Related papers: O-type Stars Stellar Parameter Estimation Using Re…
Images generated by high-resolution SAR have vast areas of application as they can work better in adverse light and weather conditions. One such area of application is in the military systems. This study is an attempt to explore the…
In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating…
The difficulty in describing the equation of state (EoS) for nuclear matter at densities above the saturation density ($\rho_0$) has led to the emergence of a multitude of models based on different assumptions and techniques. These EoSs,…
Two parameters are developed to analyze the CCD images from ground-based and/or space telescopes. The first parameter, deduced from the intensity profile of the object sharp, is useful to resolve stars and hot pixels. The second parameter…
We aim to explain the paradigm of a learning model, as well as to validate it in an applied case of an astronomy problem where the data used are declination, parallax, radial velocity of a star, as well as its annual variation in right…
Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods…
This paper describes an inverse analysis method using neural networks on optical spectroscopy, and its application to the quantitative optical constant evaluation. The present method consists of three subprocesses. First, measurable…
We pose the question of how much information on the atmospheric parameters of late-type stars can be retrieved purely from colors using standard photometric systems. We carried out numerical experiments using stellar fluxes from model…
Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk…
We devised a straightforward procedure to derive the atmosphere fundamental parameters of stars across the different MK spectral types by comparing mid-resolution spectroscopic observations with theoretical grids of synthetic spectra.The…
In the last years, automatic classification of variable stars has received substantial attention. Using machine learning techniques for this task has proven to be quite useful. Typically, machine learning classifiers used for this task…
Purpose: To develop a deep learning approach to digitally-stain optical coherence tomography (OCT) images of the optic nerve head (ONH). Methods: A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for 1…
There is a great need for accurate and autonomous spectral classification methods in astrophysics. This thesis is about training a convolutional neural network (ConvNet) to recognize an object class (quasar, star or galaxy) from…
Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The…
This article investigates the problem of estimating stellar atmospheric parameters from spectra. Feature extraction is a key procedure in estimating stellar parameters automatically. We propose a scheme for spectral feature extraction and…
With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of…
We present the first installment of a massive spectroscopic survey of Galactic O stars, based on new, high signal-to-noise ratio, R~2500 digital observations from both hemispheres selected from the Galactic O-Star Catalog of Ma\'iz…
In direct imaging at high contrast, the bright glare produced by the host star makes the detection and the characterization of sub-stellar companions particularly challenging. In spite of the use of an extreme adaptive optics system…
A method for in-service OSNR measurement with a coherent transceiver is presented and experimentally verified. A neural network is employed to identify and remove the nonlinear noise contribution to the estimated OSNR.
Artificial neural networks are finding increasing use in astronomy, but understanding the limitations of these models can be difficult. We utilize a statistical method, a sensitivity probe, designed to complement established methods for…