Probabilistic probes for galaxy evolution: signatures of AGN feedback in the AION foundation model

AION-1 is a multimodal foundation model for astrophysics pretrained on optical and infrared datasets, but not on X-ray observations, which crucially inform black hole accretion physics. We use AION's frozen encoder to measure the amount of information that the resulting representation carries about X-ray phenomena, and use the results to explore scaling relations between black hole accretion, gas dynamics, and star formation rate. A trained CLS token pools information throughout the frozen forward pass, and normalizing-flow heads on that summary yield posteriors over X-ray properties from optical spectra, images, mid-infrared photometry and redshift. The four-modality model outperforms a classical emission-line baseline on every attempted target (flux information gain 0.299 vs.\ 0.197 nats). Using modality dropout we also measure the information contained in each of the 15 input combinations. A joint posterior over the X-ray band rates, star formation rate and stellar mass yields composite targets the model was not explicitly trained on: specific star formation rate and a hardness ratio. The within-object posterior correlation between those two properties is studied as a potential tracer of active galactic nucleus (AGN) feedback as a mechanism for quenching star formation in galaxies.