The University of Southampton
University of Southampton Institutional Repository

First cosmology results using Type Ia Supernovae from the Dark Energy Survey: Photometric pipeline and light-curve data release

First cosmology results using Type Ia Supernovae from the Dark Energy Survey: Photometric pipeline and light-curve data release
First cosmology results using Type Ia Supernovae from the Dark Energy Survey: Photometric pipeline and light-curve data release
We present griz light curves of 251 SNe Ia from the first 3 years of the Dark Energy Survey Supernova Program's (DES-SN) spectroscopically classified sample. The photometric pipeline described in this paper produces the calibrated fluxes and associated uncertainties used in the cosmological parameter analysis by employing a scene modeling approach that simultaneously models a variable transient flux and temporally constant host galaxy. We inject artificial point sources onto DECam images to test the accuracy of our photometric method. Upon comparison of input and measured artificial supernova fluxes, we find that flux biases peak at 3 mmag. We require corrections to our photometric uncertainties as a function of host galaxy surface brightness at the transient location, similar to that seen by the DES Difference Imaging Pipeline used to discover transients. The public release of the light curves can be found at https://des.ncsa.illinois.edu/releases/sn.
0004-637X
Sullivan, Mark
2f31f9fa-8e79-4b35-98e2-0cb38f503850
Smith, Mathew
8bdc74e1-a37b-434d-ae75-82763109bf7a
Childress, Michael
7d0e608c-b9de-4631-bab5-7a2b810a0a2b
The DES Collaboration
Sullivan, Mark
2f31f9fa-8e79-4b35-98e2-0cb38f503850
Smith, Mathew
8bdc74e1-a37b-434d-ae75-82763109bf7a
Childress, Michael
7d0e608c-b9de-4631-bab5-7a2b810a0a2b

Sullivan, Mark, Smith, Mathew and Childress, Michael , The DES Collaboration (2019) First cosmology results using Type Ia Supernovae from the Dark Energy Survey: Photometric pipeline and light-curve data release. The Astrophysical Journal, 874, [106]. (doi:10.3847/1538-4357/ab06c1).

Record type: Article

Abstract

We present griz light curves of 251 SNe Ia from the first 3 years of the Dark Energy Survey Supernova Program's (DES-SN) spectroscopically classified sample. The photometric pipeline described in this paper produces the calibrated fluxes and associated uncertainties used in the cosmological parameter analysis by employing a scene modeling approach that simultaneously models a variable transient flux and temporally constant host galaxy. We inject artificial point sources onto DECam images to test the accuracy of our photometric method. Upon comparison of input and measured artificial supernova fluxes, we find that flux biases peak at 3 mmag. We require corrections to our photometric uncertainties as a function of host galaxy surface brightness at the transient location, similar to that seen by the DES Difference Imaging Pipeline used to discover transients. The public release of the light curves can be found at https://des.ncsa.illinois.edu/releases/sn.

Text
1811.02379 - Accepted Manuscript
Download (1MB)

More information

Accepted/In Press date: 11 February 2019
e-pub ahead of print date: 27 March 2019
Published date: March 2019

Identifiers

Local EPrints ID: 429876
URI: http://eprints.soton.ac.uk/id/eprint/429876
ISSN: 0004-637X
PURE UUID: fc792605-89cf-4a6f-ba97-eab6a260542e
ORCID for Mark Sullivan: ORCID iD orcid.org/0000-0001-9053-4820
ORCID for Mathew Smith: ORCID iD orcid.org/0000-0002-3321-1432

Catalogue record

Date deposited: 08 Apr 2019 16:30
Last modified: 16 Mar 2024 04:19

Export record

Altmetrics

Contributors

Author: Mark Sullivan ORCID iD
Author: Mathew Smith ORCID iD
Author: Michael Childress
Corporate Author: The DES Collaboration

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×