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By E. F. Guinan, E. J. Devinney, S. G. Engle, M. Degeorge, G. P. Mccook, P. A. Maurone, D. James, D. H. Bradstreet, C. R. Alcock, J. Devor, R. Seaman, T. Zwitter, K. Long, R. E. Wilson, I. Ribas and A. Gimenez

Abstract

Observational astronomy has changed drastically in the last decade: manually driven target-by-target instruments have been replaced by fully automated robotic telescopes. Data acquisition methods have advanced to the point that terabytes of data are flowing in and being stored on a daily basis. At the same time, the vast majority of analysis tools in stellar astrophysics still rely on manual expert interaction. To bridge this gap, we foresee that the next decade will witness a fundamental shift in the approaches to data analysis: case-by-case methods will be replaced by fully automated pipelines that will process the data from their reduction stage, through analysis, to storage. While major effort has been invested in data reduction automation, automated data analysis has mostly been neglected despite the urgent need. Scientific data mining will face serious challenges to identify, understand and eliminate the sources of systematic errors that will arise from this automation. As a special case, we present an artificial intelligence (AI) driven pipeline that is prototyped in the domain of stellar astrophysics (eclipsing binaries in particular), current results and the challenges still ahead. 1 Villanova University

Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.313.7708
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