Related papers: An adaptive parameter estimator for poor-quality s…
Brown dwarf spectra offer vital testbeds for our understanding of the chemical and physical processes that sculpt substellar atmospheres. Recently, atmospheric retrieval approaches have been applied to a number of low-resolution (R~100)…
The optimization of over-parameterized deep neural networks represents a large-scale, high-dimensional, and strongly non-convex decision problem that challenges existing optimization frameworks. Current evolutionary and gradient-based…
The paradigm of automated waste classification has recently seen a shift in the domain of interest from conventional image processing techniques to powerful computer vision algorithms known as convolutional neural networks (CNN).…
White dwarfs represent the most common end stage of stellar evolution and are important for a range of astrophysical questions. The high-resolution ultraviolet spectroscopic capability of the Habitable World Observatory (HWO) offers a…
Aims. We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in…
Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
Convolutional Neural Networks (CNNs) are a class of artificial neural networks whose computational blocks use convolution, together with other linear and non-linear operations, to perform classification or regression. This paper explores…
Brown dwarfs are compact objects that do not reach temperatures high enough to produce sustained hydrogen fusion. Consequently, they cool over time, gradually evolving through later spectral types. In fact, three new spectral types (L, T,…
Precise and accurate parameters for late-type (late K and M) dwarf stars are important for characterization of any orbiting planets, but such determinations have been hampered by these stars' complex spectra and dissimilarity to the Sun. We…
The third Gaia Data Release has provided the astronomical community with astrometric data of more than 1.8 billion sources, and low resolution spectra for 220 million. Such a large amount of data is difficult to handle by means of visual…
Accurately measuring the masses of white dwarf stars is crucial in many astrophysical contexts (e.g., asteroseismology and cosmochronology). These masses are most commonly determined by fitting a model atmosphere to an observed spectrum;…
Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators. Parametrized neural network reweighting can be used to extend this fitting procedure to many dimensions and…
We present a neural net algorithm for parameter estimation in the context of large cosmological data sets. Cosmological data sets present a particular challenge to pattern-recognition algorithms since the input patterns (galaxy redshift…
The quality of microscopy images often suffers from optical aberrations. These aberrations and their associated point spread functions have to be quantitatively estimated to restore aberrated images. The recent state-of-the-art method…
A novel approach is suggested for improving the accuracy of fault detection in distribution networks. This technique combines adaptive probability learning and waveform decomposition to optimize the similarity of features. Its objective is…
It is possible to reliably identify white dwarfs (WDs) without recourse to spectra, instead using photometric and astrometric measurements to distinguish them from Main Sequence stars and quasars. WDs' colours can also be used to infer…
We consider linear inverse problems under white noise. These types of problems can be tackled with, e.g., iterative regularisation methods and the main challenge is to determine a suitable stopping index for the iteration. Convergence…
This paper aims to build an estimate of an unknown density of the data with measurement error as a linear combination of functions from a dictionary. Inspired by the penalization approach, we propose the weighted Elastic-net penalized…
In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We present a surprising finding that, when recovering the…