How bright can a galaxy be? Extreme value predictions for high-redshift survey observables
Abstract
Assessing the significance of extreme galaxy candidates has typically relied on redshifts and stellar / halo masses inferred through inverse modelling, which carries substantial systematic uncertainty. We combine the halo mass function with a conditional model for galaxy emission and apply the Extreme Value Statistics (EVS) formalism to predict the distribution of the observed flux of the brightest galaxy expected in a given survey area, redshift interval and photometric band. We find the choice of parametric star formation history (SFH) dominates the modelling uncertainty, introducing a spread approaching ∼ 1 dex by z ∼ 18, whilst the stellar population synthesis model, initial mass function and metallicity each contribute ≲ 0.3 dex. The luminous z ≳ 9 candidates in COSMOS-Web cluster about the median prediction for the area they were drawn from, and lie at least an order of magnitude below the 3σ limit, a margin comparable to the SFH systematic. Of the observational samples reaching beyond z ∼ 13, the two spectroscopically confirmed galaxies at z ≃ 14 lie close to the median prediction for their area, whereas photometric candidates at z ≳ 16 reach +4σ (a probability to exceed of ∼ 10−5), the brightest lying at the limit obtained by converting all halo baryons into stars. Since the prediction bounds the flux far more tightly than the photometry bounds the redshift, such offsets are most naturally resolved by errors in the inferred redshifts. This approach allows extreme candidates to be assessed directly in the observational plane, making explicit the modelling assumptions common to forward and inverse approaches.
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BibTeX
@misc{lovell2026bright,
title = {{How bright can a galaxy be? Extreme value predictions for high-redshift survey observables}},
author = {Christopher C. Lovell and Theodore Martin and Sandro Tacchella and Christian K. Jespersen and Ian Harrison},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23016},
eprint = {2610.23016},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23016},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - How bright can a galaxy be? Extreme value predictions for high-redshift survey observables
AU - Christopher C. Lovell
AU - Theodore Martin
AU - Sandro Tacchella
AU - Christian K. Jespersen
AU - Ian Harrison
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.23016
UR - https://arxiv.org/abs/2610.23016
AB - Assessing the significance of extreme galaxy candidates has typically relied on redshifts and stellar / halo masses inferred through inverse modelling, which carries substantial systematic uncertainty. We combine the halo mass function with a conditional model for galaxy emission and apply the Extreme Value Statistics (EVS) formalism to predict the distribution of the observed flux of the brightest galaxy expected in a given survey area, redshift interval and photometric band. We find the choice of parametric star formation history (SFH) dominates the modelling uncertainty, introducing a spread approaching ∼ 1 dex by z ∼ 18, whilst the stellar population synthesis model, initial mass function and metallicity each contribute ≲ 0.3 dex. The luminous z ≳ 9 candidates in COSMOS-Web cluster about the median prediction for the area they were drawn from, and lie at least an order of magnitude below the 3σ limit, a margin comparable to the SFH systematic. Of the observational samples reaching beyond z ∼ 13, the two spectroscopically confirmed galaxies at z ≃ 14 lie close to the median prediction for their area, whereas photometric candidates at z ≳ 16 reach +4σ (a probability to exceed of ∼ 10^−5), the brightest lying at the limit obtained by converting all halo baryons into stars. Since the prediction bounds the flux far more tightly than the photometry bounds the redshift, such offsets are most naturally resolved by errors in the inferred redshifts. This approach allows extreme candidates to be assessed directly in the observational plane, making explicit the modelling assumptions common to forward and inverse approaches.
ER -