Speaker
Description
Likelihood-based forward modeling is standard in galaxy formation, but most implementations are formulated as forward maps rather than explicit trajectory-level likelihoods conditioned jointly on assembly history and environment. We introduce a Graph Path Likelihood Model (GPLM) on layered halo graphs, where temporal edges encode causal transport and coeval host edges encode environmental conditioning. On a fixed layered graph, the graph-conditioned path measure is written as $P(\mathbf{x}\mid G)\propto p_{\rm attach}(\mathbf{x}\mid G)\exp[-S(\mathbf{x}; G)]$, where $S$ is an effective action for dynamical increments, currently implemented as a Gaussian Onsager-Machlup term, and $p_{\rm attach}$ is a boundary measure for node entry. We also discuss a minimal preferential attachment-detachment prescription for the graph probability $P(G)$, which facilitates placing the likelihood within a cosmological ensemble of layered graphs. Trained on layered graphs reconstructed from TNG50-1, GPLM improves stellar- and gas-mass predictions over transport-only baselines. As fixed-graph applications, we evaluate dark-matter-deficient-galaxy operator averages, compute gas-channel response under controlled deformations, and compare full and host-ablated path measures through likelihood-ratio diagnostics. In these examples, higher-order satellites show a higher incidence of dark-matter deficiency and broader graph-to-graph variation, while the gas-rich response indicates more diverse environmental processing histories. GPLM thus provides a bridge between astrophysical forward modeling, stochastic effective actions on structured histories, and path-integral diagnostics of environment-dependent galaxy evolution.
