Related papers: Exploring X-ray variability with unsupervised mach…
Determining the number of clusters in a dataset is a fundamental issue in data clustering. Many methods have been proposed to solve the problem of selecting the number of clusters, considering it to be a problem with regard to model…
Aims: During its all-sky survey phase, the eROSITA X-ray telescope onboard SRG scans through the ecliptic poles every 4 hours. This extensive data set of long-duration, frequent, and consistent observations of thousands of X-ray sources is…
Though quite challenging, leveraging large-scale unlabeled or partially labeled images in a cost-effective way has increasingly attracted interests for its great importance to computer vision. To tackle this problem, many Active Learning…
We present the results of a search for X-ray variability in the galactic open cluster NGC 2516. This cluster has been observed on 8 occasions using XMM-Newton making it an excellent target for such a study. We find 4 sources which show…
This work describes the implementation and application of a correlation determination method based on Self Organizing Maps and Bayesian Inference (SOMBI). SOMBI aims to automatically identify relations between different observed parameters…
We have examined simultaneous X-ray and optical light curves of a sample of eight nearby Seyfert 1 galaxies observed using the EPIC X-ray cameras and Optical Monitor on board XMM. The observations span ~1 day and revealed optical…
Galaxy populations show bimodality in a variety of properties: stellar mass, colour, specific star-formation rate, size, and S\'ersic index. These parameters are our feature space. We use an existing sample of 7556 galaxies from the Galaxy…
In this paper we apply the Self-Organized Map (SOM) method for clustering the DJIA and NASDAQ100 portfolios for determination of non-linear correlations between stocks. We represent the application of this method as alternative to…
Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains. However, acquired time series are typically high-dimensional and difficult to interpret. Expressive deep learning (DL)…
Streaming data clustering is a popular research topic in data mining and machine learning. Since streaming data is usually analyzed in data chunks, it is more susceptible to encounter the dynamic cluster imbalance issue. That is, the…
(Abridged) We present the results of the systematic survey of X-ray sources in the central region of M31 using the data from XMM-Newton observations performed in the years 2000-2004. The spectral properties and variability of 123 bright…
The need for labeled data is among the most common and well-known practical obstacles to deploying deep learning algorithms to solve real-world problems. The current generation of learning algorithms requires a large volume of data labeled…
The rapid increase in serendipitous X-ray source detections requires the development of novel approaches to efficiently explore the nature of X-ray sources. If even a fraction of these sources could be reliably classified, it would enable…
The self-organizing map (SOM) is an unsupervised artificial neural network that is widely used in, e.g., data mining and visualization. Supervised and semi-supervised learning methods have been proposed for the SOM. However, their teacher…
This paper presents the clustering properties of hard (2-8 keV) X-ray selected sources detected in a wide field ($\approx \rm 2 deg^{2}$) shallow [$f_X(\rm 2-8 keV)\approx 10^{-14}\rm erg cm^{-2} s^{-1}$] and contiguous XMM-{\it Newton}…
In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with…
The deepest X-ray images of M31, obtained with XMM-Newton, are examined in this paper to derive spectral and statistical properties of the population of the softest X-ray sources. Classifying supersoft X-ray sources (SSS) with criteria…
Massive galaxy clusters are interesting astrophysical and cosmological study objects, but are relatively rare. In the redshift range z = 0.25 to 0.5 which is, for example, a favourable region for gravitational lensing studies, about 100…
Reflected or scattered light produce artefacts in astronomical observations that can negatively impact the scientific study. Hence, automated detection of these artefacts is highly beneficial, especially with the increasing amounts of data…
This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on the SOM, SO-Net…