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arxiv: 2606.23783 · v1 · pith:7YS3EZQ2new · submitted 2026-06-22 · 🌌 astro-ph.CO · astro-ph.GA

GalSBI: Forward Modelling Galaxy Clustering and Population

Silvan Fischbacher , Luca Tortorelli , Tomasz Kacprzak , Alexandre Refregier This is my paper

Pith reviewed 2026-06-26 07:13 UTC · model grok-4.3

classification 🌌 astro-ph.CO astro-ph.GA
keywords galaxy clusteringforward modelingsubhalo abundance matchingsimulation-based inferenceDES Y3redshift distributionslarge-scale structuregalaxy population
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The pith

GalSBI now jointly models galaxy populations and their clustering using optimal transport-based subhalo abundance matching.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper extends the GalSBI framework to jointly model both the statistical properties of galaxies and how they cluster in space. It achieves this by adding an efficient subhalo abundance matching method based on optimal transport that assigns galaxies to dark matter structures. The authors then run simulation-based inference to tune the model against DES Y3 imaging data and check that the output images reproduce observed photometry, morphology, angular clustering, and redshift distributions. A sympathetic reader would care because the approach supplies forward simulations that already contain realistic sample variance and source clustering, which are otherwise hard to capture in large-scale structure analyses. If the extension works as described, it supplies a public tool that can be used directly for modeling blending, redshift uncertainty, and related effects in ongoing and upcoming galaxy surveys.

Core claim

By incorporating an optimal transport-based subhalo abundance matching scheme into the GalSBI framework, the model jointly captures galaxy population characteristics and their spatial clustering. When used with simulation-based inference on DES Y3 imaging data, the resulting simulations match observed photometry, morphology, angular power spectra, and redshift distributions, with redshift means agreeing within 0.2 to 1.6 sigma. This enables forward-modelled image simulations that include realistic clustering for modeling sample variance and other effects in large-scale structure surveys.

What carries the argument

The optimal transport-based subhalo abundance matching scheme, which efficiently assigns galaxies to subhalos to incorporate clustering into the population model.

If this is right

  • Simulations now include realistic clustering, allowing modeling of sample variance in surveys.
  • Redshift distributions have more realistic uncertainty due to clustering contributions.
  • Galaxy luminosity function and galaxy-halo connection can be measured as byproducts.
  • The public code enables accurate image simulations for current and next-generation surveys.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Such models could help quantify blending effects in dense fields for future telescopes.
  • Extending this to other datasets might reveal tensions in galaxy-halo connections across surveys.
  • Improved clustering modeling could reduce biases in cosmological parameter estimation from galaxy clustering.

Load-bearing premise

The assumption that the optimal transport subhalo abundance matching produces unbiased constraints on galaxy parameters when validated against DES Y3 data.

What would settle it

Finding that the angular power spectrum of simulated galaxies deviates significantly from DES Y3 data for multiple magnitude and color cuts, beyond the reported good agreement.

read the original abstract

Forward modelling is a powerful approach for analyzing large-scale structure surveys. For this purpose, we extend the GalSBI framework to jointly model the galaxy population and clustering using an efficient subhalo abundance matching scheme based on optimal transport. We use simulation-based inference to constrain the model parameters by comparing UFig image simulations with DES Y3 imaging data. As a validation, we find that galaxy photometry and morphology agree well with multi-band imaging data of different depths, namely DES and HSC deep fields. Galaxy clustering for simulation and data is also in good agreement when comparing the angular power spectrum for different magnitude and color cuts. We further compare simulated redshift distributions against high-precision photometric redshifts in HSC deep field imaging of the COSMOS field. We find the redshift distributions across magnitude cuts to be similar to previous work, however with more realistic uncertainty modelling due to the addition of clustering contribution to sample variance. The agreement of the mean redshifts with data is very good, between $0.2\sigma$ and $1.6\sigma$ for different magnitude cuts, with sample variance being the dominant uncertainty contributor in bright samples ($<24$ mag) and subdominant compared to galaxy population model uncertainty in fainter samples. As a byproduct we measure the galaxy luminosity function and galaxy-halo connection, which are broadly consistent with existing literature. The updated GalSBI code and galaxy population model are publicly available. They enable accurate forward-modelled image simulations with realistic clustering, which can be used to model the effect of sample variance, source clustering, redshift distributions, and blending in large-scale-structure surveys. This makes GalSBI a powerful tool for the analysis of current and next-generation cosmological galaxy surveys.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper extends the GalSBI framework to jointly model galaxy population and clustering using an efficient optimal transport-based subhalo abundance matching scheme. It applies simulation-based inference to constrain model parameters by comparing UFig image simulations to DES Y3 imaging data. Validation consists of comparisons showing good agreement in galaxy photometry, morphology, angular power spectra for various magnitude and color cuts, and redshift distributions against HSC deep field data, with mean redshifts agreeing to 0.2-1.6 sigma. As a byproduct, the galaxy luminosity function and galaxy-halo connection are measured and found broadly consistent with literature. The updated code and model are made publicly available.

Significance. If the central results hold, the work supplies a publicly available forward-modeling tool that incorporates realistic clustering, sample variance, source clustering, and blending effects into image simulations. This is a strength for analyses of current and next-generation cosmological surveys. The public release of the code and galaxy population model is a clear positive contribution.

major comments (1)
  1. [Validation (abstract)] Validation (as described in the abstract): The reported validation consists solely of post-fit comparisons of summary statistics (photometry, morphology, angular power spectrum, redshift distributions) between the fitted simulations and data. No tests are described in which the full OT-based SHAM + SBI inference pipeline is run on forward-simulated mocks with known input parameters to verify posterior coverage or check for bias in the recovered galaxy population and clustering parameters. This is load-bearing for the claim that the pipeline produces unbiased constraints when applied to DES Y3 data.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for highlighting this important aspect of the validation. We respond to the major comment below.

read point-by-point responses

  1. Referee: [Validation (abstract)] Validation (as described in the abstract): The reported validation consists solely of post-fit comparisons of summary statistics (photometry, morphology, angular power spectrum, redshift distributions) between the fitted simulations and data. No tests are described in which the full OT-based SHAM + SBI inference pipeline is run on forward-simulated mocks with known input parameters to verify posterior coverage or check for bias in the recovered galaxy population and clustering parameters. This is load-bearing for the claim that the pipeline produces unbiased constraints when applied to DES Y3 data.

    Authors: We agree that explicit tests of the full OT-SHAM + SBI pipeline on forward-simulated mocks with known input parameters would provide stronger evidence that the inference recovers unbiased constraints and has good posterior coverage. The current manuscript validates the approach through direct comparison of the fitted model to real DES Y3 and HSC data across multiple summary statistics. In the revised manuscript we will add a dedicated subsection describing such mock-based recovery tests, including checks for bias and coverage. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation or validation chain

full rationale

The paper extends the prior GalSBI framework with an optimal-transport SHAM scheme and uses SBI to fit galaxy population and clustering parameters directly to DES Y3 imaging data. All reported results consist of post-fit comparisons of simulated summary statistics (photometry, morphology, angular power spectra, redshift distributions) against external observations; none of these quantities are defined in terms of the fitted parameters themselves or recovered by construction from the inference step. No self-definitional equations, fitted-input-as-prediction steps, or load-bearing self-citations that reduce the central claim to unverified prior work appear in the provided text. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review limits visibility into specific parameters; the framework relies on standard domain assumptions in subhalo abundance matching and simulation-based inference.

axioms (1)
  • domain assumption Subhalo abundance matching with optimal transport accurately captures the galaxy-halo connection and clustering statistics
    Central to the described extension of GalSBI.

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discussion (0)

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Reference graph

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