arXiv (All)
open access
Astro2Method: Benchmarking LLMs on Machine Learning Method Selection in Astronomy
Machine learning (ML) is increasingly used in astronomy, but choosing a suitable method for a scientific problem often requires expertise in both astronomy and ML. Large language models (LLMs) could help astronomers make such choices, yet this ability remains underexplored in existing benchmarks. We
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
- SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction.#165748
- Probabilistic Machine Learning for Short-Term Atmospheric Seeing Forecasting#1414259
- Superluminous supernova search with PineForest#1217252
- RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy#1233835
- A ready-to-deploy MLOps software platform for satellite and NEO detection at meter-class ground-based observatories#1064220
Record · ID 1423074
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