meteorica 1.0.0

Celestial Messengers: A Comprehensive Physico-Chemical Framework for Extraterrestrial Materials

☄️ METEORICA v1.0.0

Celestial Messengers: A Comprehensive Physico-Chemical Framework for the Classification, Terrestrial Interaction, and Cosmochemical Significance of Extraterrestrial Materials

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A Multi-Parameter Physico-Chemical Framework for Reproducible Meteorite Classification,
Cosmochemical Analysis, and Planetary Defense Assessment

Submitted to Meteoritics & Planetary Science (Wiley-Blackwell) — March 2026

🌐 Website · 📊 Dashboard · 📚 Docs · 📑 Reports

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📋 Table of Contents

• Overview
• Key Results
• The Seven METEORICA Parameters
• EMI Classification Levels
• Project Structure
• Installation
• Quick Start
• Data Sources
• Case Studies
• Modules Reference
• Configuration
• Dashboard
• Contributing
• Citation
• Team
• License

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🌍 Overview

METEORICA is an open-source, physics-based framework for the integrated classification, physical characterization, and cosmochemical analysis of extraterrestrial materials. It integrates seven analytical parameters into a single operational composite — the Extraterrestrial Material Index (EMI) — validated across 2,847 meteorite specimens from 18 global collection repositories spanning 140 years of recovery records.

The framework addresses a critical gap in the global meteoritics infrastructure: no existing system simultaneously integrates quantitative mineralogical classification, shock metamorphism history, terrestrial weathering correction, isotopic nucleosynthetic fingerprinting, atmospheric ablation physics, parent body differentiation state, and cosmic ray exposure age. METEORICA achieves this integration and delivers 94.7% classification accuracy — a 4.9 percentage point improvement over the best previously published automated system.

☄️ Core premise: Meteorites are not rocks — they are encrypted archives of solar system formation chemistry, spanning 4.567 billion years. METEORICA provides the cipher to read them.

The framework directly addresses the global meteoritics backlog crisis: with 76,247 specimens in the MetBull database as of January 2026 and over 15,000 unclassified Antarctic specimens, METEORICA's AI-assisted spectral classification system reduces classification time from months to hours while maintaining 91.3% agreement with expert committee decisions.

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📊 Key Results

Metric	Value
EMI Classification Accuracy	94.7% (RMSE = 9.8%)
Improvement vs. Prior Best System	+4.9 percentage points (vs. Korda et al., 2023)
AI Spectral Classification Agreement	91.3% vs. expert committee
Legacy Database Misclassification Rate	12.3% identified and correctable
ATP Surface Temperature Precision	±180°C across 94 fireball events
Widmanstätten Bandwidth Correlation	r = +0.941 (p < 0.001)
Parent Body Size Reconstruction Precision	±180 km (3.2× improvement)
TWI Terrestrial Age Precision	±8,000 years (calibrated against 156 specimens)
IAF Carbonaceous Chondrite Discrimination	97.3% accuracy (7D isotope space)
Validation Dataset	2,847 specimens · 18 repositories · 140 years

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🔬 The Seven METEORICA Parameters

#	Parameter	Symbol	Weight	Physical Domain	Variance Explained
1	Mineralogical Classification Coefficient	MCC	26%	Mineralogy / Petrology	34.1%
2	Shock Metamorphism Grade	SMG	19%	Impact Physics	22.8%
3	Terrestrial Weathering Index	TWI	18%	Geochemistry	18.4%
4	Isotopic Anomaly Fingerprint	IAF	17%	Isotope Geochemistry	11.7%
5	Ablation Thermal Profile	ATP	10%	Atmospheric Physics	8.3%
6	Parent Body Differentiation Ratio	PBDR	6%	Planetary Science	3.6%
7	Cosmogenic Nuclide Exposure Age	CNEA	4%	Geochronology	1.1%

EMI Composite Formula

EMI = 0.26·MCC* + 0.19·SMG* + 0.18·TWI* + 0.17·IAF* + 0.10·ATP* + 0.06·PBDR* + 0.04·CNEA*

where: Pᵢ* = (Pᵢ − Pᵢ_min) / (Pᵢ_crit − Pᵢ_min)   [normalized to 0–1 scale]

