Related papers: Classifying white dwarfs from multi-object spectro…
We present the discovery of 49 new photometrically classified T dwarfs from the combination of large infrared and optical surveys combined with follow-up TNG photometry. We used multi-band infrared and optical photometry from the UKIRT and…
Wide-field time-domain photometric sky surveys are now finding hundreds of eclipsing white dwarf plus M dwarf binaries, a population encompassing a wealth of information and potential insight into white dwarf and close binary astrophysics.…
Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data…
Machine Learning (ML) has found several applications in spectroscopy, including being used to recognise minerals and estimate elemental composition. In this work, we present novel methods for automatic mineral identification based on…
Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of machine learning techniques are tested for their ability to…
We present the first discoveries of the double-lined double white dwarf (DBL) survey that targets over-luminous sources with respect to the canonical white dwarf cooling sequence according to a set of well-defined criteria. The primary goal…
We present a technique to identify spectrophotometrically variable L7-T3 brown dwarfs with single-epoch, low-resolution, near-infrared SpeX spectra. We calculated spectral indices on known variable brown dwarfs and used them to select 11…
This paper proposes the design of decision models based on Computational Intelligence techniques applied to image sequences of dynamic laser speckle. These models aim to identify image regions of biological specimens illuminated by a…
We demonstrate a new Bayesian technique to invert color-magnitude diagrams of main sequence and white dwarf stars to reveal the underlying cluster properties of age, distance, metallicity, and line-of-sight absorption, as well as individual…
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;…
Many current neural networks for medical imaging generalise poorly to data unseen during training. Such behaviour can be caused by networks overfitting easy-to-learn, or statistically dominant, features while disregarding other potentially…
We rederive the luminosity function for the sample of DA white dwarfs from the Kiso Schmidt ultraviolet excess survey (KUV stars) using the spectroscopic method where the atmospheric parameters (Teff and log g) and absolute visual…
The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or…
Currently there are two main techniques for independently determining the ages of stellar populations: main sequence evolution theory (via cluster isochrones) and white dwarf cooling theory. Open clusters provide the ideal environment for…
Modern applications such as voice recognition rely on the ability to compare signals to pre-recorded ones to classify them. However, this comparison typically needs to ignore differences due to signal noise, temporal offset, signal…
The unique cost, flexibility, speed, and efficiency of modern UAVs make them an attractive choice in many applications in contemporary society. This, however, causes an ever-increasing number of reported malicious or accidental incidents,…
Biodiversity conservation depends on accurate, up-to-date information about wildlife population distributions. Motion-activated cameras, also known as camera traps, are a critical tool for population surveys, as they are cheap and…
In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework. Here, an extended self-dual attribute profile (ESDAP) is investigated to extract…
Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G…
Deep Neural Networks (DNN) have shown great promise in many classification applications, yet are widely known to have poorly calibrated predictions when they are over-parametrized. Improving DNN calibration without comprising on model…