Calibrated_Pose_Triplet_Classification_Prompt.docx (36.9 KB)
We’re exploring a new spectral material classification method that uses pose geometry derived from x-ray interactions. The attached prompt uses a 3-angle vector format — called a pose triplet — to compare unknown materials against a reference set and estimate their effective atomic number (Z-effective) using LLM-driven pattern recognition.
This prototype:
- Uses only 4 energy bins to compute 3 angular values (α_PE, α_CS, Δα)
- Classifies materials via Euclidean distance in angular space
- Returns calibrated Z-effective predictions and flags unknowns
We’re inviting others to:
- Run the prompt in ChatGPT or Gemini
- Swap in their own [α_PE, α_CS, Δα] triplets
- Share spectral data (real or simulated) so we can refine and benchmark the system
This is early-stage research from a medical imaging startup (Kairos Sensors) as part of our LLM+physics work. Feedback, critique, or collaboration welcome!