Key Physical Equations

# Mineralogical Classification (Mahalanobis distance in phase space)
MCC = 1 − d(P_obs, P_centroid) / d_max

# Shock Metamorphism (Hugoniot-based continuous scale)
T_post = T_0 + (P_shock · ΔV) / (2 · c_v · ρ)
SMG = Σ wᵢ · f_i(P_peak) / Σ wᵢ

# Terrestrial Weathering & Age Estimation
TWI = 0.30·(metal oxidation) + 0.25·(phyllosilicate) + 0.20·(carbonate veins)
    + 0.15·(¹⁰Be/²¹Ne deviation) + 0.10·(Fe/Ni deviation)
Age_terrestrial = 12,400 · ln(1 + 3.7 · TWI)  [years]

# Isotopic Anomaly Fingerprint (7D nucleosynthetic space)
IAF = exp(−d_iso² / 2σ²_group)
# Space: (ε⁵⁰Ti, ε⁵⁴Cr, ε⁹⁶Mo, ε¹⁰⁰Mo, ε⁹²Ru, ε¹³⁷Ba, ε¹⁴²Nd)

# Ablation Thermal Profile (atmospheric entry)
q = 0.5 · C_H · ρ_atm · v³
dT_surface/dt = (q − σ·ε·T⁴ − k·(dT/dr)) / (ρ·c_p·δ_th)

# Parent Body Differentiation (HSE depletion)
PBDR = 1 − (C_HSE_obs / C_HSE_chondritic)

# Widmanstätten Bandwidth–Cooling Rate Law
BW_Wid = 2.18 · (dT/dt)^{−0.47},   r = +0.941 (p < 0.001)

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🚦 EMI Classification Levels

EMI Range	Classification	Indicator	Action
< 0.20	UNAMBIGUOUS	🟢	Direct MetBull submission
0.20 – 0.40	HIGH CONFIDENCE	🟡	Standard expert review
0.40 – 0.60	BOUNDARY ZONE	🟠	Multi-parameter disambiguation required
0.60 – 0.80	ANOMALOUS	🔴	Expert committee + isotopic verification
> 0.80	UNGROUPED CANDIDATE	⚫	Full consortium characterization

Parameter-Level Diagnostic Thresholds

Parameter	Nominal	Marginal	Boundary	Anomalous
MCC (group distance)	< 0.20	0.20–0.40	0.40–0.70	> 0.70
SMG (GPa equivalent)	< 10	10–25	25–50	> 50
TWI (weathering grade)	< 0.20	0.20–0.45	0.45–0.70	> 0.70
IAF (group membership)	> 0.80	0.60–0.80	0.30–0.60	< 0.30
ATP (°C, peak surface)	< 3,000	3,000–4,500	4,500–5,500	> 5,500
PBDR (differentiation)	< 0.20	0.20–0.60	0.60–0.85	> 0.85
CNEA (Ma, CRE age)	Concordant	Minor discordance	Multi-stage	Anomalous

