Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs

• URL: http://arxiv.org/abs/2609.09504v1
• Date: Tue, 08 Sep 2026 22:38:38 GMT
• Title: Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs
• Abstract summary: We perform field-level likelihood-free inference of the matter density parameter $_m$ from simulated galaxy catalogs.<n>We consider galaxy positions and line-of-sight peculiar velocities, separately and jointly, and compare permutation-invariant Deep Sets and graph neural networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We perform field-level likelihood-free inference of the matter density parameter $Ω_m$ from simulated galaxy catalogs using machine learning models with differing inductive biases. Using hydrodynamic simulations from CAMELS, we examine how observable choice and architecture govern cosmological information extraction. We consider galaxy positions and line-of-sight peculiar velocities, separately and jointly, and compare permutation-invariant Deep Sets, implemented with either multilayer perceptrons (MLPs) or Kolmogorov-Arnold Networks (KANs), to graph neural networks (GNNs), which explicitly encode spatial relations. We test in-distribution and out-of-distribution (OOD) performance across simulations with different subgrid galaxy-formation prescriptions. Deep Sets infer $Ω_m$ from velocities alone with mean relative errors of approximately $18\%$ in-distribution and $\sim25\%$ OOD, with KANs and MLPs achieving comparable performance. In contrast, the same set-based approach does not yield useful $σ_8$ predictions in either in-distribution or cross-suite tests. Adding positions does not improve Deep Sets, while GNNs infer $Ω_m$ with mean relative errors of about $10\%$ in-distribution and $10$--$17\%$ OOD. These results indicate that peculiar velocities provide the dominant source of $Ω_m$ information for set-based models in this setting, while spatial information is most effectively used by architectures that explicitly encode galaxy-galaxy relations. Because the velocity inputs are exact simulated peculiar velocities, applications to survey data will require validation under realistic velocity-measurement noise, selection effects, and survey geometry.

Related papers

• Adaptive $k$ Nearest Neighbors Classifier via Granular Ball Computing [60.23863579199315]
  The $k$-Nearest Neighbor(KNN) algorithm is widely used across various tasks.<n>In this paper, an adaptive and efficient KNN approach via granular-ball computing is proposed.
  arXiv  Detail & Related papers  (2026-08-13T07:39:46Z)
• Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models [0.0]
  Semi-analytic models are widely used to simulate galaxy properties within a cosmological framework.<n>We train a graph neural network coupled to a moment neural network to obtain a robust machine learning based model for estimating matter density parameters.<n>Our results show that the network is robust to variations in astrophysical and subgrid physics, cosmological and astrophysical parameters, and the different halo-profile treatments used across simulations.
  arXiv  Detail & Related papers  (2025-12-11T02:13:38Z)
• MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control [48.504188275208556]
  We study the problem of learning a neural sampler to generate samples from discrete state spaces where the target probability mass function $piproptomathrme-U$ is known up to normalizing constant.<n>We propose $textbfM$asked $textbfDiffusion, a novel framework for discrete neural samplers by aligning measures through a family of learning objectives.
  arXiv  Detail & Related papers  (2025-08-14T14:27:16Z)
• IAEmu: Learning Galaxy Intrinsic Alignment Correlations [16.805775045014578]
  We introduce IAEmu, a neural network-based emulator that predicts the galaxy position-position ($xi$), position-orientation ($omega$), and orientation-orientation ($eta$) correlation functions.<n>Compared to simulations, IAEmu achieves 3% average error for $xi$ and 5% for $omega$, while capturing $eta$ without overfitting.<n>IAEmu is a powerful surrogate model for galaxy bias and IA studies with direct applications to Stage IV weak lensing surveys.
  arXiv  Detail & Related papers  (2025-04-07T16:19:50Z)
• Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors [58.661454334877256]
  Drug-Target binding Affinity (DTA) prediction is essential for drug discovery. Despite the application of deep learning methods to DTA prediction, the achieved accuracy remain suboptimal. We propose $k$NN-DTA, a non-representation embedding-based retrieval method adopted on a pre-trained DTA prediction model.
  arXiv  Detail & Related papers  (2024-07-21T15:49:05Z)
• 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)
• E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification [0.0]
  We train, validate, and test GCNNs equivariant to discrete subgroups of $E(2)$ on the Galaxy10 DECals dataset. An architecture equivariant to the group $D_16$ achieves a $95.52 pm 0.18%$ test-set accuracy. All GCNNs are less susceptible to one-pixel perturbations than an identically constructed CNN.
  arXiv  Detail & Related papers  (2023-11-02T18:00:02Z)
• Robust Field-level Likelihood-free Inference with Galaxies [0.0]
  We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. We find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder.
  arXiv  Detail & Related papers  (2023-02-27T19:26:13Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• 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)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

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

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.