Galactification: painting galaxies onto dark matter only simulations using a transformer-based model
- URL: http://arxiv.org/abs/2511.08438v1
- Date: Wed, 12 Nov 2025 01:58:42 GMT
- Title: Galactification: painting galaxies onto dark matter only simulations using a transformer-based model
- Authors: Shivam Pandey, Christopher C. Lovell, Chirag Modi, Benjamin D. Wandelt,
- Abstract summary: We develop a framework to rapidly generate mock galaxy catalogs conditioned on inexpensive dark-matter-only simulations.<n>We present a multi-modal, transformer-based model that takes 3D dark matter density and velocity fields as input, and outputs a corresponding point cloud of galaxies.<n>We demonstrate that our trained model faithfully reproduces a variety of galaxy summary statistics and correctly captures their variation with changes in the underlying cosmological and astrophysical parameters.
- Score: 1.0614155147784068
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Connecting the formation and evolution of galaxies to the large-scale structure is crucial for interpreting cosmological observations. While hydrodynamical simulations accurately model the correlated properties of galaxies, they are computationally prohibitive to run over volumes that match modern surveys. We address this by developing a framework to rapidly generate mock galaxy catalogs conditioned on inexpensive dark-matter-only simulations. We present a multi-modal, transformer-based model that takes 3D dark matter density and velocity fields as input, and outputs a corresponding point cloud of galaxies with their physical properties. We demonstrate that our trained model faithfully reproduces a variety of galaxy summary statistics and correctly captures their variation with changes in the underlying cosmological and astrophysical parameters, making it the first accelerated forward model to capture all the relevant galaxy properties, their full spatial distribution, and their conditional dependencies in hydrosimulations.
Related papers
- From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics [70.27068115318681]
We introduce a neural-based ''subgrid black hole'' that learns the small-scale local dynamics and embeds it within direct simulations.<n>Thanks to the great speedup in fine-scale evolution, our approach captures intrinsic variability in accretion-driven feedback, allowing dynamic coupling between the central black hole and galaxy-scale gas.
arXiv Detail & Related papers (2025-12-01T11:47:49Z) - GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
arXiv Detail & Related papers (2024-12-23T18:58:17Z) - Geometric Trajectory Diffusion Models [58.853975433383326]
Generative models have shown great promise in generating 3D geometric systems.
Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature.
We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
arXiv Detail & Related papers (2024-10-16T20:36:41Z) - Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments [1.2231689895452238]
We propose a Deep Generative Model trained on the IllustrisTNG-100 simulation to sample 3D galaxy shapes and orientations.
The model is able to learn and predict features such as galaxy orientations that are statistically consistent with the reference simulation.
arXiv Detail & Related papers (2024-09-27T13:55:10Z) - CHARM: Creating Halos with Auto-Regressive Multi-stage networks [1.6987257996124416]
CHARM is a novel method for creating mock halo catalogs.
We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space.
arXiv Detail & Related papers (2024-09-13T18:00:06Z) - Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video [58.043569985784806]
We introduce latent intuitive physics, a transfer learning framework for physics simulation.
It can infer hidden properties of fluids from a single 3D video and simulate the observed fluid in novel scenes.
We validate our model in three ways: (i) novel scene simulation with the learned visual-world physics, (ii) future prediction of the observed fluid dynamics, and (iii) supervised particle simulation.
arXiv Detail & Related papers (2024-06-18T16:37:44Z) - Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs [75.7104463046767]
This paper proposes a novel learning based simulation model that characterizes the varying spatial and temporal dependencies in particle systems.
We empirically evaluate GNSTODE's simulation performance on two real-world particle systems, Gravity and Coulomb.
arXiv Detail & Related papers (2023-05-21T03:51:03Z) - Towards solving model bias in cosmic shear forward modeling [2.967246997200238]
Weak gravitational lensing generates a slight shearing of galaxy morphologies called cosmic shear.
Modern techniques of shear estimation based on statistics of ellipticity measurements suffer from the fact that the ellipticity is not a well-defined quantity for arbitrary galaxy light profiles.
We show that a hybrid physical and deep learning Hierarchical Bayesian Model, where a generative model captures the galaxy morphology, enables us to recover an unbiased estimate of the shear on realistic galaxies.
arXiv Detail & Related papers (2022-10-28T16:23:49Z) - Satellite galaxy abundance dependency on cosmology in Magneticum
simulations [101.18253437732933]
We build an emulator of satellite abundance based on cosmological parameters.
We find that $A$ and $beta$ depend on cosmological parameters, even if weakly.
We also show that satellite abundance cosmology dependency differs between full-physics (FP) simulations, dark-matter only (DMO) and non-radiative simulations.
arXiv Detail & Related papers (2021-10-11T18:00:02Z) - Hybrid analytic and machine-learned baryonic property insertion into
galactic dark matter haloes [1.2599533416395767]
Baryonic properties require complex hydrodynamic simulations that are computationally costly to run.
We merge an extended version of the equilibrium model, an analytic formalism describing the evolution of the stellar, gas, and metal content of galaxies, into a machine learning framework.
We create a high-speed hydrodynamic simulation emulator that populates galactic dark matter haloes in N-body simulations with baryonic properties.
arXiv Detail & Related papers (2020-12-10T16:50:33Z) - Fast and Accurate Non-Linear Predictions of Universes with Deep Learning [21.218297581239664]
We build a V-Net based model that transforms fast linear predictions into fully nonlinear predictions from numerical simulations.
Our NN model learns to emulate the simulations down to small scales and is both faster and more accurate than the current state-of-the-art approximate methods.
arXiv Detail & Related papers (2020-12-01T03:30:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.