[2511.04681] Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

[2511.04681] Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

arXiv - Machine Learning 5 min read Article

Summary

This article presents a novel simulation-based inference pipeline utilizing deep learning to analyze weak lensing and galaxy clustering maps from the Dark Energy Survey Year 3, enhancing cosmological parameter constraints.

Why It Matters

The research highlights the integration of deep learning in cosmology, showcasing its potential to improve the analysis of large-scale structures in the universe. This advancement is crucial for future observational campaigns aimed at understanding dark energy and the universe's expansion.

Key Takeaways

  • Introduces a simulation-based inference pipeline combining weak lensing and galaxy clustering data.
  • Utilizes deep graph convolutional neural networks to enhance cosmological parameter estimation.
  • Demonstrates significant improvements in parameter constraints compared to traditional methods.

Astrophysics > Cosmology and Nongalactic Astrophysics arXiv:2511.04681 (astro-ph) [Submitted on 6 Nov 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Dark Energy Survey Year 3 results: Simulation-based $w$CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design Authors:A. Thomsen, J. Bucko, T. Kacprzak, V. Ajani, J. Fluri, A. Refregier, D. Anbajagane, F. J. Castander, A. Ferté, M. Gatti, N. Jeffrey, A. Alarcon, A. Amon, K. Bechtol, M. R. Becker, G. M. Bernstein, A. Campos, A. Carnero Rosell, C. Chang, R. Chen, A. Choi, M. Crocce, C. Davis, J. DeRose, S. Dodelson, C. Doux, K. Eckert, J. Elvin-Poole, S. Everett, P. Fosalba, D. Gruen, I. Harrison, K. Herner, E. M. Huff, M. Jarvis, N. Kuropatkin, P.-F. Leget, N. MacCrann, J. McCullough, J. Myles, A. Navarro-Alsina, S. Pandey, A. Porredon, J. Prat, M. Raveri, M. Rodriguez-Monroy, R. P. Rollins, A. Roodman, E. S. Rykoff, C. Sánchez, L. F. Secco, E. Sheldon, T. Shin, M. A. Troxel, I. Tutusaus, T. N. Varga, N. Weaverdyck, R. H. Wechsler, B. Yanny, B. Yin, Y. Zhang, J. Zuntz, M. Aguena, S. Allam, F. Andrade-Oliveira, D. Bacon, J. Blazek, D. Brooks, R. Camilleri, J. Carretero, R. Cawthon, L. N. da Costa, M. E. da Silva Pereira, T. M. Davis, J. De Vicente, S. Desai, P. Doel, J. García-Bellido, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, O. Lahav, S. Lee, J. L. Marshall, J. Mena-Fernández, F. Menanteau, R. Miquel, J. Muir, R. L. C. Ogando, A. A. ...

Related Articles

[2603.16105] Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization
Llms

[2603.16105] Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization

Abstract page for arXiv paper 2603.16105: Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization

arXiv - AI · 4 min ·
[2603.09643] MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings
Llms

[2603.09643] MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings

Abstract page for arXiv paper 2603.09643: MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Contro...

arXiv - AI · 4 min ·
[2602.04943] Graph-Theoretic Analysis of Phase Optimization Complexity in Variational Wave Functions for Heisenberg Antiferromagnets
Machine Learning

[2602.04943] Graph-Theoretic Analysis of Phase Optimization Complexity in Variational Wave Functions for Heisenberg Antiferromagnets

Abstract page for arXiv paper 2602.04943: Graph-Theoretic Analysis of Phase Optimization Complexity in Variational Wave Functions for Hei...

arXiv - AI · 3 min ·
[2602.00185] QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities
Llms

[2602.00185] QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities

Abstract page for arXiv paper 2602.00185: QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities

arXiv - AI · 4 min ·
More in Machine Learning: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime