Cover Image

Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gómez-Vargas, Isidro, Vázquez, J. Alberto
Format:	Preprint
Published:	2024
Subjects:	Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine Learning Neural and Evolutionary Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Symbolic Emulators for Cosmology: Accelerating Cosmological Analyses Without Sacrificing Precision
by: Bartlett, Deaglan J., et al.
Published: (2025)

syren-halofit: A fast, interpretable, high-precision formula for the $Λ$CDM nonlinear matter power spectrum
by: Bartlett, Deaglan J., et al.
Published: (2024)

syren-new: Precise formulae for the linear and nonlinear matter power spectra with massive neutrinos and dynamical dark energy
by: Sui, Ce, et al.
Published: (2024)

A precise symbolic emulator of the linear matter power spectrum
by: Bartlett, Deaglan J., et al.
Published: (2023)

syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
by: Kammerer, Lukas, et al.
Published: (2025)

Neural Networks Optimized by Genetic Algorithms in Cosmology
by: Gómez-Vargas, Isidro, et al.
Published: (2022)

Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
by: Mitra, Ayan, et al.
Published: (2024)

The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
by: Piras, Davide, et al.
Published: (2024)

Bayesian distances for quantifying tensions in cosmological inference and the surprise statistic
by: Schosser, Benedikt, et al.
Published: (2024)

Multilevel neural simulation-based inference
by: Hikida, Yuga, et al.
Published: (2025)

Approximating non-Gaussian Bayesian partitions with normalising flows: statistics, inference and application to cosmology
by: Röspel, Tobias, et al.
Published: (2025)

Field-level inference in cosmology
by: Leclercq, Florent
Published: (2025)

Approximating Rayleigh Scattering in Exoplanetary Atmospheres using Physics-informed Neural Networks (PINNs)
by: Dahlbüdding, David, et al.
Published: (2024)

Benchmarking field-level cosmological inference from galaxy redshift surveys
by: Simon, Hugo, et al.
Published: (2025)

A complete framework for cosmological emulation and inference with CosmoPower
by: Jense, H. T., et al.
Published: (2024)

Probabilistic cosmological inference on HI tomographic data
by: Andrianomena, Sambatra
Published: (2025)

Exploring the Early Universe with Deep Learning
by: de Salis, Emmanuel, et al.
Published: (2025)

Deep learning insights into cosmological structure formation
by: Lucie-Smith, Luisa, et al.
Published: (2020)

Simulation-based inference with scattering representations: scattering is all you need
by: Lin, Kiyam, et al.
Published: (2024)

Bayesian deep learning for cosmic volumes with modified gravity
by: García-Farieta, Jorge Enrique, et al.
Published: (2023)

Cosmo-FOLD: Fast generation and upscaling of field-level cosmological maps with overlap latent diffusion
by: Mishra, Satvik, et al.
Published: (2026)

The Potential Impact of Neuromorphic Computing on Radio Telescope Observatories
by: Pritchard, Nicholas J., et al.
Published: (2026)

RFI Detection with Spiking Neural Networks
by: Pritchard, Nicholas J., et al.
Published: (2023)

Advancing RFI-Detection in Radio Astronomy with Liquid State Machines
by: Pritchard, Nicholas J, et al.
Published: (2025)

Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
by: Pritchard, Nicholas J., et al.
Published: (2025)

Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy
by: Pritchard, Nicholas J., et al.
Published: (2024)

Supervised Radio Frequency Interference Detection with SNNs
by: Pritchard, Nicholas J., et al.
Published: (2024)

Neuromorphic Astronomy: An End-to-End SNN Pipeline for RFI Detection Hardware
by: Pritchard, Nicholas J., et al.
Published: (2025)

Accurate field-level weak lensing inference for precision cosmology
by: Zhou, Alan Junzhe, et al.
Published: (2023)

CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
by: Huang, Ningyuan, et al.
Published: (2025)

LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications
by: Shah, Rahul, et al.
Published: (2024)

Deep Learning Based Recalibration of SDSS and DESI BAO Alleviates Hubble and Clustering Tensions
by: Shah, Rahul, et al.
Published: (2024)

Towards cosmological inference on unlabeled out-of-distribution HI observational data
by: Andrianomena, Sambatra, et al.
Published: (2024)

The Inefficiency of Genetic Programming for Symbolic Regression
by: Kronberger, Gabriel, et al.
Published: (2024)

Savage-Dickey density ratio estimation with normalizing flows for Bayesian model comparison
by: Lin, Kiyam, et al.
Published: (2025)

Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
by: Jeffrey, Niall, et al.
Published: (2023)

Interpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations
by: Sorini, Daniele, et al.
Published: (2025)

Improving Bayesian inference in PTA data analysis: importance nested sampling with Normalizing Flows
by: Villa, Eleonora, et al.
Published: (2025)

Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere
by: Prat, J., et al.
Published: (2025)

CIGaRS I: Combined simulation-based inference from type Ia supernovae and host photometry
by: Karchev, Konstantin, et al.
Published: (2025)