The Hourglass Simulation: A Catalog for the Roman High-latitude Time-domain Core Community Survey

The Hourglass Simulation: A Catalog for the Roman High-latitude Time-domain Core Community Survey

We present a simulation of the time-domain catalog for the Nancy Grace Roman Space Telescope’s High-Latitude Time-Domain Core Community Survey. This simulation, called the Hourglass simulation, uses the most up-to-date spectral energy distribution models and rate measurements for 10 extragalactic ti...

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Bibliographic Details
Main Authors: B. M. Rose, M. Vincenzi, R. Hounsell, H. Qu, L. Aldoroty, D. Scolnic, R. Kessler, P. Macias, D. Brout, M. Acevedo, R. C. Chen, S. Gomez, E. Peterson, D. Rubin, M. Sako, the Roman Supernova Project Infrastructure Team
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:The Astrophysical Journal
Subjects:
Surveys
Catalogs
Time domain astronomy
Space telescopes
Astronomical simulations
Online Access:https://doi.org/10.3847/1538-4357/ade1d6
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Summary:We present a simulation of the time-domain catalog for the Nancy Grace Roman Space Telescope’s High-Latitude Time-Domain Core Community Survey. This simulation, called the Hourglass simulation, uses the most up-to-date spectral energy distribution models and rate measurements for 10 extragalactic time-domain sources. We simulate these models through the design reference Roman Space Telescope survey: four filters per tier, a five-day cadence, over 2 yr, a wide tier of 19 deg ^2 , and a deep tier of 4.2 deg ^2 , with ∼20% of those areas also covered with prism observations. We find that a science-independent Roman time-domain catalog, assuming a signal-to-noise ratio at a max of >5, would have approximately 21,000 Type Ia supernovae, 40,000 core-collapse supernovae, around 70 superluminous supernovae, ∼35 tidal disruption events, three kilonovae, and possibly pair-instability supernovae. In total, Hourglass has over 64,000 transient objects, 11,000,000 photometric observations, and 500,000 spectra. Additionally, Hourglass is a useful data set to train machine learning classification algorithms. We show that SCONE is able to photometrically classify Type Ia supernovae with high precision (∼95%) to a z > 2. Finally, we present the first realistic simulations of non-Type Ia supernovae spectral time series data from Roman’s prism.
ISSN:1538-4357

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