Copertina

Benchmarking field-level cosmological inference from galaxy redshift surveys

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Simon, Hugo, Lanusse, François, de Mattia, Arnaud
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Computation
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Field-level inference in cosmology
di: Leclercq, Florent
Pubblicazione: (2025)

Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey
di: Zhou, Xingchen, et al.
Pubblicazione: (2026)

Teaching dark matter simulations to speak the halo language
di: Pandey, Shivam, et al.
Pubblicazione: (2024)

pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
di: Thorp, Stephen, et al.
Pubblicazione: (2024)

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

Probabilistic cosmological inference on HI tomographic data
di: Andrianomena, Sambatra
Pubblicazione: (2025)

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

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

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

Probing down to early cosmic epochs using limited redshift ($z\lesssim 1$) optical surveys with host galaxy age and lookback time analysis
di: Kasthurirangan, Siddharth, et al.
Pubblicazione: (2025)

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

The DECADE cosmic shear project II: photometric redshift calibration of the source galaxy sample
di: Anbajagane, D., et al.
Pubblicazione: (2025)

How much information can be extracted from galaxy clustering at the field level?
di: Nguyen, Nhat-Minh, et al.
Pubblicazione: (2024)

The PAU Survey: Photometric redshift estimation in deep wide fields
di: Navarro-Gironés, D., et al.
Pubblicazione: (2023)

Lagrangian displacement field estimators in cosmology
di: Ota, Atsuhisa, et al.
Pubblicazione: (2022)

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

Differentiable Cosmological Simulation with Adjoint Method
di: Li, Yin, et al.
Pubblicazione: (2022)

Deep inference of simulated strong lenses in ground-based surveys
di: Poh, Jason, et al.
Pubblicazione: (2025)

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

Concept integral field unit spectrometer instrument for the next-generation mm-wave cosmological surveys
di: Kovács, Attila, et al.
Pubblicazione: (2025)

Inferring galaxy cluster masses from cosmic microwave background lensing with neural simulation based inference
di: Baxter, Eric J., et al.
Pubblicazione: (2024)

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

Prospects of the multi-channel photometric survey telescope in the cosmological application of Type Ia supernovae
di: Wang, Zhenyu, et al.
Pubblicazione: (2024)

gCAMB: A GPU-accelerated Boltzmann solver for next-generation cosmological surveys
di: Storchi, L., et al.
Pubblicazione: (2025)

Near-field imaging of local interference in radio interferometric data: Impact on the redshifted 21 cm power spectrum
di: Munshi, S., et al.
Pubblicazione: (2025)

KiDS-Legacy: Angular galaxy clustering from deep surveys with complex selection effects
di: Yan, Ziang, et al.
Pubblicazione: (2024)

PyBird-JAX: Accelerated inference in large-scale structure with model-independent emulation of one-loop galaxy power spectra
di: Reeves, Alexander, et al.
Pubblicazione: (2025)

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

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

Euclid Quick Data Release (Q1). Photometric redshifts and physical properties of galaxies through the PHZ processing function
di: Euclid Collaboration, et al.
Pubblicazione: (2025)

Explaining the "too massive" high-redshift galaxies in JWST data: numerical study of three effects and a simple relation
di: Ziegler, Joshua J., et al.
Pubblicazione: (2025)

A scalable Bayesian framework for galaxy emission line detection and redshift estimation
di: Kuhn, Alexander, et al.
Pubblicazione: (2026)

Dimensional reduction for sampled priors and application to photometric redshift distributions
di: Bernstein, Gary, et al.
Pubblicazione: (2025)

How to embed any likelihood into SBI: Application to Planck + Stage IV galaxy surveys and Dynamical Dark Energy
di: Abellán, Guillermo Franco, et al.
Pubblicazione: (2025)

The Aemulus Project VI: Emulation of beyond-standard galaxy clustering statistics to improve cosmological constraints
di: Storey-Fisher, Kate, et al.
Pubblicazione: (2022)

The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts
di: Nakazono, L., et al.
Pubblicazione: (2024)

Determination of galaxy photometric redshifts using Conditional Generative Adversarial Networks (CGANs)
di: Garcia-Fernandez, M.
Pubblicazione: (2025)

ANNZ+: an enhanced photometric redshift estimation algorithm with applications on the PAU Survey
di: Pathi, Imdad Mahmud, et al.
Pubblicazione: (2024)

HAYATE: Photometric redshift estimation by hybridising machine learning with template fitting
di: Tanigawa, Shingo, et al.
Pubblicazione: (2024)