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We present a random forest framework for predicting circumgalactic medium(CGM) physical conditions from quasar absorption line observables, trained on asample of Voigt profile-fit synthetic absorbers from the Simba cosmologicalsimulation. Traditionally, extracting physical conditions from CGM absorberobservations involves simplifying assumptions such as uniform single-phaseclouds, but by using a cosmological simulation we bypass such assumptions tobetter capture the complex relationship between CGM observables and underlyinggas conditions. We train random forest models on synthetic spectra for HI andselected metal lines around galaxies across a range of star formation rates,stellar masses, and impact parameters, to predict absorber overdensities,temperatures, and metallicities. The models reproduce the true values fromSimba well, with normalised transverse standard deviations of $0.50-0.54$ dexin overdensity, $0.32-0.54$ dex in temperature, and $0.49-0.53$ dex inmetallicity predicted from metal lines (not HI), across all ions. Examining thefeature importance, the random forest indicates that the overdensity is mostinformed by the absorber column density, the temperature is driven by the linewidth, and the metallicity is most sensitive to the specific star formationrate. Alternatively examining feature importance by removing one observable ata time, the overdensity and metallicity appear to be more driven by the impactparameter. We introduce a normalising flow approach in order to ensure thescatter in the true physical conditions is accurately spanned by the network.The trained models are available online.

Mapping Circumgalactic Medium Observations to Theory Using Machine Learning