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

Ultraluminous X-ray sources (ULXs) represent an extreme class of accreting compact objects: from the identification of some of the accretors as neutron stars to the detection of powerful winds travelling at 0.1-0.2 c, the increasing…

ClassX is a project aimed at creating an automated system to classify X-ray sources and is envisaged as a prototype of the Virtual Observatory. As a system, ClassX integrates into a pipeline a network of classifiers and an engine that…

The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical…

The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find…

Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous…

Astronomy has entered the multi-messenger data era and Machine Learning has found widespread use in a large variety of applications. The exploitation of synoptic (multi-band and multi-epoch) surveys, like LSST (Legacy Survey of Space and…

The planned Laser Interferometer Space Antenna (LISA) will detect gravitational wave signals from a wide range of sources. However, disentangling individual signals from the source-dominated data stream is a challenging problem and the…

Following its launch on 2024 January 9, the Einstein Probe (EP) telescope has detected hundreds of fast X-ray transients (FXTs), yet their physical origins remain elusive. Understanding their luminosity function and formation rate is…

Since the start of the Wide Angle Search for Planets (WASP) program, more than 160 transiting exoplanets have been discovered in the WASP data. In the past, possible transit-like events identified by the WASP pipeline have been vetted by…

The rate of image acquisition in modern synoptic imaging surveys has already begun to outpace the feasibility of keeping astronomers in the real-time discovery and classification loop. Here we present the inner workings of a framework,…

In today's era, a tremendous amount of data is generated by different observatories and manual classification of data is something which is practically impossible. Hence, to classify and categorize the objects there are multiple machine and…

We report the LWFA driven X-ray sources development at ELI beamlines. ELI Gammatron beamline provides X-ray pulses of energies from 1-100 keV in betatron scheme, and up to a MeV in Compton scheme. A state-of-the-art Ti:Sa diode-pumped HAPLS…

Label noise poses a significant challenge in Earth Observation (EO), often degrading the performance and reliability of supervised Machine Learning (ML) models. Yet, given the critical nature of several EO applications, developing robust…

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and…

We present the discovery of a peculiar X-ray transient, EP241021a, by the Einstein Probe (EP) mission, and the results from multiwavelength follow-up observations. The transient was first detected with the Wide-field X-ray Telescope as an…

Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of…

Identifying X-ray binary (XRB) candidates in nearby galaxies requires distinguishing them from possible contaminants including foreground stars and background active galactic nuclei. This work investigates the use of supervised machine…

We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe…

Recently, neural networks have found many applications in different fields including Genetics, Pharmacy, Astrophysics and High Energy Physics [1-3]. In the field of accelerator physics it has been used for control systems [4]. In this paper…

We present some of the salient aspects of the scientific motivation for high resolution soft X-ray spectroscopy of early-type stars with the Line Emission Mapper X-ray Probe. The major strength of {\it LEM} for hot star physics is its large…