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Title: Towards a Real-time Transient Classification Engine

Abstract

Temporal sampling does more than add another axis to the vector of observables. Instead, under the recognition that how objects change (and move) in time speaks directly to the physics underlying astronomical phenomena, next-generation wide-field synoptic surveys are poised to revolutionize our understanding of just about anything that goes bump in the night (which is just about everything at some level). Still, even the most ambitious surveys will require targeted spectroscopic follow-up to fill in the physical details of newly discovered transients. We are now building a new system intended to ingest and classify transient phenomena in near real-time from high-throughput imaging data streams. Described herein, the Transient Classification Project at Berkeley will be making use of classification techniques operating on"features" extracted from time series and contextual (static) information. We also highlight the need for a community adoption of a standard representation of astronomical time series data (i.e.,"VOTimeseries").

Authors:
; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
Computational Research Division; Physics Division
OSTI Identifier:
934980
Report Number(s):
LBNL-633E
TRN: US200815%%63
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Astronomische Nachrichten
Additional Journal Information:
Journal Volume: 329; Journal Issue: 3; Related Information: Journal Publication Date: 2008
Country of Publication:
United States
Language:
English
Subject:
79; CLASSIFICATION; ENGINES; PHYSICS; SAMPLING; TRANSIENTS; VECTORS; methods: statistical, methods: data analysis, surveys

Citation Formats

Nugent, Peter E, Bloom, Josh, Starr, Dan, Butler, Nat, Nugent, Peter, Rischard, M., Eads, D., and Poznanski, Dovi. Towards a Real-time Transient Classification Engine. United States: N. p., 2008. Web.
Nugent, Peter E, Bloom, Josh, Starr, Dan, Butler, Nat, Nugent, Peter, Rischard, M., Eads, D., & Poznanski, Dovi. Towards a Real-time Transient Classification Engine. United States.
Nugent, Peter E, Bloom, Josh, Starr, Dan, Butler, Nat, Nugent, Peter, Rischard, M., Eads, D., and Poznanski, Dovi. Fri . "Towards a Real-time Transient Classification Engine". United States. https://www.osti.gov/servlets/purl/934980.
@article{osti_934980,
title = {Towards a Real-time Transient Classification Engine},
author = {Nugent, Peter E and Bloom, Josh and Starr, Dan and Butler, Nat and Nugent, Peter and Rischard, M. and Eads, D. and Poznanski, Dovi},
abstractNote = {Temporal sampling does more than add another axis to the vector of observables. Instead, under the recognition that how objects change (and move) in time speaks directly to the physics underlying astronomical phenomena, next-generation wide-field synoptic surveys are poised to revolutionize our understanding of just about anything that goes bump in the night (which is just about everything at some level). Still, even the most ambitious surveys will require targeted spectroscopic follow-up to fill in the physical details of newly discovered transients. We are now building a new system intended to ingest and classify transient phenomena in near real-time from high-throughput imaging data streams. Described herein, the Transient Classification Project at Berkeley will be making use of classification techniques operating on"features" extracted from time series and contextual (static) information. We also highlight the need for a community adoption of a standard representation of astronomical time series data (i.e.,"VOTimeseries").},
doi = {},
journal = {Astronomische Nachrichten},
number = 3,
volume = 329,
place = {United States},
year = {2008},
month = {2}
}