Abstract:
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Astrophysics has been at the forefront of data science and statistics for decades, yet The Rubin Observatory Legacy Survey of Space and Time (LSST) will usher a new era in data-intensive astrophysics. The next-generation ground-based astronomical survey, LSST will generate 20TB of information-rich imaging data every night for 10 years. A core deliverable of the survey is the exploration of the transient sky: astronomical sources that change brightness, color, and position, enabling a deep understanding of stellar physics and cosmology. Cutting edge methodologies that scale with the data volume to address event detection in stochastic time series, outliers and anomaly detection, and lightcurve characterization, typically in irregularly time spaced time series at the limit of the signal-to-noise are under development. However, to maximize the scientific throughput of the survey, statistics and data science methodologies have to enter the picture at the experimental design level. I will review applications of machine learning in experimental design to assure the Rubin LSST enables real-time detection of rare and rapidly evolving transients and the discovery of unknown unknowns.
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