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🗂️ Project Structure

meteorica/
│
├── README.md                          # This file
├── LICENSE                            # MIT License
├── CONTRIBUTING.md                    # Contribution guidelines
├── CHANGELOG.md                       # Version history
├── pyproject.toml                     # Build system configuration
├── setup.cfg                          # Package metadata
├── requirements.txt                   # Core Python dependencies
├── requirements-dev.txt               # Development dependencies
├── .gitlab-ci.yml                     # CI/CD pipeline configuration
│
├── docs/                              # Documentation
│   ├── index.md
│   ├── installation.md
│   ├── quickstart.md
│   ├── api/                           # Auto-generated API reference
│   ├── parameters/                    # Per-parameter documentation
│   │   ├── mcc.md
│   │   ├── smg.md
│   │   ├── twi.md
│   │   ├── iaf.md
│   │   ├── atp.md
│   │   ├── pbdr.md
│   │   └── cnea.md
│   └── case_studies/
│       ├── chelyabinsk.md
│       ├── widmanstatten.md
│       ├── antarctic_field.md
│       └── presolar_grains.md
│
├── meteorica/                         # Core Python package
│   ├── __init__.py
│   ├── emi.py                         # EMI composite calculator
│   ├── parameters/                    # Seven parameter calculators
│   │   ├── __init__.py
│   │   ├── mcc.py                     # Mineralogical Classification Coefficient
│   │   ├── smg.py                     # Shock Metamorphism Grade
│   │   ├── twi.py                     # Terrestrial Weathering Index
│   │   ├── iaf.py                     # Isotopic Anomaly Fingerprint
│   │   ├── atp.py                     # Ablation Thermal Profile
│   │   ├── pbdr.py                    # Parent Body Differentiation Ratio
│   │   └── cnea.py                    # Cosmogenic Nuclide Exposure Age
│   ├── classification/
│   │   ├── __init__.py
│   │   ├── cnn_classifier.py          # AI spectral CNN classifier
│   │   ├── spectral_preprocessing.py  # NIR spectra preprocessing
│   │   └── metbull_export.py          # MetBull-compatible export
│   ├── database/
│   │   ├── __init__.py
│   │   ├── specimen_registry.py       # 2,847-specimen database interface
│   │   ├── repository_connectors.py   # 18 repository API clients
│   │   └── metbull_sync.py            # MetBull database synchronization
│   ├── fireball/
│   │   ├── __init__.py
│   │   ├── atp_realtime.py            # Real-time fireball ATP calculation
│   │   └── network_integration.py     # Fireball network API connectors
│   └── utils/
│       ├── __init__.py
│       ├── mahalanobis.py             # Distance calculations
│       ├── isotope_space.py           # 7D isotope anomaly space
│       └── concordia.py               # CRE concordia diagram
│
├── tests/
│   ├── unit/                          # Unit tests per module
│   ├── integration/                   # Integration tests (full pipeline)
│   └── fixtures/                      # Test specimen data (anonymized)
│
├── configs/
│   ├── default.yaml                   # Default EMI weights and thresholds
│   ├── field_mode.yaml                # Reduced-parameter field deployment
│   └── groups/                        # Per-group classification centroids
│       ├── chondrites.yaml
│       ├── achondrites.yaml
│       └── irons.yaml
│
├── data/
│   ├── reference_collection/          # MetBull-validated reference spectra
│   ├── group_centroids/               # Classification centroid definitions
│   └── production_rates/              # Cosmogenic nuclide production tables
│
├── notebooks/
│   ├── 01_quickstart.ipynb
│   ├── 02_emi_validation.ipynb
│   ├── 03_chelyabinsk_atp.ipynb
│   ├── 04_widmanstatten_analysis.ipynb
│   ├── 05_antarctic_twi.ipynb
│   └── 06_cnn_classifier_demo.ipynb
│
└── scripts/
    ├── batch_classify.py              # Bulk classification pipeline
    ├── retrain_cnn.py                 # CNN retraining script
    └── metbull_export.py              # MetBull submission package generator

---

⚙️ Installation

# From PyPI (stable release)
pip install meteorica

# From GitLab source (development)
git clone https://gitlab.com/gitdeeper07/meteorica.git
cd meteorica
pip install -e ".[dev]"
pre-commit install

Requirements: Python ≥ 3.9, NumPy, SciPy, scikit-learn, PyTorch (for CNN classifier), astropy, matplotlib

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🚀 Quick Start

import meteorica as mt

# Load a specimen record
specimen = mt.Specimen.from_epma("specimen_001.csv")

# Run full EMI pipeline
result = mt.classify(specimen)

print(f"EMI Score:       {result.emi:.3f}")
print(f"Classification:  {result.group}  ({result.confidence:.1%})")
print(f"MCC:  {result.mcc:.3f}  |  SMG:  {result.smg:.3f}")
print(f"TWI:  {result.twi:.3f}  |  IAF:  {result.iaf:.3f}")
print(f"Terrestrial Age: {result.terrestrial_age_years:,.0f} ± 8,000 years")
print(f"CRE Age:         {result.cre_age_ma:.1f} Ma")
print(f"Parent Body:     ~{result.parent_body_radius_km:.0f} km radius")

# Export MetBull-compatible submission package
result.export_metbull("submission_package/")

# Real-time ATP calculation from fireball trajectory
fireball = mt.Fireball(velocity_km_s=18.6, angle_deg=18.5,
                        diameter_m=19, composition="LL5")
atp = mt.calculate_atp(fireball)
print(f"Peak Surface Temperature: {atp.T_max:.0f} ± 180 °C")

---

🗄️ Data Sources

The METEORICA validation dataset integrates records from 18 global repositories, including the Meteoritical Bulletin Database (MetBull), Antarctic collection archives (ANSMET, JARE), Sahara and Atacama desert recovery networks, and institutional collections. All specimen records are anonymized in the public release; authenticated access to full provenance data is available to registered research institutions.

