rave-survey.org
metadata only
Results of CNN classifications
The table provides atmospheric parameters and abundances along with their uncertainties for 420165 stars in the RAVE survey as result of a Convolutional Neural Network application. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data, without applyi
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
- The Sloan Digital Sky Survey Quasar Catalog: Twelfth data release#614811
- AutoClassMK: A public neural network for automatic 2D MK classification of normal stars in basic Python#677638
- AutoClassMK: A public neural network for automatic 2D MK classification of normal stars in basic Python#674437
- Benchmarking Machine Learning Emulators of Stellar Evolution for Precision Asteroseismology#925377
- Benchmarking Machine Learning Emulators of Stellar Evolution for Precision Asteroseismology#921141
Record · ID 934088
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