Related papers: Astrometric Binary Classification Via Artificial N…
One of the most common and universal problems in science is to investigate a function. The prediction can be made by an Artificial Neural Network (ANN) or a mathematical model. Both approaches have their advantages and disadvantages.…
Most binaries are undetected. Astrometric reductions of a system using the assumption that the object moves like a single point mass can be biased by unresolved binary stars. The discrepancy between the centre of mass of the system (which…
We present a machine learning model to classify Active Galactic Nuclei (AGN) and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test…
Supervised classification is the most active and emerging research trends in today's scenario. In this view, Artificial Neural Network (ANN) techniques have been widely employed and growing interest to the researchers day by day. ANN…
Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degradation due to the lack of freedom as activations and weights…
Modern astronomy relies on massive databases collected by robotic telescopes and digital sky surveys, acquiring data in a much faster pace than what manual analysis can support. Among other data, these sky surveys collect information about…
Classical Cepheids (CCs) and RR Lyrae stars (RRLs) are important classes of variable stars used as standard candles to estimate galactic and extragalactic distances. Their multiplicity is imperfectly known, particularly for RRLs.…
The direct detection of binary systems in wide-field surveys is limited by the size of the stars' point-spread-functions (PSFs). A search for elongated objects can find closer companions, but is limited by the precision to which the PSF…
Kiloparsec-scale binary active galactic nuclei (AGNs) signal active supermassive black hole (SMBH) pairs in merging galaxies. Despite their significance, unambiguously confirmed cases remain scarce and most have been discovered…
Convolutional Neural Networks (CNN) have become de fact state-of-the-art for the main computer vision tasks. However, due to the complex underlying structure their decisions are hard to understand which limits their use in some context of…
A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to…
In modern astronomy, the quantity of data collected has vastly exceeded the capacity for manual analysis, necessitating the use of advanced artificial intelligence (AI) techniques to assist scientists with the most labor-intensive tasks. AI…
We present a catalog of 99,203 wide binary systems, initially identified as common proper motion (CPM) pairs from a subset of ~5.2 million stars with proper motions $\mu > 40$ mas/year, selected from Gaia data release 2 (DR2) and the…
Astrometry from Gaia DR3 has produced a sample of $\sim$170,000 Keplerian orbital solutions, with many more anticipated in the next few years. These data have enormous potential to constrain the population of binary stars, giant planets,…
Machine Learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to…
Eclipsing binaries provide one of the most direct mechanisms for measuring stellar properties such as mass and radius, but historically, determining these properties has been non-trivial and computationally prohibitive. As such, only a…
We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early…
We present results of using a basic binary classification neural network model to identify likely catastrophic outlier photometric redshift estimates of individual galaxies, based only on the galaxies' measured photometric band magnitude…
Binary and dual active galactic nuclei (AGN) are an important observational tool for studying the formation and dynamical evolution of galaxies and supermassive black holes (SMBHs). An entirely new method for identifying possible AGN pairs…
I analyze several catalogs of known visual and spectroscopic binaries and conclude that a large number of binaries is missing in current catalogs. Samples of the best studied (nearby and bright) stars indicate that the true binary fraction…