Linear Image Regridding and Coaddition with Oversampled Point Spread Functions: Lessons from 1D

Abstract Image regridding and coaddition have a wide range of applications in astronomical observations. Image Combination (I mcom) , an algorithm that provides control over point-spread function (PSF) and noise in coadded images, has been found to meet the stringent requirements of weak gravitational lensing cosmology with the forthcoming Nancy Grace Roman Space Telescope. In this work, I introduce a new algorithm, Fast I mcom , which outperforms traditional I mcom in terms of both efficiency and quality. After explaining the underlying philosophy and mathematical formalism, I conduct systematic comparisons between I mcom and Fast I mcom in terms of PSF reconstruction in 1D. While a 2D implementation is beyond the scope of this paper, I demonstrate how to generalize Fast I mcom to 2D and discuss practical issues involved. This new algorithm has the potential of reducing both the computational costs and storage requirements (current estimates are ∼100 M core-hours and ∼1.5 PB, respectively) of the Roman High Latitude Imaging Survey (HLIS) by an order of magnitude. Meanwhile, it provides implications for the dithering patterns of Roman surveys (extrapolated from 1D to 2D). I also address potential applications of Fast I mcom beyond the Roman HLIS, with focus on other weak lensing programs and Roman time domain surveys; the actual range of use cases is likely beyond what is discussed here.

Authors

Publication Details

Journal
The Astronomical Journal
Published
2026-02-09
DOI
https://doi.org/10.3847/1538-3881/ae356a
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Linear Image Regridding and Coaddition with Oversampled Point Spread Functions: Lessons from 1D

Kaili Cao, Roman HLIS Cosmology PIT
The Astronomical Journal
Galaxies: Formation, Evolution, Phenomena
article

Linear Image Regridding and Coaddition with Oversampled Point Spread Functions: Lessons from 1D

Kaili Cao, Roman HLIS Cosmology PIT
article en

Abstract

Abstract Image regridding and coaddition have a wide range of applications in astronomical observations. Image Combination (I mcom) , an algorithm that provides control over point-spread function (PSF) and noise in coadded images, has been found to meet the stringent requirements of weak gravitational lensing cosmology with the forthcoming Nancy Grace Roman Space Telescope. In this work, I introduce a new algorithm, Fast I mcom , which outperforms traditional I mcom in terms of both efficiency and quality. After explaining the underlying philosophy and mathematical formalism, I conduct systematic comparisons between I mcom and Fast I mcom in terms of PSF reconstruction in 1D. While a 2D implementation is beyond the scope of this paper, I demonstrate how to generalize Fast I mcom to 2D and discuss practical issues involved. This new algorithm has the potential of reducing both the computational costs and storage requirements (current estimates are ∼100 M core-hours and ∼1.5 PB, respectively) of the Roman High Latitude Imaging Survey (HLIS) by an order of magnitude. Meanwhile, it provides implications for the dithering patterns of Roman surveys (extrapolated from 1D to 2D). I also address potential applications of Fast I mcom beyond the Roman HLIS, with focus on other weak lensing programs and Roman time domain surveys; the actual range of use cases is likely beyond what is discussed here.

The Astronomical JournalVol. 171(3)
Openalex Percentile: Top 59%
Galaxies: Formation, Evolution, Phenomena
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.