Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators

• URL: http://arxiv.org/abs/2411.14748v1
• Date: Fri, 22 Nov 2024 05:53:46 GMT
• Title: Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators
• Authors: He Jia,
• Abstract summary: We introduce a new Simulation-Based Inference ( SBI) method that leverages a large number of approximate simulations for training and a small number of high-fidelity simulations for calibration. As a proof of concept, we demonstrate that cosmological parameters can be inferred at field level from projected 2-dim dark matter density maps up to $k_rm maxsim1.5,h$/Mpc at $z=0$. The calibrated posteriors closely match those obtained by directly training on $sim104$ expensive Particle-Particle (PP) simulations, but at a fraction of the computational cost
• Score: 0.0
• License:
• Abstract: A major challenge in extracting information from current and upcoming surveys of cosmological Large-Scale Structure (LSS) is the limited availability of computationally expensive high-fidelity simulations. We introduce Neural Quantile Estimation (NQE), a new Simulation-Based Inference (SBI) method that leverages a large number of approximate simulations for training and a small number of high-fidelity simulations for calibration. This approach guarantees an unbiased posterior and achieves near-optimal constraining power when the approximate simulations are reasonably accurate. As a proof of concept, we demonstrate that cosmological parameters can be inferred at field level from projected 2-dim dark matter density maps up to $k_{\rm max}\sim1.5\,h$/Mpc at $z=0$ by training on $\sim10^4$ Particle-Mesh (PM) simulations with transfer function correction and calibrating with $\sim10^2$ Particle-Particle (PP) simulations. The calibrated posteriors closely match those obtained by directly training on $\sim10^4$ expensive PP simulations, but at a fraction of the computational cost. Our method offers a practical and scalable framework for SBI of cosmological LSS, enabling precise inference across vast volumes and down to small scales.

Related papers

• Mean-Field Simulation-Based Inference for Cosmological Initial Conditions [4.520518890664213]
  We present a simple method for Bayesian field reconstruction based on modeling the posterior distribution of the initial matter density field to be diagonal Gaussian in Fourier space. Training and sampling are extremely fast (training: $sim 1, mathrmh$ on a GPU, sampling: $lesssim 3, mathrms$ for 1000 samples at resolution $1283$), and our method supports industry-standard (non-differentiable) $N$-body simulators.
  arXiv  Detail & Related papers  (2024-10-21T09:23:50Z)
• CHARM: Creating Halos with Auto-Regressive Multi-stage networks [1.6987257996124416]
  CHARM is a novel method for creating mock halo catalogs. We show that the mock halo catalogs and painted galaxy catalogs have the same statistical properties as obtained from $N$-body simulations in both real space and redshift space.
  arXiv  Detail & Related papers  (2024-09-13T18:00:06Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Fast emulation of cosmological density fields based on dimensionality reduction and supervised machine-learning [0.0]
  We show that it is possible to perform fast dark matter density field emulations with competitive accuracy using simple machine-learning approaches. New density cubes for different cosmological parameters can be estimated without relying directly on new N-body simulations.
  arXiv  Detail & Related papers  (2023-04-12T18:29:26Z)
• Neural Posterior Estimation with Differentiable Simulators [58.720142291102135]
  We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
  arXiv  Detail & Related papers  (2022-07-12T16:08:04Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Fast and realistic large-scale structure from machine-learning-augmented random field simulations [0.0]
  We train a machine learning model to transform projected lognormal dark matter density fields to more realistic dark matter maps. We demonstrate the performance of our model comparing various statistical tests with different field resolutions, redshifts and cosmological parameters.
  arXiv  Detail & Related papers  (2022-05-16T18:00:01Z)
• 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)
• Topological Echoes of Primordial Physics in the Universe at Large Scales [0.0]
  We compute persistence diagrams and derived statistics for simulations of dark matter halos. Our pipeline computes persistence in sub-boxes of full simulations and simulations are subsampled to uniform halo number.
  arXiv  Detail & Related papers  (2020-12-07T12:08:55Z)
• 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.