Conference proceeding
Autonomous Real-Time Science-Driven Follow-up of Survey Transients
BIG-DATA-ANALYTICS IN ASTRONOMY, SCIENCE, AND ENGINEERING, BDA 2021, v 13167, pp 59-72
01 Jan 2022
Abstract
Astronomical surveys continue to provide unprecedented insights into the time-variable Universe and will remain the source of groundbreaking discoveries for years to come. However, their data throughput has overwhelmed the ability to manually synthesize alerts for devising and coordinating necessary follow-up with limited resources. The advent of Rubin Observatory, with alert volumes an order of magnitude higher at otherwise sparse cadence, presents an urgent need to overhaul existing human-centered protocols in favor of machine-directed infrastructure for conducting science inference and optimally planning expensive follow-up observations.
We present the first implementation of autonomous real-time science-driven follow-up using value iteration to perform sequential experiment design. We demonstrate it for strategizing photometric augmentation of Zwicky Transient Facility Type Ia supernova light-curves given the goal of minimizing SALT2 parameter uncertainties. We find a median improvement of 2-6% for SALT2 parameters and 3-11% for photometric redshift with 2-7 additional data points in g, r and/or i compared to random augmentation. The augmentations are automatically strategized to complete gaps and for resolving phases with high constraining power (e.g. around peaks). We suggest that such a technique can deliver higher impact during the era of Rubin Observatory for precision cosmology at high redshift and can serve as the foundation for the development of general-purpose resource allocation systems.
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Details
- Title
- Autonomous Real-Time Science-Driven Follow-up of Survey Transients
- Creators
- Niharika Sravan - California Institute of TechnologyMatthew J. Graham - California Institute of TechnologyChristoffer Fremling - California Institute of TechnologyMichael W. Coughlin - University of Minnesota
- Contributors
- S Sachdeva (Editor)Y Watanobe (Editor)S Bhalla (Editor)
- Publication Details
- BIG-DATA-ANALYTICS IN ASTRONOMY, SCIENCE, AND ENGINEERING, BDA 2021, v 13167, pp 59-72
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 14
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Physics
- Web of Science ID
- WOS:000774231300005
- Scopus ID
- 2-s2.0-85126241065
- Other Identifier
- 991021877361504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Computer Science, Theory & Methods