# Handoff: Set Up Trajectory-Based Optimization for M1 Mirror

**From:** Mario (Clawdbot)  
**To:** Claude (Windows/LAC)  
**Date:** 2026-01-29  
**Context:** M1 GigaBIT mirror optimization using new Zernike Trajectory Method

---

## Summary

A new Zernike Trajectory extractor has been implemented and validated. It provides mode-specific optimization metrics (coma, astigmatism, trefoil, etc.) integrated across the full 20°-60° operating range instead of discrete weighted sums.

**Your task:** Set up an Atomizer optimization study using TPE optimizer with `total_filtered_rms_nm` as the primary objective, logging mode-specific metrics.

---

## What's Already Done

1. ✅ **Extractor implemented:** `optimization_engine/extractors/extract_zernike_trajectory.py`
2. ✅ **Validated on M1 model:** R² = 1.0 (physics model confirmed)
3. ✅ **Documentation:** `docs/physics/ZERNIKE_TRAJECTORY_METHOD.md`
4. ✅ **Example config:** `docs/examples/trajectory_optimization_config.yaml`

**Pull latest from Gitea:**
```bash
git pull origin main
```

---

## What You Need to Set Up

### 1. Create New Study: `SAT3_Trajectory`

Location: `studies/SAT3_Trajectory/` (or similar)

### 2. FEA Configuration

The FEA must solve **6 subcases** with these elevation angles:
- 90° (manufacturing reference)
- 20° (measurement/polishing reference)
- 30° (new - for trajectory)
- 40° (primary operational)
- 50° (new - for trajectory)
- 60° (secondary operational)

**Subcase labels in NX must be the angle numbers** (e.g., LABEL = 20, LABEL = 40, etc.)

### 3. Extractor Configuration

Use the trajectory extractor:

```yaml
extractors:
  - id: ext_trajectory
    name: "Zernike Trajectory"
    type: zernike_trajectory
    config:
      reference_angle: 20.0
      # Subcases auto-detected from OP2 labels
```

### 4. Objectives

**Primary objective (what optimizer minimizes):**
```yaml
objectives:
  - id: obj_total
    name: "Total Integrated RMS"
    source:
      extractor_id: ext_trajectory
      output_name: total_filtered_rms_nm
    direction: minimize
    weight: 1.0
```

**Secondary objectives (logged but not optimized, weight=0):**
```yaml
  - id: obj_coma
    name: "Coma RMS"
    source:
      extractor_id: ext_trajectory
      output_name: coma_rms_nm
    direction: minimize
    weight: 0.0

  - id: obj_astig
    name: "Astigmatism RMS"
    source:
      extractor_id: ext_trajectory
      output_name: astigmatism_rms_nm
    direction: minimize
    weight: 0.0

  - id: obj_trefoil
    name: "Trefoil RMS"
    source:
      extractor_id: ext_trajectory
      output_name: trefoil_rms_nm
    direction: minimize
    weight: 0.0

  - id: obj_spherical
    name: "Spherical RMS"
    source:
      extractor_id: ext_trajectory
      output_name: spherical_rms_nm
    direction: minimize
    weight: 0.0
```

### 5. Optimizer Configuration

**Use TPE (recommended for fresh start):**
```yaml
optimizer:
  type: tpe
  config:
    n_trials: 100
    n_startup_trials: 15
```

### 6. Constraints (Optional but Recommended)

```yaml
constraints:
  # Sanity check: linear model should fit well
  - id: con_r2
    name: "Trajectory fit quality"
    source:
      extractor_id: ext_trajectory
      output_name: linear_fit_r2
    type: soft
    operator: ">="
    threshold: 0.95
```

### 7. Design Variables

Use Antoine's existing wiffle tree and geometry parameters. Key ones likely include:
- Wiffle tree radial positions (R1, R2, R3)
- Wiffle tree angular offsets
- Rib thickness
- Back pocket geometry
- Support pad locations

**Ask Antoine for the current parameter names and ranges.**

---

## Validation Checklist

Before running optimization, verify:

- [ ] FEA solves all 6 subcases (90, 20, 30, 40, 50, 60)
- [ ] OP2 contains displacement results for all subcases
- [ ] Subcase labels in OP2 are angle numbers (check with extractor)
- [ ] Extractor returns valid results (run standalone test first)
- [ ] R² ≈ 1.0 (confirms physics model holds)

**Test command:**
```python
from optimization_engine.extractors.extract_zernike_trajectory import extract_zernike_trajectory

result = extract_zernike_trajectory('path/to/solution.op2')
print(f"R² = {result['linear_fit_r2']}")
print(f"Total RMS = {result['total_filtered_rms_nm']} nm")
print(f"Coma RMS = {result['coma_rms_nm']} nm")
```

---

## Expected Outputs

After running the extractor, you should see:
- `total_filtered_rms_nm`: ~4-10 nm (integrated across angles)
- `coma_rms_nm`: ~3-10 nm
- `astigmatism_rms_nm`: ~3-10 nm
- `linear_fit_r2`: ~1.0

During optimization, track:
- How `total_filtered_rms_nm` decreases
- Which modes (coma, astig, etc.) improve most
- Whether R² stays high (if it drops, something is wrong)

---

## Key Files to Reference

| File | Purpose |
|------|---------|
| `docs/physics/ZERNIKE_TRAJECTORY_METHOD.md` | Full method documentation |
| `docs/examples/trajectory_optimization_config.yaml` | Example config |
| `optimization_engine/extractors/extract_zernike_trajectory.py` | Extractor code |
| `2-Projects/P04-GigaBIT-M1/Technical-Analysis/Tensor_Project/TECH-SPEC-Zernike-Trajectory-Optimization.md` | Physics derivation + validation results |

---

## Questions?

If anything is unclear, the tech spec in PKM has the full physics derivation and validation results. The extractor auto-detects angles from OP2 subcase labels, so as long as the FEA is set up correctly, it should "just work."

Good luck! 🚀

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*Handoff document by Mario (Clawdbot), 2026-01-29*