FORECASTOR -- II. Simulating Galaxy Surveys with the Cosmological Advanced Survey Telescope for Optical and UV Research
Madeline A. Marshall, Laurie Amen, Tyrone E. Woods, Patrick J. Cote,, L. Y. Aaron Yung, Melissa Amenouche, Emily K. Pass, Michael Balogh, Samir, Salim, and Thibaud Moutard

TL;DR
This paper introduces a simulation tool for the CASTOR space telescope, predicting survey performance and galaxy detection capabilities to aid in planning and optimizing its scientific output.
Contribution
It presents a novel Python-based image simulator for CASTOR, enabling detailed performance predictions and survey design optimization before launch.
Findings
CASTOR will achieve 75% completeness for point sources down to ~27-30 mag.
Wide survey will detect hundreds of millions of galaxies up to z~4.
Ultra-Deep survey will probe galaxies up to z~5 with stellar masses >10^7 M_sun.
Abstract
The Cosmological Advanced Survey Telescope for Optical and UV Research (CASTOR) is a planned flagship space telescope, covering the blue-optical and UV part of the spectrum. Here we introduce the CASTOR image simulator, a Python GalSim package-based script capable of generating mock CASTOR images from an input catalogue. We generate example images from the CASTOR Wide, Deep, and Ultra-Deep surveys using simulated light-cones from the Santa Cruz Semi-Analytic Model. We make predictions for the performance of these surveys by comparing galaxies that are extracted from each image using Source Extractor to the input catalogue. We find that the Wide, Deep, and Ultra-Deep surveys will be 75% complete for point sources down to ~27, 29 and 30 mag, respectively, in the UV, u, and g filters, with the UV-split and u-split filters reaching a shallower depth. With a large area of ~2200 deg, the…
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Taxonomy
TopicsHistory and Developments in Astronomy · Astronomy and Astrophysical Research · Astronomical Observations and Instrumentation
