From starlight to dark matter: a stochastic interpolation approach to map dark matter from stellar density

arXiv:2609.27028 · astro-ph.GA, astro-ph.IM · Submitted 2026-09-22 · Read on arXiv

astro-ph.GA, astro-ph.IM

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: 18 pages plus appendices, 10 figures. Comments are welcome. The code accompanying this paper, for training the model and reproducing the analysis, is available in a GitHub repository (https://github.com/xou-mit/star2dm)

Code: https://github.com/xoumit/star2dm

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 13× in this paper · explained in 7 episodes
• Anisotropy — 10× in this paper · explained in 3 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Generative Model — 9× in this paper · explained in 2 episodes
• Baryonic Feedback — 8× in this paper · explained in 3 episodes
• Stellar Feedback — 8× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Diffusion Model — 6× in this paper · explained in 8 episodes
• AGN — 5× in this paper · explained in 1 episode
• Generative Models — 4× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Degeneracy — 2× in this paper · explained in 3 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Gravitational Lensing — 2× in this paper · explained in 2 episodes
• Systematic Bias — 2× in this paper · explained in 3 episodes
• AGN Feedback — 1× in this paper · explained in 1 episode
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Forward modeling — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Metallicity — 1× in this paper · explained in 3 episodes
• Simulation-based Inference — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Weak Lensing — 1× in this paper · explained in 2 episodes

Sources

• The Wide Field Infrared Survey Telescope: 100 Hubbles for the 2020s
• Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
• Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes
• Denoising Diffusion Probabilistic Models
• BaryonBridge: Stochastic Interpolant Model for Fast Hydrodynamical Simulations
• Theoretical Predictions for the Inner Dark Matter Distribution in the Milky Way Informed by Simulations
• Cosmo-FOLD: Fast generation and upscaling of field-level cosmological maps with overlap latent diffusion
• Cosmological Field Emulation and Parameter Inference with Diffusion Models
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• The DREAMS Project: A New Suite of 1,024 Simulations to Contextualize the Milky Way and Assess Physics Uncertainties
• The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Satellite Galaxies
• Score-Based Generative Modeling through Stochastic Differential Equations

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