Application of Data Mining in Astronomy and Astroparticle Physics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".
Deadline for manuscript submissions: closed (25 May 2022) | Viewed by 439
Special Issue Editor
Interests: astroparticle physics, active galactic nuclei; cosmic rays and gamma rays; gravitational wave detection; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As is the case in many other fields, finding ways to successfully handle a large amount of data is becoming more and more important in Astronomy and Astroparticle Physics. In the case of existing and future projects, tens to hundreds of Petabytes are not unusual. While data storage is mostly a technological challenge, efficient data access and powerful processing becomes a major concern. Low- and high-level data-oriented, very flexible database solutions supersede classical file-oriented tree-like storage systems as an archiving solution. Applying a newly developed analysis method on such a large data set requires the appropriate performance of the storage engine and computing systems involved. Data reduction is highly topical. Supervised and unsupervised feature extraction allow for the production of suitable summary data. Although new challenges arise in the application of machine learning techniques, rapid progress in data assessment even by mostly inexperienced people is achieved. Empirical observables previously hidden behind too complex and multidimensional interdependencies in the data can be revealed and utilized. Additionally, readily available rich libraries allow shifting focus from the implementation of basic analysis algorithms toward a deeper understanding of their application and results. The use of common analysis techniques and software facilitate interdisciplinary communication and co-operation, providing a substantial efficiency gain.
Prof. Dr. Thomas Bretz
Guest Editor
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Keywords
- data analysis and feature extraction
- data archiving and large data volumes
- data mining and processing
- machine and deep learning
- analysis libraries
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