DAMEWARE: A web cyberinfrastructure for astrophysical data mining

Abstract

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. The new panchromatic, synoptic sky surveys require advanced tools for discovering patterns and trends hidden behind data which are both complex and of high dimensionality. We present DAMEWARE (DAta Mining & Exploration Web Application REsource): a general purpose, web-based, distributed data mining environment developed for the exploration of large datasets, and finely tuned for astronomical applications. By means of graphical user interfaces, it allows the user to perform classification, regression or clustering tasks with machine learning methods. Salient features of DAMEWARE include its capability to work on large datasets with minimal human intervention, and to deal with a wide variety of real problems such as the classification of globular clusters in the galaxy NGC1399, the evaluation of photometric redshifts and, finally, the identification of candidate Active Galactic Nuclei in multiband photometric surveys. In all these applications, DAMEWARE allowed to achieve better results than those attained with more traditional methods. With the aim of providing potential users with all needed information, in this paper we briefly describe the technological background of DAMEWARE, give a short introduction to some relevant aspects of data mining, followed by a summary of some science cases and, finally, we provide a detailed description of a template use case.

To appear in PASP (accepted for pubblication)

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• Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case
• Astroinformatics based search for globular clusters in the Fornax Deep Survey
• A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on Solar System bodies
• Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning
• Data Driven Discovery in Astrophysics
• Statistical characterization and classification of astronomical transients with Machine Learning in the era of the Vera C. Rubin Observatory
• Neural Gas based classification of Globular Clusters
• Galaxy evolution within the Kilo-Degree Survey
• Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground