Classifying Supermassive Black Hole Growth Regimes to Observables Across Cosmological Simulations with Forecasts for LSST

Hitaishi Chillara

First listed 2026-04-16 | Last updated 2026-04-16

Abstract

The possibility of over-massive black holes suggested by James Webb Space Telescope photometric discoveries of 'little red dots', may disfavor light supermassive black hole (SMBH) seeds. However, what should constitute the mass (range) of 'heavy' seeds remains relatively unconstrained. Moreover, Vera C Rubin Observatory's Legacy Survey of Space and Time will photometrically characterize galaxies without direct black hole mass measurements. We forward-model the SIMBA, IllustrisTNG, and EAGLE cosmological simulations into the photometric bands of LSST to train an ensemble machine learning classifier. Our framework achieves $91\%$--$94\%$ accuracy across SIMBA and IllustrisTNG in distinguishing between over-massive and under-massive SMBH growth regimes under LSST magnitude limits, using only broadband photometry. Furthermore, cross-simulation transfer experiments (training on one cosmological simulation and evaluating on another using rank-normalized features) achieve $83\%$--$89\%$ accuracy. This suggests the relative photometric ordering of growth regimes is largely preserved even across fundamentally different sub-grid SMBH feedback prescriptions. Signal decomposition shows our classification is driven by host galaxy colors ($82\%$--$87\%$ accuracy) and, relatedly, the accretion-state's spectral energy distribution shape as opposed to an inversion of our forward model's analytical luminosity prescription. Given that the evaluated simulations employ heavy seed prescriptions ($\geq 10^{4}~M_\odot$), our methodology establishes a validated baseline for classifying post-seeding growth regimes.

Short digest

This paper connects black-hole growth regimes in cosmological simulations to observables that LSST and JWST-like surveys can measure. The main result is that different simulations produce distinct black-hole-to-host scaling behavior and number-density evolution, with the over-massive branch most relevant for interpreting little red dots and other early AGN. The paper matters because it frames the current LRD discussion inside a broader simulation-based forecast rather than treating the sources as an isolated anomaly.

Key figures to inspect

• Figure 2 is the must-see overview of the Simba black-hole population, because it combines the mass function, the black-hole-to-host relation, and the number-density evolution in one place.
• Figure 4 is the most directly interpretable comparison figure, since it sets the Simba and IllustrisTNG median relations against the Trinity empirical model and the JWST AGN points.
• Figure 3 is where to inspect the Simba scaling relations themselves and compare them against the overlaid local and TNG trends.
• Figure 1 is mainly a methodology sanity check, showing that the removal of label-flip objects is not driving the classification by redshift or by a pathological boundary effect.