Related papers: Exploring X-ray variability with unsupervised mach…
Efficient and automated classification of periodic variable stars is becoming increasingly important as the scale of astronomical surveys grows. Several recent papers have used methods from machine learning and statistics to construct…
Vision Transformers (ViTs) have demonstrated exceptional performance in various vision tasks. However, they tend to underperform on smaller datasets due to their inherent lack of inductive biases. Current approaches address this limitation…
The XMM-RM project was designed to provide X-ray coverage of the Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) field. 41 XMM-Newton exposures, placed surrounding the Chandra AEGIS field, were taken, covering an area of 6.13 deg^2…
Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor…
The growing volume of data produced by large astronomical surveys necessitates the development of efficient analysis techniques capable of effectively managing high-dimensional datasets. This study addresses this need by demonstrating some…
We present preliminary results from a detailed analysis of the X-ray point sources in the XMM-Newton survey of M31. These sources are expected to be mostly X-ray binaries. We have so far studied 225 of the 535 sources found by automated…
Aims. M 31 has been completely imaged for the first time obtaining a luminosity lower limit ~10^35 erg s-1 in the 0.2-4.5 keV band. Our XMM-Newton EPIC survey combines archival observations with observations taken between June 2006 and…
Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. Uncorrected…
We report the final optical identifications of the medium-depth (~60 ksec), contiguous (2 deg^2) XMM-Newton survey of the COSMOS field. XMM-Newton has detected ~800 X-ray sources down to limiting fluxes of ~5x10^{-16}, ~3x10^{-15}, and…
In this paper we serendipitously identify X-ray cluster candidates using XMM-Newton archival observations complemented by 5-band optical photometric follow-up observations (r~23 mag) as part of the X-ray Identification (XID) programme. Our…
Self-Organizing Maps (SOM) are a classical method for unsupervised learning, vector quantization, and topographic mapping of high-dimensional data. However, existing SOM formulations often involve a trade-off between computational…
A well characterised detection pipeline is an important ingredient for X-ray cluster surveys. We present the final development of the XXL Survey pipeline. The pipeline optimally uses X-ray information by combining many overlapping…
We present results from a study of the non-nuclear discrete sources in a sample of three nearby spiral galaxies (NGC 4395, NGC 4736, and NGC 4258) based on XMM-Newton archival data supplemented with Chandra data for spectral and timing…
We report the scaling relations derived by fitting the X-ray parameters determined from analyzing the XMM-Newton observations of 120 galaxy clusters in the Planck Early Sunyaev-Zel'dovich sample spanning the redshift range of…
We present a 100 yr optical lightcurve of the recently discovered supersoft X-ray source RX~J0019.8+2156 as deduced from photographic plates of Harvard and Sonneberg Observatory. Apart from the periodic orbital variations two different…
Our goal is to characterize AGN populations by comparing their X-ray and optical classifications. We present a sample of 99 spectroscopically identified X-ray point sources in the XMM-LSS survey which are significantly detected in the…
During the last decade, a considerable amount of effort has been made to classify variable stars using different machine learning techniques. Typically, light curves are represented as vectors of statistical descriptors or features that are…
Self-Organizing Maps (SOMs) provide topology-preserving projections of high-dimensional data, yet their use as generative models remains largely unexplored. We show that the activation pattern of a SOM -- the squared distances to its…
The XMM-Newton spectral-fit database (XMMFITCAT) is a catalogue of spectral fitting results for the source detections within the XMM-Newton Serendipitous source catalogue with more than 50 net (background-subtracted) counts per detector in…
Most change detection methods assume that pre-change and post-change images are acquired by the same sensor. However, in many real-life scenarios, e.g., natural disaster, it is more practical to use the latest available images before and…