Dark from light (DfL): Inferring halo properties from luminous tracers with machine learning trained on cosmological simulations

We present Dark from Light (DfL), a novel method for inferring the dark sector in wide-field galaxy surveys that leverages a machine learning approach trained on contemporary cosmological simulations. The aim of this algorithm is to provide a fast, straightforward, and accurate route to estimating dark matter halo masses and group membership in wide-field spectroscopic galaxy surveys. This approach requires a highly limited number of input parameters (RA, Dec, z, and stellar mass plus uncertainties) and yields full probability distribution functions for the output halo masses of galaxies, groups, and clusters. To achieve this, we trained a series of random forest (RF) regression models on the IllustrisTNG and EAGLE simulations at z = 0 − 3, which provided model-dependent mappings from luminous tracers to dark matter halo properties. We incorporated the individual regression models into a virial group-finding algorithm (DfL), which outputs halo properties for observational input data. We tested the method at z = 0 − 2 for both the EAGLE and IllustrisTNG models, as well as in a cross-validation mode (where one simulation is used to train the model and the other to test). We demonstrate that known halo masses can be recovered with a mean systematic bias of ⟨b⟩= ± 0.10 dex (resulting from simulation choice), a mean statistical uncertainty of ⟨σ⟩=0.12 dex across epochs, and a central – (core) satellite classification accuracy of 96%. We establish that this approach yields halo mass recovery that is superior to standard abundance matching applied to groups identified through a friends-of-friends algorithm. Additionally, we compare the outputs of DfL to observational constraints on the M* − MHalo relation from strong gravitational lensing at z ∼ 0, demonstrating the promise of this novel approach. Finally, we systematically quantify how DfL performs on observational-like input data with varying stellar mass uncertainty and spectroscopic incompleteness, enabling robust error calibration in applications with observational galaxy surveys.