eScholarship
metadata only
Transferability of data-driven optimization results across multiple pixelated CdZnTe spectrometers
Recent work by Vavrek et al. (2026) showed that machine learning methods can be used to exploit spatial patterns of performance variations within the highly-segmented H3D M400 gamma spectrometer to improve an overall spectroscopic performance metric. That work also introduced the spectre-ml software
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
- A hybrid machine learning approach for high-throughput screening of thermodynamically stable double perovskites with optimal band gap#292085
- GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra#985521
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.#161900
- Faster Algorithms for $(2k-1)$-Stretch Distance Oracles#160914
- Integrating High-Throughput DFT and Machine Learning for Predictive Design of Hydrogen-Donor Solvents in Coal Liquefaction.#358252
Record · ID 996223
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