Data challenges of time domain astronomy

Abstract

Astronomy has been at the forefront of the development of the techniques and methodologies of data intensive science for over a decade with large sky surveys and distributed efforts such as the Virtual Observatory. However, it faces a new data deluge with the next generation of synoptic sky surveys which are opening up the time domain for discovery and exploration. This brings both new scientific opportunities and fresh challenges, in terms of data rates from robotic telescopes and exponential complexity in linked data, but also for data mining algorithms used in classification and decision making. In this paper, we describe how an informatics-based approach-part of the so-called "fourth paradigm" of scientific discovery-is emerging to deal with these. We review our experiences with the Palomar-Quest and Catalina Real-Time Transient Sky Surveys; in particular, addressing the issue of the heterogeneity of data associated with transient astronomical events (and other sensor networks) and how to manage and analyze it.

15 pages, 3 figures, to appear in special issue of Distributed and Parallel Databases on Data Intensive eScience

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Cited by in corpus (6)

• Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases
• An analysis of feature relevance in the classification of astronomical transients with machine learning methods
• Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys
• Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys
• FALCO: a Foundation model of Astronomical Light Curves for time dOmain astronomy
• Spatially resolved stellar-to-total dynamical mass relation: Radial variations, gradients and profiles of galaxy stellar populations