Reconstructing the Projected Dark Matter Field across 0.1-100 Mpc Scales from the SDSS Survey
astro-ph.GA
Submitted: 2026-09-16
Updated: 2026-09-16
Comments: 17 pages, 7 figures, Accepted for publication in the ApJL; This is the sixteenth paper in the "From Haloes to Galaxies" series
License: http://creativecommons.org/licenses/by/4.0/
The gist: Dark matter sets the gravitational environment in which galaxies form and evolve, but cannot be observed directly.
Terminology
Abstract
Dark matter sets the gravitational environment in which galaxies form and evolve, but cannot be observed directly. We present a conditional diffusion model that reconstructs the projected dark matter density field from the galaxy stellar-mass density field for direct application to galaxy surveys. The model is trained on CAMELS and validated on the independent IllustrisTNG300-1 simulation. Halo masses inferred from the reconstructed projected-aperture measurements agree well with the corresponding true values, with a scatter below 0.2 dex. On 100 kpc scales, reconstructed surface densities show a typical scatter of 0.3 dex in the regime most relevant for observations. We apply the model to SDSS galaxies with M 10 9,M in a contiguous low-redshift region. Averaging over 100 stochastic realizations, we reconstruct and publicly release a projected dark matter field covering 90 times90,(h-1 Mpc) squared with a pixel size of 0.097,h-1 Mpc. This pixel area corresponds to the characteristic projected area of halos with masses of 10 10.6,h-1,M. The map reveals the multiscale projected cosmic web, including cluster-scale overdensities, filaments and voids. Projected-aperture masses are statistically consistent with SDSS group-catalog masses, while the derived halo mass function broadly matches mock-catalog expectations. The reconstructed projected potential places Coma in one of the deepest wells and near a convergence region of the inferred projected acceleration field, suggesting that the reconstruction retains both local overdensities and coherent large-scale projected gravitational structure. This work shows that diffusion-based dark matter reconstruction can be applied to real galaxy surveys, enabling halo-mass- and spatially resolved dark-matter-environment-based studies of galaxy evolution in SDSS and future wide-area surveys.
Sources
- The DESI Experiment Part I: Science,Targeting, and Survey Design
- The DESI Experiment Part II: Instrument Design
- Constrained Local UniversE Simulations (CLUES)
- Denoising Diffusion Probabilistic Models
- Variational Diffusion Models
- Reconstruction of Dark Matter and Baryon Density From Galaxies: A Comparison of Linear, Halo Model and Machine Learning-Based Methods
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- Decoupled Weight Decay Regularization
- Attention Is All You Need
- Multifield Cosmology with Artificial Intelligence
- The IllustrisTNG Simulations: Public Data Release
- The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
- Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS
- BAM: Bottleneck Attention Module
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- HuggingFace's Transformers: State-of-the-art Natural Language Processing
- Group Normalization
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