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Bibliografski detalji
Glavni autori:	Kim, Soojin, Hwang, Ho Seong, Jeffrey, Niall
Format:	Preprint
Izdano:	2025
Teme:	Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Online pristup:	https://arxiv.org/abs/2509.25836
Oznake:	Dodaj oznaku Bez oznaka, Budi prvi tko označuje ovaj zapis!

Slični predmeti

Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps
od: Diao, Kangning, i dr.
Izdano: (2025)

Lensing in the Blue III: Weak Lensing Shape Catalogs of 30 Merging Galaxy Clusters
od: Saha, Sayan, i dr.
Izdano: (2026)

RVSNUpy: A Python Package for Spectroscopic Redshift Measurement Based on Cross-Correlation
od: Kim, Taewan, i dr.
Izdano: (2025)

Weak Gravitational Lensing
od: Prat, J., i dr.
Izdano: (2025)

Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method
od: Campos, A., i dr.
Izdano: (2024)

Strong Lensing Model and Dust Extinction Maps of the Host Galaxy of Type Ia Supernova H0pe
od: Galan, A., i dr.
Izdano: (2025)

CSST Strong Lensing Preparation: Fast Modeling of Galaxy-Galaxy Strong Lenses in the Big Data Era
od: Cao, Xiaoyue, i dr.
Izdano: (2025)

Dark Energy Survey Year 6 Results: Cell-based Coadds and Metadetection Weak Lensing Shape Catalogue
od: Yamamoto, M., i dr.
Izdano: (2025)

Simulation-Based Inference Benchmark for Weak Lensing Cosmology
od: Zeghal, Justine, i dr.
Izdano: (2024)

Analytical Noise Bias Correction for Precise Weak Lensing Shear Inference
od: Li, Xiangchong, i dr.
Izdano: (2024)

Weak lensing scattering transform: dark energy and neutrino mass sensitivity
od: Cheng, Sihao, i dr.
Izdano: (2021)

UNIONS-3500 Weak Lensing: II. B-mode validation for cosmic shear
od: Daley, C., i dr.
Izdano: (2026)

EMUSE: Evolutionary Map of the Universe Search Engine
od: Gupta, Nikhel, i dr.
Izdano: (2025)

Map-level baryonification: unified treatment of weak lensing two-point and higher-order statistics
od: Zhou, Alan Junzhe, i dr.
Izdano: (2025)

Gravitational Lensing of Galaxy Clustering
od: Buncher, Brandon, i dr.
Izdano: (2024)

Improved Weak Lensing Photometric Redshift Calibration via StratLearn and Hierarchical Modeling
od: Autenrieth, Maximilian, i dr.
Izdano: (2024)

Kinematic Lensing Ratio: Reviving Weak Lensing Cosmography as a Geometric Dark Energy Probe
od: Li, Qinxun
Izdano: (2026)

Application of Machine Learning Methods for Detecting Atypical Structures in Astronomical Maps
od: Karkin, I. A., i dr.
Izdano: (2024)

Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
od: Fagin, Joshua, i dr.
Izdano: (2024)

Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference
od: Jarugula, Sreevani, i dr.
Izdano: (2024)

Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
od: Jeffrey, Niall, i dr.
Izdano: (2023)

HOLISMOKES XV. Search for strong gravitational lenses combining ground-based and space-based imaging
od: Melo, A., i dr.
Izdano: (2024)

D$_4$CNN$\times$AnaCal: Physics-Informed Machine Learning for Accurate and Precise Weak Lensing Shear Estimation
od: Lin, Shurui, i dr.
Izdano: (2026)

The Atacama Cosmology Telescope: DR6 Maps
od: Naess, Sigurd, i dr.
Izdano: (2025)

Finding Strongly Lensed Supernovae from Blended Light Curves
od: Park, Sangwoo, i dr.
Izdano: (2026)

Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field
od: Pearce-Casey, R., i dr.
Izdano: (2024)

Weak-Lensing Analysis of the Galaxy Cluster Abell 85: Constraints on the Merger Scenarios of Its Southern Subcluster
od: Kim, Soojin, i dr.
Izdano: (2025)

21 cm Intensity Mapping with the DSA-2000
od: Byrne, Ruby, i dr.
Izdano: (2023)

Bayesian mass mapping with weak lensing data using KARMMA -- validation with simulations and application to Dark Energy Survey Year 3 data
od: Boruah, Supranta S., i dr.
Izdano: (2024)

The Three Hundred Project: Mapping The Matter Distribution in Galaxy Clusters Via Deep Learning from Multiview Simulated Observations
od: de Andres, Daniel, i dr.
Izdano: (2023)

Mapping dark matter and the emergence of large-scale structure
od: Loveday, Jon, i dr.
Izdano: (2026)

Drone Beam Mapping of the TONE Radio Dish Array
od: Kuhn, Emily R., i dr.
Izdano: (2025)

An Interpretable AI Framework to Disentangle Self-Interacting and Cold Dark Matter in Galaxy Clusters: The CKAN Approach
od: Huang, Zhenyang, i dr.
Izdano: (2025)

BrahMap: A scalable and modular map-making framework for the CMB experiments
od: Anand, Avinash, i dr.
Izdano: (2025)

Cosmoglobe: Mapping the Universe from the Milky Way to the Big Bang
od: Martins, Ana Isabel Silva, i dr.
Izdano: (2026)

Correlation-based Beam Calibration of 21cm Intensity Mapping
od: Ding, Jiacheng, i dr.
Izdano: (2024)

Probing Time-Dependent Physics with Phase-Folding CMB Maps
od: Guan, Yilun
Izdano: (2025)

$\texttt{DiffLense}$: A Conditional Diffusion Model for Super-Resolution of Gravitational Lensing Data
od: Reddy, Pranath, i dr.
Izdano: (2024)

Direct Optimal Mapping Image Power Spectrum and its Window Functions
od: Xu, Zhilei, i dr.
Izdano: (2023)

Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing
od: Filipp, Andreas, i dr.
Izdano: (2024)