Efficient Optimal Image Reconstruction for the Nancy Grace Roman Space Telescope and Beyond: II. Implementation of Effortless

arXiv:2607.09862 · astro-ph.IM, astro-ph.CO · Submitted 2026-07-10 · Read on arXiv

Kaili Cao

astro-ph.IM, astro-ph.CO

Submitted: 2026-07-10

Comments: 28 pages, 15 figures, submitted to ApJS

Code: https://github.com/Roman-HLIS-Cosmology-PIT/effortless

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 21× in this paper · explained in 7 episodes
• Weak Lensing — 10× in this paper · explained in 2 episodes
• Gravitational Lensing — 6× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Roman High Latitude Imaging Survey — 3× in this paper
• Aperture — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Roman Project Infrastructure Team — 2× in this paper
• Trace — 2× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Legacy Survey of Space and Time — 1× in this paper

Sources

• The Wide Field Infrared Survey Telescope: 100 Hubbles for the 2020s
• Report of the Dark Energy Task Force
• Efficient Optimal Image Reconstruction for the Nancy Grace Roman Space Telescope and Beyond: I. First Results with Effortless
• Fisher Forecasts for Cosmological Yields from 3! times!2 pt Analysis of the Roman Space Telescope High Latitude Imaging Survey
• Dark Energy Survey Year 6 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing
• Dark Energy Survey Year 6 Results: Cosmological Constraints from Cosmic Shear
• Cosmology from weak lensing, galaxy clustering, CMB lensing and tSZ: II. Optimizing Roman survey design for CMB cross-correlation science
• Metacalibration: Direct Self-Calibration of Biases in Shear Measurement
• Euclid Definition Study Report
• Large Synoptic Survey Telescope: Dark Energy Science Collaboration
• Roman Observations Time Allocation Committee: Final Report and Recommendations
• Scikit-learn: Machine Learning in Python

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