Imagen de Portada

What can we learn about Reionization astrophysical parameters using Gaussian Process Regression?

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Mukherjee, Purba, Dey, Antara, Pal, Supratik
Formato:	Preprint
Publicado:	2024
Materias:	Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning
por: Mukherjee, Purba, et al.
Publicado: (2023)

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

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

Probing Interacting Dark Sectors with upcoming Post-Reionization and Galaxy Surveys
por: Shah, Rahul, et al.
Publicado: (2026)

A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning
por: Shah, Rahul, et al.
Publicado: (2023)

Fast and robust Bayesian Inference using Gaussian Processes with GPry
por: Gammal, Jonas El, et al.
Publicado: (2022)

Ionospheric effect on the synthetic Epoch of Reionization observations with the SKA1-Low
por: Pal, Samit Kumar, et al.
Publicado: (2024)

GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning
por: Do, Tuan, et al.
Publicado: (2024)

Exhaustive Symbolic Regression
por: Bartlett, Deaglan J., et al.
Publicado: (2022)

Simulation-Based Inference with Quantile Regression
por: Jia, He
Publicado: (2024)

Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation
por: Savchenko, Oleg, et al.
Publicado: (2025)

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

Bayesian model comparison and validation with Gaussian Process Regression for interferometric 21-cm signal recovery
por: Liu, Yuchen, et al.
Publicado: (2025)

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

A representation learning approach to probe for dynamical dark energy in matter power spectra
por: Piras, Davide, et al.
Publicado: (2023)

(Exhaustive) Symbolic Regression and model selection by minimum description length
por: Desmond, Harry
Publicado: (2025)

RGC: a radio AGN classifier based on deep learning. I. A semi-supervised multiclass model for VLA images
por: Hossain, M. S., et al.
Publicado: (2025)

Primordial magnetic fields in the light of upcoming post-EoR Lyman-$α$ and 21-cm observations
por: Bhaumik, Arko, et al.
Publicado: (2026)

A candidate field for deep imaging of the Epoch of Reionization observed with MWA
por: Zhang, Xueying, et al.
Publicado: (2025)

The Necessity of Individually Validated Beam Models for an Interferometric Epoch of Reionization Detection
por: Chokshi, A., et al.
Publicado: (2024)

Galactification: painting galaxies onto dark matter only simulations using a transformer-based model
por: Pandey, Shivam, et al.
Publicado: (2025)

Uncertainty-aware and Data-efficient Cosmological Emulation using Gaussian Processes and PCA
por: Günther, Sven
Publicado: (2023)

Likelihood-free Model Selection in Cosmic Reionization with Three-dimensional Tomographic 21 cm Lightcone Images
por: Binnie, T., et al.
Publicado: (2025)

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

A demonstration of the effect of fringe-rate filtering in the Hydrogen Epoch of Reionization Array delay power spectrum pipeline
por: Garsden, Hugh, et al.
Publicado: (2024)

Investigating Mutual Coupling in the Hydrogen Epoch of Reionization Array and Mitigating its Effects on the 21-cm Power Spectrum
por: Rath, E., et al.
Publicado: (2024)

A GPR-Based Emulator for Semi-numerical Reionization Code SCRIPT: Parameter Inference from 21 cm Data
por: Choudhury, T. Roy, et al.
Publicado: (2023)

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

Inferring the Hubble Constant Using Simulated Strongly Lensed Supernovae and Neural Network Ensembles
por: Gonçalves, Gonçalo, et al.
Publicado: (2025)

Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models
por: Schanz, Andreas, et al.
Publicado: (2023)

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

Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators
por: Jia, He
Publicado: (2024)

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

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

COmoving Computer Acceleration (COCA): $N$-body simulations in an emulated frame of reference
por: Bartlett, Deaglan J., et al.
Publicado: (2024)

Three-dimensional Conditional Diffusion Models for Cosmological 21 cm Lightcone Emulation
por: Xia, Bin, et al.
Publicado: (2026)

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
por: Barco, Gabriel Missael, et al.
Publicado: (2024)

A point cloud approach to generative modeling for galaxy surveys at the field level
por: Cuesta-Lazaro, Carolina, et al.
Publicado: (2023)

Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models
por: Barco, Gabriel Missael, et al.
Publicado: (2025)

Mean-Field Simulation-Based Inference for Cosmological Initial Conditions
por: Savchenko, Oleg, et al.
Publicado: (2024)