DarkAI: Reconstructing the density, velocity and tidal field of dark matter from a DESI-like bright galaxy sample
arXiv:2501.12621 · doi:10.3847/1538-4365/adfa26
Abstract
Reconstructing the mass density, velocity, and tidal (MTV) fields of dark matter from galaxy surveys is essential for advancing our understanding of the LSS of the Universe. In this work, we present a machine learning-based framework using a UNet convolutional neural network to reconstruct the MTV fields from mock samples of the DESI bright galaxy survey within the redshift range . Our approach accounts for realistic observational effects, including geometric selection, flux-limited data, and redshift space distortion (RSD) effects, thereby improving the fidelity of the reconstructed fields. Testing on mock galaxy catalogs generated from the Jiutian N-body simulation, our method achieves significant accuracy level. The reconstructed density field exhibits strong consistency with the true field, effectively eliminating most RSD effects and achieving a cross-correlation power spectrum coefficient greater than 0.985 on scales with . The velocity field reconstruction accurately captures large-scale coherent flows and small-scale turbulent features, exhibiting slopes of grid-to-grid relationships close to unity and scatter below 100 . Additionally, the tidal field is reconstructed without bias, successfully recovering the features of the large-scale cosmic web, including clusters, filaments, sheets, and voids. Our results confirm that the proposed framework effectively captures the large-scale distribution and dynamics of dark matter while addressing key systematic challenges. These advancements provide a reliable and robust tool for analyzing current and future galaxy surveys, paving the way for new insights into cosmic structure formation and evolution.
23 pages, 20 figures
References in corpus (25)
- Array Programming with NumPy
- The NumPy array: a structure for efficient numerical computation
- The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
- The Early Data Release of the Dark Energy Spectroscopic Instrument
- Galaxy Groups in the SDSS DR4: II. halo occupation statistics
- The Atacama Cosmology Telescope: A Catalog of > 4000 Sunyaev-Zel'dovich Galaxy Clusters
- DESI 2024 III: Baryon Acoustic Oscillations from Galaxies and Quasars
- DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation
- ELUCID - Exploring the Local Universe with reConstructed Initial Density field III: Constrained Simulation in the SDSS Volume
- HBT+: an improved code for finding subhalos and building merger trees in cosmological simulations
- Reconstructing the cosmic density field with the distribution of dark matter halos
- DESI 2024 II: Sample Definitions, Characteristics, and Two-point Clustering Statistics
- Possible observational evidence that cosmic filaments spin
- Cosmological parameters from the likelihood analysis of the galaxy power spectrum and bispectrum in real space
- Scale-dependent Galaxy Bias
- Mapping the real space distributions of galaxies in SDSS DR7: I. Two Point Correlation Functions
- Reconstructing the cosmological density and velocity fields from redshifted galaxy distributions using V-net
- Effective cosmic density field reconstruction with convolutional neural network
- Cosmic Velocity Field Reconstruction Using AI
- Weak-Lensing Detection of Intracluster Filaments in the Coma Cluster
- (DarkAI) Mapping the large-scale density field of dark matter using artificial intelligence
- CSST large-scale structure analysis pipeline: I. constructing reference mock galaxy redshift surveys
- Narrowing down the Hubble tension to the first two rungs of distance ladders
- 21-cm foreground removal using AI and frequency-difference technique
- Toward a more stringent test of gravity with redshift space power spectrum: simultaneous probe of growth and amplitude of large-scale structure