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  5. A Synthetic Roman Space Telescope High-Latitude Time-Domain Survey: Supernovae in the Deep Field

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Preprint
en
2022

A Synthetic Roman Space Telescope High-Latitude Time-Domain Survey: Supernovae in the Deep Field

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0 Files

en
2022
DOI: 10.48550/arxiv.2204.13553arxiv.org/abs/2204.13553

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Alexei V Filippenko
Alexei V Filippenko

University of California, Berkeley

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Kevin X. Wang
D. Scolnic
M. A. Troxel
+14 more

Abstract

NASA will launch the Nancy Grace Roman Space Telescope (Roman) in the second half of this decade, which will allow for a generation-defining measurement of dark energy through multiple probes, including Type Ia supernovae (SNe Ia). To improve decisions on survey strategy, we have created the first simulations of realistic Roman images that include artificial SNe Ia injected as point sources in the images. Our analysis combines work done on Roman simulations for weak gravitational lensing studies as well as catalog-level simulations of SN samples. We have created a time series of images over two years containing $\sim$ 1,050 SNe Ia, covering a 1 square degree subarea of a planned 5 square degree deep survey. We have released these images publicly for community use along with input catalogs of all injected sources. We create secondary products from these images by generating coadded images and demonstrating recovery of transient sources using image subtraction. We perform first-use analyses on these images in order to measure galaxy-detection efficiency, point source-detection efficiency, and host-galaxy association biases. The simulated images can be found here: https://roman.ipac.caltech.edu/sims/SN_Survey_Image_sim.html.

How to cite this publication

Kevin X. Wang, D. Scolnic, M. A. Troxel, S. Rodney, B Popovic, Caleb Duff, Alexei V Filippenko, R. J. Foley, Rebekah Hounsell, Saurabh W. Jha, D. O. Jones, Bhavin Joshi, Heyang Long, Phillip Macias, Adam G. Riess, Benjamin Rose, M. Yamamoto (2022). A Synthetic Roman Space Telescope High-Latitude Time-Domain Survey: Supernovae in the Deep Field. , DOI: https://doi.org/10.48550/arxiv.2204.13553.

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Publication Details

Type

Preprint

Year

2022

Authors

17

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.48550/arxiv.2204.13553

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