Related papers: X-ray Sources Classification Using Machine Learnin…

We describe a new method of transient point source localization for coded-aperture X-ray detectors that we have applied to data from the HETE Wide-Field X-Ray Monitor (WXM). The method is based upon the calculation of the likelihood…

Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or…

Robust environment perception is essential for decision-making on robots operating in complex domains. Principled treatment of uncertainty sources in a robot's observation model is necessary for accurate mapping and object detection. This…

The limited angular resolution of gamma-ray telescopes prevents the straight identification of the majority of the sources detected so far. While the only certified galactic gamma-ray emitters are associated with pulsars, more than 90% of…

The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…

Realizing high-throughput aberration-corrected Scanning Transmission Electron Microscopy (STEM) exploration of atomic structures requires rapid tuning of multipole probe correctors while compensating for the inevitable drift of the optical…

This study presents a comprehensive evaluation of various classification algorithms used for the detection of exoplanets using labeled time series data from the Kepler mission. The study investigates the performance of six commonly employed…

A design study is currently in progress for a third generation gravitational-wave (GW) detector called Einstein Telescope (ET). An important kind of source for ET will be the inspiral and merger of binary neutron stars (BNS) up to $z \sim…

Usually considered as a classification problem, entity resolution (ER) can be very challenging on real data due to the prevalence of dirty values. The state-of-the-art solutions for ER were built on a variety of learning models (most…

We present a new method of transient point source deconvolution for coded-aperture X-Ray detectors. Our method is based upon the calculation of the likelihood function and its interpretation as a probability density for the transient source…

The recent launches of the Einstein Probe (EP) and the Space Variable Objects Monitor (SVOM) mission have led to the detection of a growing number of long GRBs with significant, early soft X-ray flux during their gamma-ray emission,…

Most of the existing classification methods are aimed at minimization of empirical risk (through some simple point-based error measured with loss function) with added regularization. We propose to approach this problem in a more information…

Improvements in X-ray optics critically depend on the measurement of their optical performance. The knowledge of wavefront aberrations, for example, can be used to improve the fabrication of optical elements or to design phase correctors to…

Spectral signatures are crucial in the era of large X-ray surveys. Automatic machine learning methods have proven useful in this respect, but so far they have not been applied to large spectral datasets, such as the Chandra Source Catalog…

We have investigated a number of factors that can have significant impacts on the classification performance of $\gamma$-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of…

When it comes to identifying or to characterizing gamma-ray sources, X-ray observations are of paramount importance. Correlated X-and-gamma-ray flux variations are a powerful identification tool, if the gamma-ray source is unidentified, or…

Automated experiments in scanning transmission electron microscopy (STEM) require rapid image segmentation to optimize data representation for human interpretation, decision-making, site-selective spectroscopies, and atomic manipulation.…

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric…

We anticipate that hundreds of thousands of distant, strongly gravitationally lensed sources will be detectable with the European Space Agency's (ESA) Euclid mission and the Rubin Observatory Legacy Survey of Space and Time. We consider the…

We present EMUSE (Evolutionary Map of the Universe Search Engine), a tool designed for searching specific radio sources within the extensive datasets of the EMU (Evolutionary Map of the Universe) survey, with potential applications to other…