L1 ⚛ L1-009 ⊙ Testnet

Two-Photon Microscopy (chi-3 NLO excitation)

Microscopy · Non-linear optical deep tissue imaging

⬇ Download bundle 🚀 Submit to mainnet — earn 100 PWM + 5% royalty

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-009.md

# ⚛ L1 Principle — Two-Photon Microscopy (chi-3 NLO excitation) **ID:** `L1-009` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Microscopy — *Non-linear optical deep tissue imaging* > **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** 3D intensity > **📡 Carrier:** photon · **🌫 Noise:** shot poisson > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41554154 --- ## 🧠 1. Introduction **Two-Photon Microscopy (chi-3 NLO excitation)** is a **nonlinear inverse problem** whose unknown lives in **3D intensity** space, within the **Non-linear optical deep tissue imaging** sub-domain of **Microscopy**. Measurements consist of photons collected by an optical detector via a **two photon nlo** sensing mechanism. The forward operator applies, in order: L · excitation · nonlinear chi3 operator; ordered pixel-by-pixel sampling; detector accumulates flux over the exposure window. Observations are corrupted by Poisson shot noise from quantum-limited detection. Existence of the recovered 3D intensity is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 15); pulse_compression_drift dominates the stability cliff; sample_scattering and the remaining mismatch parameters contribute higher-order bias terms. Photon-shot-noise-limited (poisson counting) sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point. ## ⚙ 2. Forward Model Physical chain: **x** → L · excitation · nonlinear chi3 → Raster scan → Temporal integration → **y** (detector). ``` y = ∫_t dt S_raster `L.excitation.nonlinear_chi3` x, measurements ~ Poisson(αy) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `L.excitation.nonlinear_chi3` | L · excitation · nonlinear chi3 operator | | `S.scan.raster` | Ordered pixel-by-pixel sampling | | `int.temporal` | Detector accumulates flux over the exposure window | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Microscopy | | Sub domain | Non-linear optical deep tissue imaging | | Carrier | photon | | Problem class | nonlinear_inverse | | Solution space | 3D_intensity | | Noise model | shot_poisson | | Integration axis | temporal | | Difficulty delta | 5 | | L dag | 3.5 | ## 📡 4. Measurement Model Existence of the recovered 3D intensity is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 15); pulse_compression_drift dominates the stability cliff; sample_scattering and the remaining mismatch parameters contribute higher-order bias terms. Photon-shot-noise-limited (poisson counting) sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point. | Metric | Value | |---|---| | Metric | PSNR_dB | | Secondary | SSIM | ## 📏 5. Operating Range (Ω) **Center problem class:** `twophoton` · **Forward operator:** `twophoton_forward` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 512 | | W | px | 512 | | Z | — | 100 | | Na | — | 1 | | Pixel nm | nm | 400 | | Lambda ex nm | nm | 920 | | Peak photons | photons | 200 | | Pulse width fs | — | 150 | | Sample scattering | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 128 – 2048 | | W | px | 128 – 2048 | | Z | — | 16 – 512 | | Na | — | 0.6 – 1.1 | | Pixel nm | nm | 200 – 1000 | | Lambda ex nm | nm | 800 – 1060 | | Peak photons | photons | 20 – 2000 | | Pulse width fs | — | 50 – 500 | | Sample scattering | — | 0.0 – 0.5 | | Laser pointing jitter | — | 0.0 – 0.02 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 25.0 | Metric | Range | |---|---| | Psnr db | 12.0 – 42.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `300` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x435c478b52cf1037fadff4c7b460c6bab0766e1d7f546f116ba5292006bd69fb` - **Chain tx hash:** `0x97c63d098b3b153461432279e78ea19fc3313dc98b4d2a1ce1b77fb99c46fdb8` - **Chain block:** `41554154` --- ## File Mapping This bundle consists of: `L1-009.md`, `L1-009.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-009.md` | Source of truth — edit this | Human or LLM | | `L1-009.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-009`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

📝 Polish this artifact in your LLM

Download the files on the right.
Paste the Markdown into ChatGPT / Claude / Gemini and edit freely.
Send your edited Markdown back with the prompt below.
Save the regenerated JSON, then submit via /submit.

Prompt to give your LLM

Read the attached Markdown (L1-009.md).
Regenerate L1-009.json so every field matches the Markdown.
Preserve the schema documented in the File Mapping footer at the bottom of the MD.
Output each file in its own fenced code block tagged with the filename.

⌥ Hierarchy

Upstream lineage and downstream children

↓ L2 Spec 1 child ▾

L2-009-001 Two-Photon Microscopy (chi-3 NLO excitation) Mismatch-Only Spec

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-009",
  "chain_block": 41554154,
  "chain_hash": "0x435c478b52cf1037fadff4c7b460c6bab0766e1d7f546f116ba5292006bd69fb",
  "chain_tx_hash": "0x97c63d098b3b153461432279e78ea19fc3313dc98b4d2a1ce1b77fb99c46fdb8",
  "domain": "Microscopy",
  "hardness_fn": {
    "delta": 5,
    "kappa": 300,
    "metric": "PSNR_dB",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "PSNR_dB",
    "regime": "Existence of the recovered 3D intensity is guaranteed within the declared Omega bounds. Uniqueness is local rather than global (non-convex landscape); convergence depends on initialisation and priors. Stability is moderately conditioned (kappa_eff ~= 15); pulse_compression_drift dominates the stability cliff; sample_scattering and the remaining mismatch parameters contribute higher-order bias terms. Photon-shot-noise-limited (poisson counting) sets the irreducible data-fidelity floor, while mild Tikhonov or analytic inversion is sufficient at the nominal Omega point.",
    "secondary": "SSIM"
  },
  "physics_fingerprint": {
    "L_DAG": 3.5,
    "carrier": "photon",
    "difficulty_delta": 5,
    "domain": "Microscopy",
    "integration_axis": "temporal",
    "noise_model": "shot_poisson",
    "primitives": [
      "L.excitation.nonlinear_chi3",
      "S.scan.raster",
      "int.temporal"
    ],
    "problem_class": "nonlinear_inverse",
    "sensing_mechanism": "two_photon_nlo",
    "solution_space": "3D_intensity",
    "sub_domain": "Non-linear optical deep tissue imaging",
    "title": "Two-Photon Microscopy (chi-3 NLO excitation)"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "twophoton_forward"
    ],
    "allowed_omega_dimensions": [
      "H",
      "W",
      "Z",
      "pixel_nm",
      "NA",
      "lambda_ex_nm",
      "peak_photons",
      "pulse_width_fs",
      "sample_scattering",
      "pulse_compression_drift",
      "sample_scattering",
      "laser_pointing_jitter"
    ],
    "allowed_problem_classes": [
      "twophoton"
    ],
    "center_spec": {
      "epsilon_fn_center": "25.0",
      "forward_operator": "twophoton_forward",
      "input_format": "measurement_only",
      "omega": {
        "H": 512,
        "NA": 1.0,
        "W": 512,
        "Z": 100,
        "lambda_ex_nm": 920,
        "peak_photons": 200,
        "pixel_nm": 400,
        "pulse_width_fs": 150,
        "sample_scattering": 0.0
      },
      "problem_class": "twophoton"
    },
    "epsilon_bounds": {
      "psnr_db": [
        12.0,
        42.0
      ]
    },
    "omega_bounds": {
      "H": [
        128,
        2048
      ],
      "NA": [
        0.6,
        1.1
      ],
      "W": [
        128,
        2048
      ],
      "Z": [
        16,
        512
      ],
      "lambda_ex_nm": [
        800,
        1060
      ],
      "laser_pointing_jitter": [
        0.0,
        0.02
      ],
      "peak_photons": [
        20,
        2000
      ],
      "pixel_nm": [
        200,
        1000
      ],
      "pulse_width_fs": [
        50,
        500
      ],
      "sample_scattering": [
        0.0,
        0.5
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Non-linear optical deep tissue imaging",
  "title": "Two-Photon Microscopy (chi-3 NLO excitation)"
}

🎁

Earn PWM by promoting this to mainnet

• 100 PWM one-time grant on mainnet promotion
• 5% upstream royalty on every downstream L4 mining event, forever
• Your wallet is enrolled the moment the founder accepts

🚀 Submit to mainnet →

How: Download the bundle above → polish the Markdown in ChatGPT / Claude / Gemini → submit your version → founder reviews → mainnet.