A halo-based intrinsic-alignment model for simulation-based inference
Abstract
Simulation-based inference and other field-level weak-lensing analyses require intrinsic-alignment (IA) models generating realistic intrinsic-ellipticity fields across two-point and non-Gaussian observables. We develop a halo-based IA prescription with separate central and satellite components and test it against intrinsic galaxy shapes in FLAMINGO. We jointly fit two-point and higher-order statistics involving the IA, lensing, and density fields, including correlations between the intrinsic shapes of distinct galaxies, and compare against field-level nonlinear-alignment and density-weighted NLA prescriptions. We first evaluate the models at the positions of the original FLAMINGO galaxies, fixing the galaxy--matter connection and isolating the IA response. NLA and δNLA leave substantial discrepancies on nonlinear scales and across higher-order observables, whereas the halo model provides a simultaneous description of all statistics. We then remove the FLAMINGO galaxy catalogue and regenerate the source population from the halo field using a low-dimensional halo occupation model; the agreement is preserved even when the detailed occupation is only approximately reproduced. Much of this performance can be retained with a compact four-parameter portable model, and the same architecture transfers successfully to an independent gravity-only N-body realization with different halo definitions and shape measurements. These results show that nonlinear field-level IA modelling can be substantially improved over simple NLA-like prescriptions without introducing a large nuisance-parameter space. The halo model therefore provides a promising route for incorporating intrinsic alignments into simulation-based weak-lensing inference, although further validation across halo masses, hydrodynamical simulations, and realistic survey selections will be needed before application to data.
Related
- LMC-induced Perturbations in the Milky Way Halo II: Bridging Field-level Inference and Summary-level Simulation-Based Inference
- The Deep Learning Halo Definer: A Multimodal Framework for Halo Mass and Gas Fraction Inference on Galaxy Groups and Clusters
- Model-Independent Assessments of Tidal Alignment and Density-Weighting Dependence in Galaxy Intrinsic Alignments
- CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations
- Intrinsic Tidal Shear at the Largest Scales: Galaxy Multiplet Alignment in DESI DR2
BibTeX
@misc{gatti2026halobased,
title = {{A halo-based intrinsic-alignment model for simulation-based inference}},
author = {M. Gatti},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.37254},
eprint = {2609.37254},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.37254},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A halo-based intrinsic-alignment model for simulation-based inference
AU - M. Gatti
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.37254
UR - https://arxiv.org/abs/2609.37254
AB - Simulation-based inference and other field-level weak-lensing analyses require intrinsic-alignment (IA) models generating realistic intrinsic-ellipticity fields across two-point and non-Gaussian observables. We develop a halo-based IA prescription with separate central and satellite components and test it against intrinsic galaxy shapes in FLAMINGO. We jointly fit two-point and higher-order statistics involving the IA, lensing, and density fields, including correlations between the intrinsic shapes of distinct galaxies, and compare against field-level nonlinear-alignment and density-weighted NLA prescriptions. We first evaluate the models at the positions of the original FLAMINGO galaxies, fixing the galaxy--matter connection and isolating the IA response. NLA and δNLA leave substantial discrepancies on nonlinear scales and across higher-order observables, whereas the halo model provides a simultaneous description of all statistics. We then remove the FLAMINGO galaxy catalogue and regenerate the source population from the halo field using a low-dimensional halo occupation model; the agreement is preserved even when the detailed occupation is only approximately reproduced. Much of this performance can be retained with a compact four-parameter portable model, and the same architecture transfers successfully to an independent gravity-only N-body realization with different halo definitions and shape measurements. These results show that nonlinear field-level IA modelling can be substantially improved over simple NLA-like prescriptions without introducing a large nuisance-parameter space. The halo model therefore provides a promising route for incorporating intrinsic alignments into simulation-based weak-lensing inference, although further validation across halo masses, hydrodynamical simulations, and realistic survey selections will be needed before application to data.
ER -