Related papers: A Classifier to Detect Elusive Astronomical Object…
AIMS: Using Wide-field Infrared Survey Explorer (WISE) data and previous optical and near-infrared sky surveys, we try to identify still missing stellar and substellar neighbours of the Sun. METHODS: When checking the brightest red WISE…
We present ORACLE, the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent neural network with Gated Recurrent Units (GRUs), and has…
Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to developed an optimized version of an Artificial Neural Network…
We present results from a Hubble Space Telescope imaging search for low-mass binary and planetary companions to 33 nearby brown dwarfs with spectral types of T8-Y1. Our survey provides new photometric information for these faint systems,…
The detection of low surface brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic…
Based on the spatial distribution of objects in the solar neighbourhood with a radius of 20 parsecs, and after correcting for the incompleteness of observational data, an expression was obtained for estimating the probability of finding an…
Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts. The classifier uses the…
We address the estimation of the 6D pose of an unknown target spacecraft relative to a monocular camera, a key step towards the autonomous rendezvous and proximity operations required by future Active Debris Removal missions. We present a…
In this work we present a system for the automatic classification of the light curves of eclipsing binaries. This system is based on a classification scheme that aims to separate eclipsing binary sistems according to their geometrical…
Machine-learning algorithms offer immense possibilities in the development of several cognitive applications. In fact, large scale machine-learning classifiers now represent the state-of-the-art in a wide range of object…
Recent monitoring programs of ultra cool field M and L dwarfs (low mass stars or brown dwarfs) have uncovered low amplitude photometric I-band variations which may be associated with an inhomogeneous distribution of photospheric…
We present initial results of our effort to create a statistically robust, volume-limited sample of ultracool dwarfs from the 2MASS Second Incremental Data Release. We are engaged in a multifaceted search for nearby late-type dwarfs and…
Despite the advances provided by large-scale photometric surveys, stellar features - such as metallicity - generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a…
We present an application of computer vision methods to classify the light curves of eclipsing binaries (EB). We have used pre-trained models based on convolutional neural networks ($\textit{ResNet50}$) and vision transformers…
Employing a new machine learning method, named hierarchical extreme learning machine (HELM) algorithm, we identified 56 hot subdwarf stars in the first data release (DR1) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope…
The multiplicity properties of brown dwarfs are critical empirical constraints for formation theories, while multiples themselves provide unique opportunities to test evolutionary and atmospheric models and examine empirical trends. Studies…
Known for their efficiency in analyzing large data sets, machine learning classifiers are widely used in wide-field sky surveys. The upcoming Vera C. Rubin Observatory Legacy of Time and Space Survey (LSST) will generate millions of alerts…
In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural…
Brown dwarfs form the key, yet poorly understood, link between stellar and planetary astrophysics. These objects offer unique tests of Galactic structure, but observational limitations have inhibited their large-scale analysis to date.…
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…