Going Forward with the Nancy Grace Roman Space Telescope Transient Survey: Validation of Precision Forward-modeling Photometry for Undersampled Imaging. (28th June 2021)

• Record Type: Journal Article
• Authors: Rubin, David; Cikota, Aleksandar; Aldering, Greg; Fruchter, Andy; Perlmutter, Saul; Sako, Masao
• Is Part Of: Publications of the Astronomical Society of the Pacific. Volume 133:Number 1024(2021)
• Publication Date: 2021-06-28
• Subjects: Astronomy -- Periodicals; Astronomy; Periodicals; Periodicals; 520.5
• DOI: 10.1088/1538-3873/abf406
• Languages: English
• ISSNs: 0004-6280

Abstract

Abstract: The Nancy Grace Roman Space Telescope (Roman) is an observatory for both wide-field observations and coronagraphy that is scheduled for launch in the mid-2020s. Part of the planned survey is a deep, cadenced field or fields that enable cosmological measurements with type Ia supernovae (SNe Ia). With a pixel scale of 0.″11, the Wide Field Instrument will be undersampled, presenting a difficulty for precisely subtracting the galaxy light underneath the SNe. We use simulated data to validate the ability of a forward-model code (such codes are frequently also called "scene-modeling" codes) to perform precision supernova photometry for the Roman SN survey. Our simulation includes over 760, 000 image cutouts around SNe Ia or host galaxies (∼10% of a full-scale survey). To have a realistic 2D distribution of underlying galaxy light, we use the VELA simulated high-resolution images of galaxies. We run each set of cutouts through our forward-modeling code which automatically measures time-dependent SN fluxes. Given our assumed inputs of a perfect model of the instrument point-spread functions and calibration, we find biases at the millimagnitude level from this method in four red filters ( Y 106, J 129, H 158, and F 184), easily meeting the 0.5% Roman inter-filter calibration requirement for a cutting-edge measurement of cosmological parameters using SNe Ia. Simulated data in the bluer Z 087 filter shows larger ∼ 2–3 mmg biases, also meeting this requirement, but with more room for improvement. Our forward-model code has been released on Zenodo.