L1 ⚛ L1-482 ⊙ Testnet

Monte Carlo Event Generation

Particle Physics · Collider phenomenology

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

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-482.md

# ⚛ L1 Principle — Monte Carlo Event Generation **ID:** `L1-482` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Particle Physics — *Collider phenomenology* > **🎯 Problem class:** parameter estimation · **🧮 Solution space:** shower parameter vector > **📡 Carrier:** particle · **🌫 Noise:** monte carlo statistical > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41555301 --- ## 🧠 1. Introduction **Monte Carlo Event Generation** is a **parameter-estimation problem** whose unknown lives in **shower parameter vector** space, within the **Collider phenomenology** sub-domain of **Particle Physics**. Measurements consist of particle interactions in a detector via a **parton shower hadronization** sensing mechanism. The forward operator applies, in order: operator inherits structure from a graph (mesh, network); S · hadronization · lund string operator; O · chi2 · event shape operator. Observations are corrupted by monte carlo statistical. Existence of the recovered shower_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 ~= 1000.0); non_perturbative_power_corrections dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Monte carlo statistical sets the irreducible data-fidelity floor. ## ⚙ 2. Forward Model Physical chain: **x** → Structured graph operator → S · hadronization · lund string → O · chi2 · event shape → **y** (detector). ``` y = `O.chi2.event_shape` `S.hadronization.lund_string` G x ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `G.structured` | Operator inherits structure from a graph (mesh, network) | | `S.hadronization.lund_string` | S · hadronization · lund string operator | | `O.chi2.event_shape` | O · chi2 · event shape operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Particle Physics | | Sub domain | Collider phenomenology | | Carrier | particle | | Problem class | parameter_estimation | | Solution space | shower_parameter_vector | | Noise model | monte_carlo_statistical | | Integration axis | phase_space | | Difficulty delta | 5 | | L dag | 4.5 | ## 📡 4. Measurement Model Existence of the recovered shower_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 ~= 1000.0); non_perturbative_power_corrections dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Monte carlo statistical sets the irreducible data-fidelity floor. | Metric | Value | |---|---| | Metric | tune_chi2_per_dof | | Secondary | event_shape_RMSE | ## 📏 5. Operating Range (Ω) **Center problem class:** `parameter_estimation` · **Forward operator:** `parton_shower_hadronization` **Center point:** | Parameter | Unit | Value | |---|---|---| | Energy gev | — | 91.2 | | N observables | — | 50 | | Alpha s range | — | 0.12 | | N events per point | — | 1000 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | Energy gev | — | 14 – 14000 | | N observables | — | 10 – 200 | | Alpha s range | — | 0.1 – 0.14 | | N events per point | — | 100 – 100000 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 1.1 tune_chi2_per_dof | Metric | Range | |---|---| | Tune chi2 per dof | 0.8 – 3.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **tune_chi2_per_dof**, with κ = `100000.0` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x8fc5f8da1f4c2de286421b8fd16cd9fb1776c67066902c4bfbd1ac08cc7cc4c4` - **Chain tx hash:** `0x273b61d986bd867cba824525bc91324edb62d388cfd0fa61c94bad35e96b5aa3` - **Chain block:** `41555301` --- ## File Mapping This bundle consists of: `L1-482.md`, `L1-482.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-482.md` | Source of truth — edit this | Human or LLM | | `L1-482.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 `L1-482`._ _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 (L1-482.md).
Regenerate L1-482.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 ▾

L2-482-001 Monte Carlo Event Generation — Nominal + Mismatch Specs

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-482",
  "chain_block": 41555301,
  "chain_hash": "0x8fc5f8da1f4c2de286421b8fd16cd9fb1776c67066902c4bfbd1ac08cc7cc4c4",
  "chain_tx_hash": "0x273b61d986bd867cba824525bc91324edb62d388cfd0fa61c94bad35e96b5aa3",
  "domain": "Particle Physics",
  "hardness_fn": {
    "delta": 5,
    "kappa": 100000.0,
    "metric": "tune_chi2_per_dof",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "tune_chi2_per_dof",
    "regime": "Existence of the recovered shower_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 ~= 1000.0); non_perturbative_power_corrections dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Monte carlo statistical sets the irreducible data-fidelity floor.",
    "secondary": "event_shape_RMSE"
  },
  "physics_fingerprint": {
    "L_DAG": 4.5,
    "carrier": "particle",
    "difficulty_delta": 5,
    "domain": "Particle Physics",
    "integration_axis": "phase_space",
    "noise_model": "monte_carlo_statistical",
    "primitives": [
      "G.structured",
      "S.hadronization.lund_string",
      "O.chi2.event_shape"
    ],
    "problem_class": "parameter_estimation",
    "sensing_mechanism": "parton_shower_hadronization",
    "solution_space": "shower_parameter_vector",
    "sub_domain": "Collider phenomenology",
    "title": "Monte Carlo Event Generation"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "parton_shower_hadronization"
    ],
    "allowed_omega_dimensions": [
      "N_observables",
      "N_events_per_point",
      "alpha_s_range",
      "energy_GeV"
    ],
    "allowed_problem_classes": [
      "parameter_estimation"
    ],
    "center_spec": {
      "epsilon_fn_center": "1.1 tune_chi2_per_dof",
      "forward_operator": "parton_shower_hadronization",
      "input_format": "measurement_only",
      "omega": {
        "N_events_per_point": 1000,
        "N_observables": 50,
        "alpha_s_range": 0.12,
        "energy_GeV": 91.2
      },
      "problem_class": "parameter_estimation"
    },
    "epsilon_bounds": {
      "tune_chi2_per_dof": [
        0.8,
        3.0
      ]
    },
    "omega_bounds": {
      "N_events_per_point": [
        100,
        100000
      ],
      "N_observables": [
        10,
        200
      ],
      "alpha_s_range": [
        0.1,
        0.14
      ],
      "energy_GeV": [
        14,
        14000
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Collider phenomenology",
  "title": "Monte Carlo Event Generation"
}

🎁

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.