Imagen de Portada

Ly$α$NNA: A Deep Learning Field-level Inference Machine for the Lyman-$α$ Forest

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Nayak, Parth, Walther, Michael, Gruen, Daniel, Adiraju, Sreyas
Formato:	Preprint
Publicado:	2023
Materias:	Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

LyαNNA II: Field-level inference with noisy Lyα forest spectra
por: Nayak, Parth, et al.
Publicado: (2025)

Validation of the DESI DR2 Ly$α$ BAO analysis using synthetic datasets
por: Casas, L., et al.
Publicado: (2025)

MAMMOTH-Subaru. II. Diverse Populations of Circumgalactic Ly$α$ Nebulae at Cosmic Noon
por: Li, Mingyu, et al.
Publicado: (2024)

Dynamical Dark Energy Imprints in the Lyman-Alpha Forest
por: Garza, Diego, et al.
Publicado: (2026)

Human vs. machine -- 1:3. Joint analysis of classical and ML-based summary statistics of the Lyman-$α$ forest
por: Chang, S., et al.
Publicado: (2025)

Simulation-based inference has its own Dodelson-Schneider effect (but it knows that it does)
por: Homer, Jed, et al.
Publicado: (2024)

An AI super-resolution field emulator for cosmological hydrodynamics: the Lyman-α forest
por: Hafezianzadeh, Fatemeh, et al.
Publicado: (2025)

Machine Learning Techniques for Astrophysics and Cosmology: Simulation-Based Inference
por: Thiele, Leander
Publicado: (2026)

Machine Learning applications to Galaxy Clusters
por: Yepes, Gustavo, et al.
Publicado: (2026)

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

Field-level inference in cosmology
por: Leclercq, Florent
Publicado: (2025)

Differentiable Cosmological Hydrodynamics for Field-Level Inference and High Dimensional Parameter Constraints
por: Horowitz, Benjamin, et al.
Publicado: (2025)

Quantifying the Impact of LSST $u$-band Survey Strategy on Photometric Redshift Estimation and the Detection of Lyman-break Galaxies
por: Crenshaw, John Franklin, et al.
Publicado: (2025)

A Cohesive Deep Drilling Field Strategy for LSST Cosmology
por: Gris, Philippe, et al.
Publicado: (2024)

Rapid and Late Cosmic Reionization Driven by Massive Galaxies: a Joint Analysis of Constraints from 21-cm, Lyman Line & CMB Data Sets
por: Sims, Peter H., et al.
Publicado: (2025)

Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts
por: Tortorelli, Luca, et al.
Publicado: (2026)

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference
por: Jarugula, Sreevani, et al.
Publicado: (2024)

Cosmology with Galaxy Cluster Properties using Machine Learning
por: Qiu, Lanlan, et al.
Publicado: (2023)

Constraining Below-threshold Radio Source Counts With Machine Learning
por: Todarello, Elisa, et al.
Publicado: (2023)

Differentiable Forward Modeling for Efficient and Accurate Shear Inference
por: Mendoza, Ismael, et al.
Publicado: (2026)

Application of Machine Learning Methods for Detecting Atypical Structures in Astronomical Maps
por: Karkin, I. A., et al.
Publicado: (2024)

Field-level cosmological model selection: field-level simulation-based inference for Stage IV cosmic shear can distinguish dynamical dark energy
por: Mancini, A. Spurio, et al.
Publicado: (2024)

Simulation-Based Inference of the sky-averaged 21-cm signal from CD-EoR with REACH
por: Saxena, Anchal, et al.
Publicado: (2024)

Optimizing NILC Extractions of the Thermal Sunyaev-Zeldovich Effect with Deep Learning
por: Pratt, Cameron T., et al.
Publicado: (2024)

Deep Investigation of Neutral Gas Origins (DINGO): Options for robust Deep Spectral Line Imaging in the SKA-Era
por: Rhee, Jonghwan, et al.
Publicado: (2025)

Fuzzy Galaxies or Cirrus? Decomposition of Galactic Cirrus in Deep Wide-Field Images
por: Liu, Qing, et al.
Publicado: (2024)

Peculiar Velocity Reconstruction From Simulations and Observations Using Deep Learning Algorithms
por: Wang, Yuyu, et al.
Publicado: (2024)

Identifying Microlensing by Compact Dark Matter through Diffraction Patterns in Gravitational Waves with Machine Learning
por: Liu, Ao, et al.
Publicado: (2025)

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach
por: M, Obasho, et al.
Publicado: (2025)

Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference
por: Lemke, Anna-Malin, et al.
Publicado: (2026)

Fisher Score Matching for Simulation-Based Forecasting and Inference
por: Sui, Ce, et al.
Publicado: (2025)

Simulation-Based Inference for Probabilistic Galaxy Detection and Deblending
por: Mendoza, Ismael, et al.
Publicado: (2026)

Simulation-Based Inference Benchmark for Weak Lensing Cosmology
por: Zeghal, Justine, et al.
Publicado: (2024)

Data Compression with Noise Suppression for Inference under Noisy Covariance
por: Sugiyama, Sunao, et al.
Publicado: (2025)

Evidence of Ultra-faint Radio Frequency Interference in Deep 21~cm Epoch of Reionization Power Spectra with the Murchison Widefield Array
por: Wilensky, Michael J., et al.
Publicado: (2023)

D$_4$CNN$\times$AnaCal: Physics-Informed Machine Learning for Accurate and Precise Weak Lensing Shear Estimation
por: Lin, Shurui, et al.
Publicado: (2026)

cloelike: A Python Library for Cosmological Likelihood Inference in the Euclid Era
por: Bonici, Marco, et al.
Publicado: (2026)

Analytical Noise Bias Correction for Precise Weak Lensing Shear Inference
por: Li, Xiangchong, et al.
Publicado: (2024)

Machine-learning applications for weak-lensing cosmology
por: Shirasaki, Masato
Publicado: (2026)

A New Window into the Baryon Cycle at Cosmic Noon with Line Intensity Mapping: Forecasts for auto- and cross-correlations in [CII]-158$μ$m, HI 21 cm, CO$_{J+1\rightarrow J}$, and H$α$ galaxies
por: Agrawal, Shubh, et al.
Publicado: (2026)