ULTIMATE deblending I. A 50-band UV-to-MIR photometric catalog combining space- and ground-based data in the JWST/PRIMER survey
Hanwen Sun, Tao Wang, Ke Xu, David Elbaz, Emiliano Merlin, Cheng Cheng, Emanuele Daddi, Shuowen Jin, Wei-hao Wang, Longyue Chen, Adriano Fontana, Zhen-Kai Gao, Jiasheng Huang, Benjamin Magnelli, Valentina Sangalli, Yijun Wang, Tiancheng Yang, Yuheng Zhang, and Luwenjia Zhou

TL;DR
This paper presents a comprehensive 50-band UV-to-MIR photometric catalog from the JWST/PRIMER survey, improving galaxy property measurements and redshift accuracy through advanced deblending and data integration.
Contribution
It introduces a new self-consistent UV-to-Radio photometric catalog combining space- and ground-based data, enhancing galaxy analysis in the early universe.
Findings
Including ground-based deblended photometry improves redshift accuracy by ~40%.
The catalog covers 627.1 arcmin^2 across two JWST fields.
Publicly released UV-to-MIR data serve as a reference for galaxy evolution studies.
Abstract
Our understanding of the early Universe has long been limited by biased galaxy samples selected through various color criteria. With deep JWST infrared imaging, mass-complete galaxy samples can now be studied up to for the first time. However, recent work has revealed systematic uncertainties in measuring physical properties of galaxies based solely on JWST/NIRCam and HST photometry, due to their limited wavelength coverage. This highlights the need for supplementary data, particularly in the rest-frame UV and near-infrared. Here we present the ULTIMATE-deblending project, which will eventually deliver self-consistent UV-to-Radio photometry for galaxies detected in deep JWST surveys, including both NIRCam and MIRI data. In this first paper, we release a 50-band photometric catalog spanning CFHT/U to JWST/MIRI F1800W, covering a total of 627.1 arcmin across two JWST/PRIMER…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
