Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models
This work provides a diagnostic tool for cosmologists to uncover independent physical drivers in complex astronomical datasets, specifically for understanding dark matter halo structures.
This paper introduces a disentangled latent conditional flow matching (DL-CFM) model to separate physical factors in thermal Sunyaev-Zel'dovich (tSZ) maps of dark matter halos. By using halo mass and concentration as auxiliary variables, the model successfully recovers the established mass-concentration scaling relation and identifies latent space outliers potentially linked to unusual halo formation histories.
Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliary-variable-guided framework for disentangling representations of thermal Sunyaev-Zel'dovich (tSZ) maps of dark matter halos. We introduce halo mass and concentration as auxiliary variables and apply a lightweight alignment penalty to encourage latent dimensions to reflect these physical quantities. To generate sharp and realistic samples, we extend latent conditional flow matching (LCFM), a state-of-the-art generative model, to enforce disentanglement in the latent space. Our Disentangled Latent-CFM (DL-CFM) model recovers the established mass-concentration scaling relation and identifies latent space outliers that may correspond to unusual halo formation histories. By linking latent coordinates to interpretable astrophysical properties, our method transforms the latent space into a diagnostic tool for cosmological structure. This work demonstrates that auxiliary guidance preserves generative flexibility while yielding physically meaningful, disentangled embeddings, providing a generalizable pathway for uncovering independent factors in complex astronomical datasets.