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Photometric classification and redshift estimation of LSST Supernovae

Dai, Mi and Kuhlmann, Steve and Wang, Yun and Kovacs, Eve (2018) Photometric classification and redshift estimation of LSST Supernovae. Monthly Notices of the Royal Astronomical Society, 477 (3). pp. 4142-4151. ISSN 0035-8711. https://resolver.caltech.edu/CaltechAUTHORS:20180620-161516818

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Abstract

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of spectroscopically following up all the SNe. We investigate the performance of an SN classifier that uses SN colours to classify LSST SNe with the Random Forest classification algorithm. Our classifier results in an area-under-the-curve of 0.98 which represents excellent classification. We are able to obtain a photometric SN sample containing 99 per cent SNe Ia by choosing a probability threshold. We estimate the photometric redshifts (photo-z) of SNe in our sample by fitting the SN light curves using the SALT2 model with nested sampling. We obtain a mean bias (⟨z_(phot) − z_(spec)⟩) of 0.012 with σ((z_(phot) − z_(spec))/(1+z_(spec)) = 0.0294 without using a host-galaxy photo-z prior, and a mean bias (⟨z_(phot) − z_(spec)⟩) of 0.0017 with σ((z_(phot) − z_(spec))/(1 + z_(spec)) = 0.0116 using a host-galaxy photo-z prior. Assuming a flat ΛCDM model with Ωm = 0.3, we obtain Ωm of 0.305 ± 0.008 (statistical errors only), using the simulated LSST sample of photometric SNe Ia (with intrinsic scatter σ_(int) = 0.11) derived using our methodology without using host-galaxy photo-z prior. Our method will help boost the power of SNe from the LSST as cosmological probes.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/sty965DOIArticle
http://arxiv.org/abs/1701.05689arXivDiscussion Paper
Additional Information:© 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) Accepted 2018 April 12. Received 2018 March 29; in original form 2017 January 23. Some of the computing for this project was performed at the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU). Argonne National Laboratory’s work was supported under U.S. Department of Energy, contract DE-AC02-06CH11357.
Group:Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-AC02-06CH11357
Subject Keywords:supernovae: general – cosmology: observations
Issue or Number:3
Record Number:CaltechAUTHORS:20180620-161516818
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180620-161516818
Official Citation:Mi Dai, Steve Kuhlmann, Yun Wang, Eve Kovacs; Photometric classification and redshift estimation of LSST Supernovae, Monthly Notices of the Royal Astronomical Society, Volume 477, Issue 3, 1 July 2018, Pages 4142–4151, https://doi.org/10.1093/mnras/sty965
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87284
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:21 Jun 2018 03:34
Last Modified:03 Oct 2019 19:54

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