Related papers: X-ray Sources Classification Using Machine Learnin…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
A procedure is described for estimating an optimum kernel for the detection by convolution of signals among Poissonian noise. The technique is applied to the detection of x-ray point sources in XMM-Newton data, and is shown to yield an…
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources,…
The discovery and subsequent study of optical counterparts to transient sources is crucial for their complete astrophysical understanding. Various gamma ray burst (GRB) detectors, and more notably the ground--based gravitational wave…
Future space telescopes may be able to directly image $\sim$10 - 100 planets with sizes and orbits consistent with habitable surface conditions ("exo-Earth candidates" or EECs), but observers will face difficulty in distinguishing these…
The launch of the \textit{Einstein Probe} unleashed a new era of high-energy transient discovery in the largely unexplored soft X-ray band. The \textit{Einstein Probe} has detected a significant number of fast X-ray transients that display…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…
Because of the electromagnetic radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave signal with electromagnetic…
Energy dispersive X-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such…
Stars are ubiquitous X-ray emitters and will be a substantial fraction of the X-ray sources detected in the on-going all-sky survey performed by the eROSITA instrument aboard the Spectrum Roentgen Gamma (SRG) observatory. We use the X-ray…
We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected…
Power systems are developing very fast nowadays, both in size and in complexity; this situation is a challenge for Early Event Detection (EED). This paper proposes a data- driven unsupervised learning method to handle this challenge.…
With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series…
XMM-Newton provides unprecedented insight into the X-ray Universe, recording variability information for hundreds of thousands of sources. Manually searching for interesting patterns in light curves is impractical, requiring an automated…
Wildfires present intricate challenges for prediction, necessitating the use of sophisticated machine learning techniques for effective modeling\cite{jain2020review}. In our research, we conducted a thorough assessment of various machine…
Convolution Neural Networks trained for the task of lens finding with similar architecture and training data as is commonly found in the literature are biased classifiers. An understanding of the selection function of lens finding neural…
With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we…
Despite being operational for only a short time, the Einstein Probe mission, with its large field of view and rapid localisation capabilities, has already significantly advanced the study of rapid variability in the soft X-ray sky. We…
Learning at the edge is a challenging task from several perspectives, since data must be collected by end devices (e.g. sensors), possibly pre-processed (e.g. data compression), and finally processed remotely to output the result of…
The search for atmospheric biosignatures in Earth-like exoplanets is one of the most pressing challenges in observational astrobiology. Detecting biogenic gases in terrestrial planets requires high-resolution observations and long…