The HST See Change Program: I. Survey Design, Pipeline, and Supernova Discoveries
Brian Hayden, David Rubin, Kyle Boone, Greg Aldering, Jakob Nordin, Mark Brodwin, Susana Deustua, Sam Dixon, Parker Fagrelius, Andy Fruchter, Peter Eisenhardt, Anthony Gonzalez, Ravi Gupta, Isobel Hook, Chris Lidman, Kyle Luther, Adam Muzzin, Zachary Raha, Pilar Ruiz-Lapuente, Clare Saunders, Caroline Sofiatti, Adam Stanford, Nao Suzuki, Tracy Webb, Steven C. Williams, Gillian Wilson, Mike Yen, Rahman Amanullah, Kyle Barbary, Hans Bohringer, Greta Chappell, Carlos Cunha, Miles Currie, Rene Fassbender, Michael Gladders, Ariel Goobar, Hendrik Hildenrandt, Henk Hoekstra, Xiaosheng Huang, Dragan Huterer, M. James Jee, Alex Kim, Marek Kowalski, Eric Linder, Joshua E. Meyers, Reynald Pain, Saul Perlmutter, Johan Richard, Piero Rosati, Eduardo Rozo, Eli Rykoff, Joana Santos, Anthony Spadafora, Daniel Stern, Risa Wechsler, The Supernova Cosmology Project
The See Change survey was designed to make z>1 cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measurements through weak lensing. This survey observed twelve galaxy clusters with the Hubble Space Telescope spanning the redshift range z=1.13 to 1.75, discovering 57 likely transients and 27 likely SNe Ia at z∼0.8−2.3. As in similar previous surveys (Dawson et al. 2009), this proved to be a highly efficient use of HST for SN observations; the See Change survey additionally tested the feasibility of maintaining, or further increasing, the efficiency at yet higher redshifts, where we have less detailed information on the expected cluster masses and star-formation rates. We find that the resulting number of SNe Ia per orbit is a factor of ∼8 higher than for a field search, and 45% of our orbits contained an active SN Ia within 22 rest-frame days of peak, with one of the clusters by itself yielding 6 of the SNe Ia. We present the survey design, pipeline, and SN discoveries. Novel features include fully blinded SN searches, the first random forest candidate classifier for undersampled IR data (with a 50% detection threshold within 0.05 magnitudes of human searchers), real-time forward-modeling photometry of candidates, and semi-automated photometric classifications and follow-up forecasts. We also describe the spectroscopic follow-up, instrumental in measuring host-galaxy redshifts. The cosmology analysis of our sample will be presented in a companion paper.
Comments: ApJ preprint
Subjects: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
Cite as: arXiv:2103.13285 [astro-ph.CO] (or arXiv:2103.13285v1 [astro-ph.CO] for this version)
Submission history
From: Brian Hayden
[v1] Wed, 24 Mar 2021 15:56:10 UTC (1,431 KB)
