High-redshift galaxy compilation

Data
Author
Affiliation

Hollis Akins

UT Austin

Published

March 14, 2025

Modified

October 1, 2025

This page maintains an up-to-date (as of October 1, 2025) compilation of very high-redshift (\(z>10\)) galaxies spectroscopically-confirmed with JWST. The table below includes key properties of these galaxies, as reported in their respective discovery papers, including their redshifts, UV magnitudes, stellar masses, UV slopes, and more. References are provided numerically. The table is dynamically generated in the code block below, and automatically exported to various formats for further use in analysis or publications.

Show the code 
from IPython.display import Markdown
from tabulate import tabulate
from astropy.table import Table

table = Table(names=('ID', 'redshift', 'Muv', 'beta', 'logMstar', 'reference'),
              dtype=(str, float, float, float, float, str),
              units=(None,  None, 'mag', None, 'Msun', None))

table.add_row(dict(
   ID="Maise's", 
   redshift=11.44, 
   Muv=-20.2, 
   beta=-2.47,
   logMstar=8.4,
   reference='@finkelstein2022c, @arrabalharo2023b'
))
table.add_row(dict(
   ID='GHZ2', 
   redshift=12.34, 
   Muv=-20.5, 
   beta=-2.39,
   logMstar=9.05,
   reference='@castellano2024, @zavala2025'
))
table.add_row(dict(
   ID='GN-z11', 
   redshift=10.60, 
   Muv=-12.5, 
   beta=-2.41,
   logMstar=9.1,
   reference='@oesch2016, @bunker2023, @tacchella2023b'
))
table.add_row(dict(
   ID='GS-z14-0', 
   redshift=14.32, 
   Muv=-20.8, 
   beta=-2.2,
   logMstar=8.7,
   reference='@carniani2024'
))
table.add_row(dict(
   ID='GS-z14-1', 
   redshift=13.90, 
   Muv=-19.0, 
   beta=-2.71,
   logMstar=8.0,
   reference='@carniani2024'
))
table.add_row(dict(
   ID='GS-z10-0', 
   redshift=10.37, 
   Muv=-18.6, 
   beta=-2.49,
   logMstar=7.58,
   reference='@curtislake2023'
))
table.add_row(dict(
   ID='GS-z11-0', 
   redshift=11.48, 
   Muv=-19.3, 
   beta=-2.18,
   logMstar=8.67,
   reference='@curtislake2023'
))
table.add_row(dict(
   ID='GS-z12-0', 
   redshift=12.48, 
   Muv=-18.2, 
   beta=-1.84,
   logMstar=7.64,
   reference='@curtislake2023, @deugenio2024b'
))
table.add_row(dict(
   ID='GS-z13-0', 
   redshift=13.17, 
   Muv=-18.7, 
   beta=-2.37,
   logMstar=7.95,
   reference='@curtislake2023'
))


Markdown(tabulate(table, 
   headers=('Name', 'Redshift', r'$M_{\rm UV}$', r'$\beta$', r'$\log_{10} M_*$', 'Reference'), 
   colalign=("left", "decimal", "decimal", "decimal", "decimal", "center"),
   floatfmt=".2f"
))
Name Redshift \(M_{\rm UV}\) \(\beta\) \(\log_{10} M_*\) Reference
Maise’s 11.44 -20.20 -2.47 8.40 (1), (2)
GHZ2 12.34 -20.50 -2.39 9.05 (3), (4)
GN-z11 10.60 -12.50 -2.41 9.10 (5), (6), (7)
GS-z14-0 14.32 -20.80 -2.20 8.70 (8)
GS-z14-1 13.90 -19.00 -2.71 8.00 (8)
GS-z10-0 10.37 -18.60 -2.49 7.58 (9)
GS-z11-0 11.48 -19.30 -2.18 8.67 (9)
GS-z12-0 12.48 -18.20 -1.84 7.64 (9), (10)
GS-z13-0 13.17 -18.70 -2.37 7.95 (9)

You can copy the above python code directly into your scripts/notebooks for easy analysis. You can also find table in the following formats: TeX, FITS, ASCII. To update the compilation, please see the instructions for contribution.

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