Journal article
Real-time, Value-driven Data Augmentation in the Era of LSST
The Astrophysical journal, v 893(2), p127
01 Apr 2020
Abstract
The deluge of data from time-domain surveys is rendering traditional human-guided data collection and inference techniques impractical. We propose a novel approach for conducting data collection for science inference in the era of massive large-scale surveys that uses value-based metrics to autonomously strategize and coordinate follow-up in real time. We demonstrate the underlying principles in the Recommender Engine For Intelligent Transient Tracking (REFITT) that ingests live alerts from surveys and value-added inputs from data brokers to predict the future behavior of transients and design optimal data augmentation strategies given a set of scientific objectives. The prototype presented in this paper is tested to work given simulated Rubin Observatory Legacy Survey of Space and Time (LSST) core-collapse supernova (CC SN) light curves from the Photometric LSST Astronomical Time-series Classification Challenge data set. CC SNe were selected for the initial development phase as they are known to be difficult to classify, with the expectation that any learning techniques for them should be at least as effective for other transients. We demonstrate the behavior of REFITT on a random LSST night given similar to 32,000 live CC SNe of interest. The system makes good predictions for the photometric behavior of the events and uses them to plan follow-up using a simple data-driven metric. We argue that machine-directed follow-up maximizes the scientific potential of surveys and follow-up resources by reducing downtime and bias in data collection.
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Details
- Title
- Real-time, Value-driven Data Augmentation in the Era of LSST
- Creators
- Niharika Sravan - Purdue University West LafayetteDan Milisavljevic - Purdue University West LafayetteJack M. Reynolds - Purdue University West LafayetteGeoffrey Lentner - Purdue University West LafayetteMark Linvill - Purdue University West Lafayette
- Publication Details
- The Astrophysical journal, v 893(2), p127
- Publisher
- IOP Publishing Ltd
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Physics
- Web of Science ID
- WOS:000529874200001
- Scopus ID
- 2-s2.0-85085306919
- Other Identifier
- 991021877481904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Astronomy & Astrophysics