Related papers: Steps toward a classifier for the Virtual Observat…
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…
Over the past 30 years, numerous large-scale photometric astronomical surveys have been conducted, including SDSS, Pan-STARRS, Gaia,2MASS, WISE, and others. These surveys provide extensive photometric measurements that can be used to infer…
With the imminent data releases from next-generation spectroscopic surveys, hundreds of thousands of white dwarf spectra are expected to become available within the next few years, increasing the data volume by an order of magnitude. This…
There is a growing need for massive computational resources for the analysis of new astronomical datasets. To tackle this problem, we present here our first steps towards marrying two new and emerging technologies; the Virtual Observatory…
Context. The populations of small bodies of the Solar System (asteroids, comets, Kuiper-Belt objects) are used to constrain the origin and evolution of the Solar System. Both their orbital distribution and composition distribution are…
Many synoptic surveys are observing large parts of the sky multiple times. The resulting lightcurves provide a wonderful window to the dynamic nature of the universe. However, there are many significant challenges in analyzing these light…
The Sloan Digital Sky Survey has obtained 450 square degrees of photometric scan data, in five bands (u',g',r',i',z'), which we use to identify clusters of galaxies. We illustrate how we do star-galaxy separation, and present a simple and…
Supervised statistical classification is a vital tool for satellite image processing. It is useful not only when a discrete result, such as feature extraction or surface type, is required, but also for continuum retrievals by dividing the…
This paper introduces a novel sector-based methodology for star-galaxy classification, leveraging the latest Sloan Digital Sky Survey data (SDSS-DR18). By strategically segmenting the sky into sectors aligned with SDSS observational…
We describe the application of data mining algorithms to research problems in astronomy. We posit that data mining has always been fundamental to astronomical research, since data mining is the basis of evidence-based discovery, including…
The Earth observation satellites have been monitoring the earth's surface for a long time, and the images taken by the satellites contain large amounts of valuable data. However, it is extremely hard work to manually analyze such huge data.…
With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial…
The Sloan Digital Sky Survey (SDSS) is making a multi-colour, three dimensional map of the nearby Universe. The survey is in two parts. The first part is imaging one quarter of the sky in five colours from the near ultraviolet to the near…
Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are…
One important tool is the optimal clustering of data into useful categories. Dividing similar objects into a smaller number of clusters is of importance in many applications. These include search engines, monitoring of academic performance,…
Many real-life data are described by categorical attributes without a pre-classification. A common data mining method used to extract information from this type of data is clustering. This method group together the samples from the data…
In this contribution we would like to emphasize the usefulness of data mining multiwavelength surveys like the Sloan Digital Sky Survey (SDSS) or the ROSAT All Sky Survey (RASS) -- which have become available to the public recently -- in…
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis.…
Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure.…
With the advent of digital astronomy, new benefits and new problems have been presented to the modern day astronomer. While data can be captured in a more efficient and accurate manor using digital means, the efficiency of data retrieval…