernovae (SNe Ia) have become the most precise distance indicators in astrophysics due to their incredible observational homogeneity. Increasing discovery rates, however, have revealed multiple sub-populations with spectroscopic…

Artificial Intelligence algorithms have been steadily increasing in popularity and usage. Deep Learning, allows neural networks to be trained using huge datasets and also removes the need for human extracted features, as it automates the…

This work uses a combination of a variational auto-encoder and generative adversarial network to compare different dark energy models in light of observations, e.g., the distance modulus from type Ia supernovae. The network finds an…

The rate evolution of subluminous Type Ia Supernovae is presented using data from the Supernova Legacy Survey. This sub-sample represents the faint and rapidly-declining light-curves of the observed supernova Ia (SN Ia) population here…

We demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral…

After the tremendous development of neural networks trained by backpropagation, it is a good time to develop other algorithms for training neural networks to gain more insights into networks. In this paper, we propose a new algorithm for…

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the…

We introduce genetic algorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state. Specifically, we will give a brief introduction to the genetic…

Supernovae Ia (SNe Ia) light curves have been used to prove the universe is expanding. As standard candles, SNe Ia appear to indicate the rate of expansion has increased in the past and is now decreasing. This independent evaluation of SNe…

This paper presents an implementation of multilayer feed forward neural networks (NN) to optimize CMOS analog circuits. For modeling and design recently neural network computational modules have got acceptance as an unorthodox and useful…

The back-propagation algorithm has long been the de-facto standard in optimizing weights and biases in neural networks, particularly in cutting-edge deep learning models. Its widespread adoption in fields like natural language processing,…