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Published June 2016 | Submitted
Journal Article Open

Real-time data mining of massive data streams from synoptic sky surveys

Abstract

The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arises in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.

Additional Information

© 2016 Elsevier B.V. Received 16 March 2015; Received in revised form 3 October 2015; Accepted 19 October 2015. This work was supported in part by the NASA grant 08-AISR08-0085, the NSF grants AST-0909182, IIS-1118041, and AST-1313422, by the W. M. Keck Institute for Space Studies at Caltech (KISS), and by the US Virtual Astronomical Observatory, itself supported by the NSF grant AST-0834235. Some of this work was assisted by the Caltech students Nihar Sharma, Yutong Chen, Alex Ball, Victor Duan, Allison Maker, and others, supported by the Caltech SURF program. We thank numerous collaborators and colleagues, especially within the CRTS survey team, and the worldwide Virtual Observatory and astroinformatics community, for stimulating discussions.

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August 22, 2023
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