Related papers: The miniJPAS survey: star-galaxy classification us…
The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets. Recent advances in machine learning (ML) provide…
We present a study of the mid-infrared environments and association with star formation tracers of 6.7 GHz methanol masers taken from the Methanol Multi-Beam (MMB) Survey. Our ultimate goal is to establish the mass of the host star and its…
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such…
Extragalactic surveys provide significant statistical data for the study of crucial galaxy parameters used to constrain galaxy evolution, e.g. stellar mass (M$_*$) and star formation rate (SFR), under different environmental conditions.…
This survey paper offers a comprehensive review of methodologies utilizing machine learning (ML) classification techniques for identifying wafer defects in semiconductor manufacturing. Despite the growing body of research demonstrating the…
Star-galaxy separation is a crucial step in creating target catalogues for extragalactic spectroscopic surveys. A classifier biased towards inclusivity risks including spurious stars, wasting fibre hours, while a more conservative…
Ground-based optical surveys such as PanSTARRS, DES, and LSST, will produce large catalogs to limiting magnitudes of r > 24. Star-galaxy separation poses a major challenge to such surveys because galaxies---even very compact…
We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…
Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…
We present new lensing frequency estimates for existing and forthcoming deep near-infrared surveys, including those from JWST and VISTA. The estimates are based on the JAdes extraGalactic Ultradeep Artificial Realisations (JAGUAR) galaxy…
With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine…
A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are > $10^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~few x $10^6$ targets. As we…
The detection of point sources in Cosmic Microwave Background maps is usually based on a single-frequency approach, whereby maps at each frequency are filtered separately and the spectral information on the sources is derived combining the…
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin…
We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major…
Low-redshift strong-lensing galaxies can provide robust measurements of the stellar mass-to-light ratios in early-type galaxies (ETG), and hence constrain variations in the stellar initial mass function (IMF). At present, only a few such…
Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a…
Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…
We extend the work developed in previous papers on microlensing with a selection of variable stars. We use the Pixel Method to select variable stars on a set of 2.5 x 10**6 pixel light curves in the LMC Bar presented elsewhere. The previous…
The importance of photometric galaxy redshift estimation is rapidly increasing with the development of specialised powerful observational facilities. We develop a new photometric redshift estimation workflow TOPz to provide reliable and…