arXiv (All)
open access
Tighter Dark Matter Constraints from the Projected Mass Method: A Neural Network Enhanced Method for Galaxy Groups and Clusters
astrophysics-of-galaxiescosmology-and-nongalactic-astrophysicsgeneral-relativity-and-quantum-cosmology
Measuring the total mass of the Milky Way and nearby galaxy groups is difficult because classical dynamical estimators rely on assumptions about satellite orbital geometry that are rarely satisfied in practice, and because only a handful of satellite galaxies are typically available as kinematic tra
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
- Eliminating Dark Matter: Wave Geometry in the Time Field Model as an Alternative for Galactic Dynamics#721777
- Single-Parameter Exponential Model Outperforms Standard Dark Matter and Modified Newtonian Frameworks in Predicting Galactic Rotation Curves#1411852
- A New Dark Matter Model for Galaxies#639549
- DESI 2024 II: sample definitions, characteristics, and two-point clustering statistics#983412
- Bimodal Regime Structure in Galactic Rotation Curves: Evidence for Distinct Dynamical States and a Field-Based Interpretation of the Dark Matter Effect#164842
Record · ID 1423926
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