arXiv (All)
open access
Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach
Extracting cosmological information from microwave sky observations requires accurate estimation of the underlying Cosmic Microwave Background (CMB) by removing foreground contamination, instrumental noise, and the effects of beam convolution. In this work, we develop a machine learning-based approa
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Detection of metal absorption lines in quasar spectra: a neural network approach using U-Net#1064616
- Learning the averaged history of an inhomogeneous universe from its present day density field#784739
- Learning the averaged history of an inhomogeneous universe from its present day density field#783149
- The GOGREEN Survey: AI Powered Deconvolution Lifts The Veil on Outside-in Environmental Quenching at z > 1#822322
- Convolutional non-parametric Gamma-Ray Signal and Background Separation#1105476
Record · ID 1130466
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive