Related papers: Automatic classification of eclipsing binary stars…
Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any…
The mass discrepancy problem, observed in high-mass stars within eclipsing binaries, highlights systematic differences between dynamical and evolutionary mass estimates, challenging the accuracy of stellar evolution models. We aim to…
Epilepsy is one of the most common neurological disorders that can be diagnosed through electroencephalogram (EEG), in which the following epileptic events can be observed: pre-ictal, ictal, post-ictal, and interictal. In this paper, we…
The classification of galaxy morphologies is an important step in the investigation of theories of hierarchical structure formation. While human expert visual classification remains quite effective and accurate, it cannot keep up with the…
Context. Intermediate- to high-mass stars are the least numerous types of stars and they are less well understood than their more numerous low-mass counterparts in terms of their internal physical processes. Modelling the photometric…
Visual inspection of x-ray scattering images is a powerful technique for probing the physical structure of materials at the molecular scale. In this paper, we explore the use of deep learning to develop methods for automatically analyzing…
Semi-supervised classification based on active learning has made significant progress, but the existing methods often ignore the uncertainty estimation (or reliability) of the prediction results during the learning process, which makes it…
Particle tracking is a fundamental task in digital microscopy. Recently, machine-learning approaches have made great strides in overcoming the limitations of more classical approaches. The training of state-of-the-art machine-learning…
In various situations one is given only the predictions of multiple classifiers over a large unlabeled test data. This scenario raises the following questions: Without any labeled data and without any a-priori knowledge about the…
Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up…
Probing properties of neutron stars from photometric observations of these objects helps us answer crucial questions at the forefront of multi-messenger astronomy, such as, what is behavior of highest density matter in extreme environments…
In this study, the distributions of the double-lined detached binaries (DBs) on the planes of mass-luminosity, mass radius and mass-effective temperature have been studied. We improved the classical mass-luminosity relation based on the…
This paper reviews methods which can be used to detect binaries involving low- and intermediate-mass stars, with special emphasis on evolved systems. Besides the traditional methods involving radial-velocity or photometric monitoring, the…
Recent developments in computational power and machine learning techniques motivate their use in many different astrophysical research areas. Consequently, many machine learning models have been trained to classify exoplanet transit signals…
We present the results of our study of the eclipsing binary systems CSS J112237.1+395219, LINEAR 1286561 and LINEAR 2602707 based on new CCD $B$, $V$, $R_c$ and $I_c$ complete light curves. The ultra-short period nature of the stars…
Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover…
We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ…
The Kepler K2 mission now makes it possible to find and study a wider variety of eclipsing binary stars than has been possible to-date, particularly long-period systems with narrow eclipses. Our aim is to characterise eclipsing binary stars…
The Laser Interferometer Space Antenna (LISA) is expected to detect close white dwarf binaries (CWDBs) through their gravitational radiation. Around 3000 binaries will be spectrally resolved at frequencies > 3 mHz, and their positions on…
Binary classification is a task that involves the classification of data into one of two distinct classes. It is widely utilized in various fields. However, conventional classifiers tend to make overconfident predictions for data that…