Altmetric

STACCATO: a novel solution to supernova photometric classification with biased training sets

File Description SizeFormat 
ClassificationSNIA.pdfAccepted version1.4 MBAdobe PDFView/Open
Title: STACCATO: a novel solution to supernova photometric classification with biased training sets
Authors: Revsbech, EA
Trotta, R
Van Dyk, DA
Item Type: Journal Article
Abstract: We present a new solution to the problem of classifying Type Ia supernovae from their light curves alone given a spectroscopically confirmed but biased training set, circumventing the need to obtain an observationally expensive unbiased training set. We use Gaussian processes (GPs) to model the supernovae's (SN's) light curves, and demonstrate that the choice of covariance function has only a small influence on the GPs ability to accurately classify SNe. We extend and improve the approach of Richards et al. – a diffusion map combined with a random forest classifier – to deal specifically with the case of biased training sets. We propose a novel method called Synthetically Augmented Light Curve Classification (STACCATO) that synthetically augments a biased training set by generating additional training data from the fitted GPs. Key to the success of the method is the partitioning of the observations into subgroups based on their propensity score of being included in the training set. Using simulated light curve data, we show that STACCATO increases performance, as measured by the area under the Receiver Operating Characteristic curve (AUC), from 0.93 to 0.96, close to the AUC of 0.977 obtained using the ‘gold standard’ of an unbiased training set and significantly improving on the previous best result of 0.88. STACCATO also increases the true positive rate for SNIa classification by up to a factor of 50 for high-redshift/low-brightness SNe.
Issue Date: 9-Oct-2017
Date of Acceptance: 29-Sep-2017
URI: http://hdl.handle.net/10044/1/53914
DOI: https://dx.doi.org/10.1093/mnras/stx2570
ISSN: 0035-8711
Publisher: Oxford University Press
Journal / Book Title: Monthly Notices of the Royal Astronomical Society
Volume: 473
Issue: 3
Copyright Statement: © 2017 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.
Sponsor/Funder: Imperial College Trust
Imperial College Trust
European Commission
Science and Technology Facilities Council
Science and Technology Facilities Council (STFC)
Funder's Grant Number: N/A
NA
H2020-MSCA-RISE-2015-691164
ST-N000838
ST/N000838/1
Keywords: astro-ph.IM
0201 Astronomical And Space Sciences
Astronomy & Astrophysics
Publication Status: Published
Appears in Collections:Physics
Mathematics
Astrophysics
Statistics
Faculty of Natural Sciences



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commonsx