Diffusion Geometry for Galaxy Bulk-Flow Reconstruction I: Foundations and Full-Vector Validation

We formulate a diffusion-spectral field-reconstruction framework based on diffusion geometry for reconstructing cosmological vector fields from finite and nonuniformly distributed galaxy point clouds. Low-frequency eigenfunctions of a Markov operator defined on the point cloud are used as a global basis shared by the Cartesian velocity components. Within the same operator framework, we construct generator-spectrum regularization, out-of-graph evaluation through Nyström extension, and a metric-calibrated gradient based on the carré du champ and the coordinate Gram matrix. We validate the method using three controlled mock catalogs constructed from three-dimensional velocity vectors in a cosmological simulation, successively incorporating complete sampling, distance-dependent selection, and direction-dependent selection. Metric calibration recovers coordinate and affine gradients to machine precision. We further show that convergence with increasing retained mode number into the high-mode regime is essential for both method comparison and the interpretation of selection weighting. After convergence is established, the diffusion-spectral estimator outperforms the adaptive local-kernel baseline in every split for Mock-1 and Mock-2, while the apparent benefits of selection weighting seen at low mode numbers in Mock-2 and Mock-3 do not persist. The most stable primary estimator uses an operator-side unweighted geometry together with an unweighted loss. Direction-dependent weighting does not improve the mean reconstruction risk, but acts as a secondary sensitivity model that slightly reduces the dispersion of reconstruction errors across different directions. We deliberately leave the three-dimensional identifiability problem from radial-velocity observations outside the scope of this study. Instead, we establish the preceding methodological foundation for field representation, regularization, local differentiation, and out-of-sample extension on irregular galaxy point clouds. This preprint has been submitted to Physical Review D as part of a joint submission with the companion paper, “Diffusion Geometry for Galaxy Bulk-Flow Reconstruction II: Three-Dimensional Potential-Flow Reconstruction from Radial Velocities.” This record contains the submitted manuscript, not an accepted manuscript or a publisher-issued version. The research code, configuration records, and selected numerical outputs supporting Papers I and II are publicly archived as software version 0.1.0 at https://doi.org/10.5281/zenodo.22898864. The scope of the public archive and access to additional data are described in the manuscript's Data Availability statement.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-25
DOI
https://doi.org/10.5281/zenodo.22935023
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
preprint

Diffusion Geometry for Galaxy Bulk-Flow Reconstruction I: Foundations and Full-Vector Validation

Tsutomu T. Takeuchi, Hai-Xia Ma, Gayathri Asok, Yu Ogane
Zenodo (CERN European Organization for Nuclear Research)
Galaxies: Formation, Evolution, Phenomena
preprint

Diffusion Geometry for Galaxy Bulk-Flow Reconstruction I: Foundations and Full-Vector Validation

Tsutomu T. Takeuchi, Hai-Xia Ma, Gayathri Asok, Yu Ogane
preprint en

Abstract

We formulate a diffusion-spectral field-reconstruction framework based on diffusion geometry for reconstructing cosmological vector fields from finite and nonuniformly distributed galaxy point clouds. Low-frequency eigenfunctions of a Markov operator defined on the point cloud are used as a global basis shared by the Cartesian velocity components. Within the same operator framework, we construct generator-spectrum regularization, out-of-graph evaluation through Nyström extension, and a metric-calibrated gradient based on the carré du champ and the coordinate Gram matrix. We validate the method using three controlled mock catalogs constructed from three-dimensional velocity vectors in a cosmological simulation, successively incorporating complete sampling, distance-dependent selection, and direction-dependent selection. Metric calibration recovers coordinate and affine gradients to machine precision. We further show that convergence with increasing retained mode number into the high-mode regime is essential for both method comparison and the interpretation of selection weighting. After convergence is established, the diffusion-spectral estimator outperforms the adaptive local-kernel baseline in every split for Mock-1 and Mock-2, while the apparent benefits of selection weighting seen at low mode numbers in Mock-2 and Mock-3 do not persist. The most stable primary estimator uses an operator-side unweighted geometry together with an unweighted loss. Direction-dependent weighting does not improve the mean reconstruction risk, but acts as a secondary sensitivity model that slightly reduces the dispersion of reconstruction errors across different directions. We deliberately leave the three-dimensional identifiability problem from radial-velocity observations outside the scope of this study. Instead, we establish the preceding methodological foundation for field representation, regularization, local differentiation, and out-of-sample extension on irregular galaxy point clouds. This preprint has been submitted to Physical Review D as part of a joint submission with the companion paper, “Diffusion Geometry for Galaxy Bulk-Flow Reconstruction II: Three-Dimensional Potential-Flow Reconstruction from Radial Velocities.” This record contains the submitted manuscript, not an accepted manuscript or a publisher-issued version. The research code, configuration records, and selected numerical outputs supporting Papers I and II are publicly archived as software version 0.1.0 at https://doi.org/10.5281/zenodo.22898864. The scope of the public archive and access to additional data are described in the manuscript's Data Availability statement.

Zenodo (CERN European Organization for Nuclear Research)
The Institute of Statistical Mathematics (JP), Nagoya University (JP)
Galaxies: Formation, Evolution, Phenomena
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.