Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN

Abstract

Modern machine learning will allow for simulation-based inference from reionization-era 21cm observations at the Square Kilometre Array. Our framework combines a convolutional summary network and a conditional invertible network through a physics-inspired latent representation. It allows for an efficient and extremely fast determination of the posteriors of astrophysical and cosmological parameters, jointly with well-calibrated and on average unbiased summaries. The sensitivity to non-Gaussian information makes our method a promising alternative to the established power spectra.

arXiv comment15+10 pages, 11 figures

References in corpus (54)

• LOFAR: The LOw-Frequency ARray
• Normalizing Flows: An Introduction and Review of Current Methods
• The Murchison Widefield Array: the Square Kilometre Array Precursor at low radio frequencies
• 21cmFAST: A Fast, Semi-Numerical Simulation of the High-Redshift 21-cm Signal
• Hydrogen Epoch of Reionization Array (HERA)
• Efficient Simulations of Early Structure Formation and Reionization
• The Precision Array for Probing the Epoch of Reionization: 8 Station Results
• What Next-Generation 21 cm Power Spectrum Measurements Can Teach Us About the Epoch of Reionization
• Guided Image Generation with Conditional Invertible Neural Networks
• Real-time gravitational-wave science with neural posterior estimation
• Validating Bayesian Inference Algorithms with Simulation-Based Calibration
• The Baryon Acoustic Oscillation Broadband and Broad-beam Array: Design Overview and Sensitivity Forecasts
• 21cmFAST v3: A Python-integrated C code forgenerating 3D realizations of the cosmic 21cm signal
• Astraeus I: The interplay between galaxy formation and reionization
• Machine Learning and LHC Event Generation
• Deep learning from 21-cm tomography of the Cosmic Dawn and Reionization
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• 21CMMC with a 3D light-cone: the impact of the co-evolution approximation on the astrophysics of reionisation and cosmic dawn
• New Constraints on Warm Dark Matter from the Lyman-$\alpha$ Forest Power Spectrum
• High Mass X-ray Binaries and the Cosmic 21-cm Signal: Impact of Host Galaxy Absorption
• Radio Galaxy Zoo: Compact and extended radio source classification with deep learning
• Unveiling Dark Matter free-streaming at the smallest scales with high redshift Lyman-alpha forest
• Unveiling the nature of dark matter with high redshift 21 cm line experiments
• Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA
• Simulation-Based Inference of Reionization Parameters From 3D Tomographic 21 cm Lightcone Images
• Photometry of high-redshift blended galaxies using deep learning
• Simulating the 21-cm signal from reionisation including non-linear ionisations and inhomogeneous recombinations
• Fuzzy Dark Matter and the 21cm Power Spectrum
• MadNIS -- Neural Multi-Channel Importance Sampling
• Cosmology with one galaxy?
• Two Invertible Networks for the Matrix Element Method
• ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks
• Measuring QCD Splittings with Invertible Networks
• Exploring the likelihood of the 21-cm power spectrum with simulation-based inference
• Modern Machine Learning for LHC Physicists
• Machine Learning for Observational Cosmology
• Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation
• SKA Science Data Challenge 2: analysis and results
• Inferring Astrophysics and Dark Matter Properties from 21cm Tomography using Deep Learning
• Cubic-Spline Flows
• General Modified Gravity With 21cm Intensity Mapping: Simulations and Forecast
• Impact of dark matter models on the EoR 21-cm signal bispectrum
• Constraining coupled quintessence with the 21cm signal
• Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification
• Machine Learning and Cosmology
• Cornering Extended Starobinsky Inflation with CMB and SKA
• Synergistic tests of inflation
• Galaxy Spectra neural Network (GaSNet). II. Using Deep Learning for Spectral Classification and Redshift Predictions
• Noise-Net: Determining physical properties of HII regions reflecting observational uncertainties
• Lux ex tenebris: The imprint of annihilating dark matter on the intergalactic medium during Cosmic Dawn
• Deep Learning 21cm Lightcones in 3D
• Application-driven Validation of Posteriors in Inverse Problems
• 3D ScatterNet: Inference from 21 cm Light-cones
• Constraining General Modifications of Gravity during Reionisation

Cited by in corpus (9)

• SKATR: A Self-Supervised Summary Transformer for SKA
• The 21cm-galaxy cross-correlation: Realistic forecast for 21cm signal detection and reionisation constraints
• From ANN to BNN: Inferring Reionization Parameters using Uncertainty-aware Emulators of 21-cm Summaries
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• 21 cm Cosmology Sensitivity to Small-Scale Structure: Warm vs Neutrino-Interacting Dark Matter
• Sample Variance Denoising in Cylindrical 21-cm Power Spectra
• Large Language Models -- the Future of Fundamental Physics?
• Direct reconstruction of the Reionization history from 21cm 2D Power Spectra
• CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization