arXiv (All)
open access
Stochastic analysis of finite-temperature effects on cosmological parameters by artificial neural networks
cosmology-and-nongalactic-astrophysicsgeneral-relativity-and-quantum-cosmologyhigh-energy-physics-phenomenologyhigh-energy-physics-theory
We explore the impact of finite-temperature quantum gravity effects on cosmological parameters, particularly the effective vacuum-energy sector, by incorporating temperature-dependent quantum corrections into the Hubble parameter. To this end, we modify the Cosmic Linear Anisotropy Solving System an
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
- Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology#986298
- Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology#366263
- Late-Time Alleviation of the Hubble Tension in CPL Cosmology with Massive Neutrinos via Bayesian Physics-Informed Neural Networks#1084106
- Cosmo-PINN: A Physics-Informed Neural Network for Cosmological Reconstruction#1354915
- Dynamical Approach to the Cosmological Constant#643627
Record · ID 1199669
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