L1 ⚛ L1-372 ⊙ Testnet

Supernova Light Curve Fitting

Astrophysics · Transient photometry

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⬇ L1-372.md

# ⚛ L1 Principle — Supernova Light Curve Fitting **ID:** `L1-372` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Astrophysics — *Transient photometry* > **🎯 Problem class:** parameter estimation · **🧮 Solution space:** lc parameter vector > **📡 Carrier:** photon · **🌫 Noise:** observation gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41555177 --- ## 🧠 1. Introduction **Supernova Light Curve Fitting** is a **parameter-estimation problem** whose unknown lives in **lc parameter vector** space, within the **Transient photometry** sub-domain of **Astrophysics**. Measurements consist of photons collected by an optical detector via a **photometric lc sampling** sensing mechanism. The forward operator applies, in order: operator inherits structure from a graph (mesh, network); O · chi2 · lightcurve operator; S · k correction · photometric operator. Observations are corrupted by observation gaussian. Existence of the recovered lc_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 20); host_galaxy_dust_uncertainty dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Observation gaussian sets the irreducible data-fidelity floor. ## ⚙ 2. Forward Model Physical chain: **x** → Structured graph operator → O · chi2 · lightcurve → S · k correction · photometric → **y** (detector). ``` y = `S.k_correction.photometric` `O.chi2.lightcurve` G x ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `G.structured` | Operator inherits structure from a graph (mesh, network) | | `O.chi2.lightcurve` | O · chi2 · lightcurve operator | | `S.k_correction.photometric` | S · k correction · photometric operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Astrophysics | | Sub domain | Transient photometry | | Carrier | photon | | Problem class | parameter_estimation | | Solution space | lc_parameter_vector | | Noise model | observation_gaussian | | Integration axis | temporal_photometric | | Difficulty delta | 3 | | L dag | 2.8 | ## 📡 4. Measurement Model Existence of the recovered lc_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 20); host_galaxy_dust_uncertainty dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Observation gaussian sets the irreducible data-fidelity floor. | Metric | Value | |---|---| | Metric | distance_modulus_RMSE_mag | | Secondary | Hubble_residual_sigma | ## 📏 5. Operating Range (Ω) **Center problem class:** `parameter_estimation` · **Forward operator:** `photometric_lc_sampling` **Center point:** | Parameter | Unit | Value | |---|---|---| | N bands | bands | 4 | | N epochs | — | 20 | | Redshift z | — | 0.5 | | Photometric noise mmag | — | 5 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | N bands | bands | 3 – 6 | | N epochs | — | 5 – 100 | | Redshift z | — | 0.01 – 2.0 | | Photometric noise mmag | — | 1.0 – 50.0 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 0.12 distance_modulus_RMSE_mag | Metric | Range | |---|---| | Distance modulus rmse mag | 0.05 – 0.3 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **distance_modulus_RMSE_mag**, with κ = `500` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x40624c6a7dafde3ec3b1fff82eadd3e283922ec56280673b45ff6155a8d8d5cf` - **Chain tx hash:** `0x505569141ed7a5a60baaac8f740eaa546ffd55cbac6dd980b55ccc3c0a6fcaa6` - **Chain block:** `41555177` --- ## File Mapping This bundle consists of: `L1-372.md`, `L1-372.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-372.md` | Source of truth — edit this | Human or LLM | | `L1-372.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 `L1-372`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

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Read the attached Markdown (L1-372.md).
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L2-372-001 Supernova Light Curve Fitting — Nominal + Mismatch Specs

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📋 JSON metadata

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  "chain_tx_hash": "0x505569141ed7a5a60baaac8f740eaa546ffd55cbac6dd980b55ccc3c0a6fcaa6",
  "domain": "Astrophysics",
  "hardness_fn": {
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    "kappa": 500,
    "metric": "distance_modulus_RMSE_mag",
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    "regime": "Existence of the recovered lc_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 20); host_galaxy_dust_uncertainty dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Observation gaussian sets the irreducible data-fidelity floor.",
    "secondary": "Hubble_residual_sigma"
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  "physics_fingerprint": {
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    "carrier": "photon",
    "difficulty_delta": 3,
    "domain": "Astrophysics",
    "integration_axis": "temporal_photometric",
    "noise_model": "observation_gaussian",
    "primitives": [
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      "O.chi2.lightcurve",
      "S.k_correction.photometric"
    ],
    "problem_class": "parameter_estimation",
    "sensing_mechanism": "photometric_lc_sampling",
    "solution_space": "lc_parameter_vector",
    "sub_domain": "Transient photometry",
    "title": "Supernova Light Curve Fitting"
  },
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    "allowed_omega_dimensions": [
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      "N_bands",
      "photometric_noise_mmag",
      "redshift_z"
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    "allowed_problem_classes": [
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    ],
    "center_spec": {
      "epsilon_fn_center": "0.12 distance_modulus_RMSE_mag",
      "forward_operator": "photometric_lc_sampling",
      "input_format": "measurement_only",
      "omega": {
        "N_bands": 4,
        "N_epochs": 20,
        "photometric_noise_mmag": 5.0,
        "redshift_z": 0.5
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      "problem_class": "parameter_estimation"
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    "epsilon_bounds": {
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        0.05,
        0.3
      ]
    },
    "omega_bounds": {
      "N_bands": [
        3,
        6
      ],
      "N_epochs": [
        5,
        100
      ],
      "photometric_noise_mmag": [
        1.0,
        50.0
      ],
      "redshift_z": [
        0.01,
        2.0
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Transient photometry",
  "title": "Supernova Light Curve Fitting"
}

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