Related papers: Automatic classification of time-variable X-ray so…
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…
The XMM identification programme (XID) is accumulating Optical/IR images and spectra to identify thousands of XMM sources. The XID Database is the main repository of this data and permits easy selection and correlation of XMM X-ray and UV…
We present an extension to our XMM-Newton X-ray source catalogue of M 31, containing 39 newly found sources. In order to classify and identify more of the sources we search for X-ray time variability in XMM-Newton archival data of the M 31…
We present a systematic search for variability among the ROSAT All-Sky Survey (RASS) X-ray sources. We generated lightcurves for about 30000 X-ray point sources detected sufficiently high above background. For our variability study…
Objective: Systematic reviews of scholarly documents often provide complete and exhaustive summaries of literature relevant to a research question. However, well-done systematic reviews are expensive, time-demanding, and labor-intensive.…
We present a detailed analysis of the stellar content of the current version of the XMM-Newton slew survey (XMMSL2). Since stars emit only a small fraction of their total luminosity in the X-ray band, the stellar XMMSL2 sources ought to…
Using archival X-ray data from the second XMM-Newton serendipitous source catalogue, we present comparative analysis of the overall population of X-ray sources in the Large and Small Magellanic Clouds. We see a difference between the…
We present a collection of $\sim10,000$ X-ray sources from the 4th XMM-Newton Serendipitous Source Catalog (4XMM-DR13) with literature-verified classifications and multi-wavelength (MW) counterparts. We describe the process by which MW…
Aims. Construction of a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested…
The Survey Science Centre of the XMM-Newton satellite released the first incremental version of the 2XMM catalogue in August 2008 . With more than 220,000 X-ray sources, the 2XMMi was at that time the largest catalogue of X-ray sources ever…
Robust fast methods to classify variable light curves in large sky surveys are becoming increasingly important. While it is relatively straightforward to identify common periodic stars and particular transient events (supernovae, novae,…
To assist in the development of machine learning methods for automated classification of spectroscopic data, we have generated a universal synthetic dataset that can be used for model validation. This dataset contains artificial spectra…
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…
Deep anomaly detection models using a supervised mode of learning usually work under a closed set assumption and suffer from overfitting to previously seen rare anomalies at training, which hinders their applicability in a real scenario. In…
We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will…
The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time series observations that record the variability…
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…
Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing…
Random Forests (RFs) are strong machine learning tools for classification and regression. However, they remain supervised algorithms, and no extension of RFs to the one-class setting has been proposed, except for techniques based on…
The XMM-Newton Serendipitous Ultraviolet Source Survey (XMM-SUSS) is a catalogue of ultraviolet (UV) sources detected serendipitously by the Optical Monitor (XMM-OM) on-board the XMM-Newton observatory. The catalogue contains…