Diverse Metallicities in Protoclusters at z ~ 3.4 detected by Deep Learning

Abstract We present spectroscopic follow-up observations with Subaru/MOIRCS of four protocluster candidate regions identified by the deep-learning model PCFNet. In three candidate regions, we confirm four protoclusters at z = 3.316, 3.403, 3.408 and 3.425, exhibiting galaxy density peaks over five times the uniform distribution. The detection rate of confirmed members aligns with PCFNet’s predictions, underscoring its effectiveness. Analysis of spectral energy distributions (SEDs), incorporating up to ten photometric bands (uu*grizyJHK), reveals that protocluster members lie on the star-formation main sequence. The masses of the most massive halos in the protoclusters are estimated from the most massive stellar mass of the member galaxies, yielding log Mhalo/M⊙ = 12.4–12.8, which are consistent with those at this redshift predicted by cosmological simulations and are comparable to those of the progenitors of typical local clusters ($M_\mathrm{halo}^{z=0}\sim 10^{14}M_\odot$). Using the Voronoi Tessellation Monte Carlo technique based on the probability of protocluster membership from PCFNet, we find that our protoclusters exhibit a significantly higher SFRD than the cosmic SFRD at z ~ 3.4. Based on the [O iii]/Hβ emission line ratio, two protoclusters show marginally (1.4–2σ) higher average metallicities than the field, while the other protoclusters are statistically consistent with the field. These diverse metallicities highlight the environmental impact within protoclusters detected by PCFNet, likely reflecting the complex opposing interplay between metal-rich gas recycling and metal-poor gas inflow, which may depend on the surrounding environment of each protocluster. This work demonstrates PCFNet’s capability to unveil diverse samples of high-redshift protoclusters, paving the way for a more comprehensive understanding of their role in galaxy evolution.

Authors

Institutions

Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2026-11-06
DOI
https://doi.org/10.1093/mnras/stag2110
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Diverse Metallicities in Protoclusters at z ~ 3.4 detected by Deep Learning

Rieko Momose, Satoshi Kikuta, Jun Toshikawa, Shunta Shimizu et al.
3 links
Monthly Notices of the Royal Astronomical Society
Galaxies: Formation, Evolution, Phenomena
article

Diverse Metallicities in Protoclusters at z ~ 3.4 detected by Deep Learning

Rieko Momose, Satoshi Kikuta, Jun Toshikawa, Shunta Shimizu, Hisakazu Uchiyama, Yoshihiro Takeda, T. Yoshioka, Junya Arita, Ryo Emori, Kentaro Koretomo, Hiroki Hoshi, Kei Ito, Nobunari Kashikawa, Yongming Liang, Mariko Kubo
article en

Abstract

Abstract We present spectroscopic follow-up observations with Subaru/MOIRCS of four protocluster candidate regions identified by the deep-learning model PCFNet. In three candidate regions, we confirm four protoclusters at z = 3.316, 3.403, 3.408 and 3.425, exhibiting galaxy density peaks over five times the uniform distribution. The detection rate of confirmed members aligns with PCFNet’s predictions, underscoring its effectiveness. Analysis of spectral energy distributions (SEDs), incorporating up to ten photometric bands (uu*grizyJHK), reveals that protocluster members lie on the star-formation main sequence. The masses of the most massive halos in the protoclusters are estimated from the most massive stellar mass of the member galaxies, yielding log Mhalo/M⊙ = 12.4–12.8, which are consistent with those at this redshift predicted by cosmological simulations and are comparable to those of the progenitors of typical local clusters ($M_\mathrm{halo}^{z=0}\sim 10^{14}M_\odot$). Using the Voronoi Tessellation Monte Carlo technique based on the probability of protocluster membership from PCFNet, we find that our protoclusters exhibit a significantly higher SFRD than the cosmic SFRD at z ~ 3.4. Based on the [O iii]/Hβ emission line ratio, two protoclusters show marginally (1.4–2σ) higher average metallicities than the field, while the other protoclusters are statistically consistent with the field. These diverse metallicities highlight the environmental impact within protoclusters detected by PCFNet, likely reflecting the complex opposing interplay between metal-rich gas recycling and metal-poor gas inflow, which may depend on the surrounding environment of each protocluster. This work demonstrates PCFNet’s capability to unveil diverse samples of high-redshift protoclusters, paving the way for a more comprehensive understanding of their role in galaxy evolution.

Monthly Notices of the Royal Astronomical Society
University of Hyogo (JP), Carnegie Institution for Science (US), Tohoku University (JP), Hosei University (JP), Kwansei Gakuin University (JP), Carnegie Observatories (US), National Astronomical Observatory of Japan (JP), Institute for Cosmic Ray Research (JP), DTU Space (DK), Nishi-Harima Astronomical Observatory (JP), Research Center for the Early Universe (JP), The University of Tokyo (JP), Technical University of Denmark (DK)
National Science Foundation, National Aeronautics and Space Administration, Gordon and Betty Moore Foundation, Princeton University, Johns Hopkins University, Space Telescope Science Institute, Queen's University Belfast, Durham University, Ministry of Education, Culture, Sports, Science and Technology, Academia Sinica, Cabinet Office, Government of Japan, Max-Planck-Gesellschaft, National Central University, National Astronomical Observatory of Japan, High Energy Accelerator Research Organization, Eötvös Loránd Tudományegyetem, Max-Planck-Institut für Astronomie, Toray Science Foundation, Smithsonian Astrophysical Observatory, Science Mission Directorate, Japan Society for the Promotion of Science, Japan Science and Technology Agency, Planetary Science Division, Los Alamos National Laboratory
Openalex Percentile: Top 36%
Galaxies: Formation, Evolution, Phenomena
DOIOpen Access PDFOpenAlex
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.

Laika access coming soon