Título:	Autonomous real-time science-driven follow-up in the era of LSST
Autor(es):	Srava, Niharika Milisavljevic, Dan Subrayan, Bhagya Weil, Kathryn Lentner, Geoffrey Linvill, Marie Louise Banovetz, John Reynolds, Jamie D. Wright, Aidan K. Andrews, Michael Markey, Carleen Earnhardt, Allison N. Lee, R. Townsend, Jonathan P Dickinson, Daniel J. Parker, Stephanie Margutti, Raffaella Chornock, Ryan Moriya, Takashi Bersten, Melina Orellana, Mariana Dominga
Fecha de publicación:	ene-2021
Revista:	Bulletin of the AAS
Resumen:	The deluge of data from time-domain surveys is rendering traditional human-guided data collection and inference techniques impractical. In order to maximize the science potential of surveys and follow-up resources, autonomous systems reacting in real-time to maximize diverse science goals are needed. We designate the class of systems that strategize and coordinate value-driven follow-up in real-time ORACLEs (Object Recommender for Augmentation and Coordinating Liaison Engine) and demonstrate key underlying principles in a prototype ORACLE called Recommender Engine For Intelligent Transient Tracking (REFITT). REFITT is an autonomous real-time decision support and resource allocation system that ingests live alerts from surveys and value-added inputs from data brokers, and using machine-learning based predictive modeling for sparse multi-channel time-series strategizes, optimal follow-up using value-based metrics. We validate the performance of REFITT given simulated core-collapse supernova light-curves from the Rubin Observatory Legacy Survey of Space and Time, and value-added inputs from data brokers. We suggest that ORACLEs like REFITT are an essential component in the broader software infrastructure necessary to support survey science.
URI:	http://rid.unrn.edu.ar/handle/20.500.12049/8459
Otros enlaces:	https://baas.aas.org/pub/2021n1i211p05/release/1 https://ui.adsabs.harvard.edu/abs/2021AAS...23721105S/abstract
Aparece en las colecciones:	Objetos de conferencia

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