Related papers: Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments

Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments

URL: http://arxiv.org/abs/2409.18761v1
Date: Fri, 27 Sep 2024 13:55:10 GMT
Title: Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments
Authors: Yesukhei Jagvaral, Francois Lanusse, Rachel Mandelbaum,
Abstract summary: 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.
Score: 1.2231689895452238
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Forthcoming cosmological imaging surveys, such as the Rubin Observatory LSST, require large-scale simulations encompassing realistic galaxy populations for a variety of scientific applications. Of particular concern is the phenomenon of intrinsic alignments (IA), whereby galaxies orient themselves towards overdensities, potentially introducing significant systematic biases in weak gravitational lensing analyses if they are not properly modeled. Due to computational constraints, simulating the intricate details of galaxy formation and evolution relevant to IA across vast volumes is impractical. As an alternative, we propose a Deep Generative Model trained on the IllustrisTNG-100 simulation to sample 3D galaxy shapes and orientations to accurately reproduce intrinsic alignments along with correlated scalar features. We model the cosmic web as a set of graphs, each graph representing a halo with nodes representing the subhalos/galaxies. The architecture consists of a SO(3) $\times$ $\mathbb{R}^n$ diffusion generative model, for galaxy orientations and $n$ scalars, implemented with E(3) equivariant Graph Neural Networks that explicitly respect the Euclidean symmetries of our Universe. The model is able to learn and predict features such as galaxy orientations that are statistically consistent with the reference simulation. Notably, our model demonstrates the ability to jointly model Euclidean-valued scalars (galaxy sizes, shapes, and colors) along with non-Euclidean valued SO(3) quantities (galaxy orientations) that are governed by highly complex galactic physics at non-linear scales.

Related papers

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)
How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds [1.203672395409347]
We present NeHOD, a generative framework based on variational diffusion model and Transformer, for painting galaxies/subhalos on top of dark matter halos. For each halo, NeHOD predicts the positions, velocities, masses, and concentrations of its central and satellite galaxies. We show that our model captures the complex relationships between subhalo properties as a function of the simulation parameters.
arXiv Detail & Related papers (2024-09-04T18:00:00Z)
Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata [70.9375320609781]
We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans, abundantly captured by autonomous vehicles (AV) We propose hierarchical Generative Cellular Automata (hGCA), a spatially scalable 3D generative model, which grows geometry with local kernels following, in a coarse-to-fine manner, equipped with a light-weight planner to induce global consistency.
arXiv Detail & Related papers (2024-06-12T14:56:56Z)
Learning Galaxy Intrinsic Alignment Correlations [16.805775045014578]
The intrinsic alignments (IA) of galaxies represent the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. We present a deep learning approach to emulate galaxy position-position, position-orientation, and orientation-orientation correlation function measurements and uncertainties.
arXiv Detail & Related papers (2024-04-21T16:16:56Z)
Galaxy 3D Shape Recovery using Mixture Density Network [0.6597195879147557]
A common intrinsic shape recovery method relies on an expected monotonic relationship between the intrinsic misalignment of the kinematic and morphological axes and the triaxiality parameter. Recent studies have cast doubt about underlying assumptions relating shape and intrinsic kinematic misalignment. We use a supervised machine learning approach with mixture density network (MDN) to recover the 3D shape of individual galaxies.
arXiv Detail & Related papers (2024-04-06T03:48:11Z)
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems [87.30652640973317]
Recent advances in computational modelling of atomic systems represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space. Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation. This paper provides a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems.
arXiv Detail & Related papers (2023-12-12T18:44:19Z)
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning [69.6810940330906]
We propose a novel contrastive learning framework to learn high-quality graph embedding. Specifically, we design the alignment metric that effectively captures the hierarchical data-invariant information. We show that in the hyperbolic space one has to address the leaf- and height-level uniformity which are related to properties of trees.
arXiv Detail & Related papers (2023-10-27T15:31:42Z)
Geometric Neural Diffusion Processes [55.891428654434634]
We extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling. We show that with these conditions, the generative functional model admits the same symmetry.
arXiv Detail & Related papers (2023-07-11T16:51:38Z)
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)
Morphological Classification of Galaxies in S-PLUS using an Ensemble of Convolutional Networks [0.0]
We combine accurate visual classifications of the Galaxy Zoo project with emph Deep Learning methods. A neural network model was created through an Ensemble of four other convolutional models, allowing a greater accuracy in the classification than what would be obtained with any one individual.
arXiv Detail & Related papers (2021-07-05T21:51:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.