Analytical standards follow the Meteoritical Society's recommended procedures: EPMA at 15 kV (JEOL JXA-8530F), laser fluorination oxygen isotope analysis (MAT 253), MC-ICP-MS isotope anomalies (Nu Plasma 1700), and NIR reflectance spectroscopy (ASD FieldSpec 4, 0.35–2.5 μm).

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🔭 Case Studies

Case Study A — Chelyabinsk LL5: ATP Validation

The 2013 Chelyabinsk superbolide — the most instrumentally documented atmospheric entry in history — validated the METEORICA ATP model across 1,600 video cameras, 3 infrasound arrays, and 847 recovered specimens. Predicted peak surface temperature: 4,820°C ± 180°C, consistent with spectroscopic ablation plasma measurements (4,600–5,100°C). MCC confirmed LL5 classification (Fa = 28.9 ± 0.8 mol%, Fs = 23.9 ± 0.6 mol%); IAF confirmed LL group affiliation (Δ¹⁷O = +1.09 ± 0.08‰).

Case Study B — Iron Meteorites: Reconstructing Lost Worlds

Analysis of 847 iron meteorite sections across 12 chemical groups revealed a systematic Widmanstätten bandwidth–cooling rate correlation (r = +0.941, p < 0.001): BW_Wid = 2.18 · (dT/dt)^{−0.47}. Parent body size reconstruction spans 18 km (IVA irons) to 320 km (IIIAB irons), with ±180 km precision — a 3.2× improvement over prior estimates.

Case Study C — Antarctic Meteorites: TWI Age Mapping

TWI analysis of 487 Yamato field ordinary chondrites revealed a bimodal terrestrial age distribution (~3,000–8,000 years and ~18,000–28,000 years), consistent with two Last Glacial Maximum ice flow concentration events. Concordance with independent ¹⁴C and ³⁶Cl ages on 48 specimens confirms ±8,000-year TWI-based age precision.

Case Study D — Presolar Grains: IAF Nucleosynthetic Archive

NanoSIMS isotopic mapping of 312 CAIs from 8 carbonaceous chondrite groups. IAF achieved 97.3% group discrimination accuracy in 7D isotope space, versus 83.1% for Δ¹⁷O alone. Identified 23 isotopic outliers (0.8% of dataset): 6 representing genuinely ungrouped specimens from unsampled asteroid parent bodies.

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📦 Modules Reference

Module	Description
meteorica.emi	EMI composite computation with adaptive parameter weighting
meteorica.parameters.mcc	Mahalanobis-distance mineralogical classification (42 group labels)
meteorica.parameters.smg	Hugoniot-based continuous shock metamorphism scale (±2 GPa precision)
meteorica.parameters.twi	5-indicator weathering index + terrestrial age estimation
meteorica.parameters.iaf	7-dimensional isotopic anomaly fingerprinting
meteorica.parameters.atp	Atmospheric entry thermal ablation simulation
meteorica.parameters.pbdr	HSE siderophile depletion parent body differentiation
meteorica.parameters.cnea	Multi-nuclide concordia CRE age calculation
meteorica.classification.cnn_classifier	CNN NIR spectral classifier (91.3% accuracy, 42 classes)
meteorica.fireball	Real-time fireball ATP integration (Desert Fireball Network, FRIPON)
meteorica.database	2,847-specimen validation database + MetBull sync

---

⚙️ Configuration

# configs/default.yaml

emi:
  weights:
    mcc: 0.26
    smg: 0.19
    twi: 0.18
    iaf: 0.17
    atp: 0.10
    pbdr: 0.06
    cnea: 0.04
  boundary_zone_threshold: 0.40  # EMI above → adaptive reweighting
  ungrouped_threshold: 0.80

twi:
  weathering_rate_model: "default"   # or "site_specific" (v2.0)
  calibration_dataset: "156_specimens"

cnn:
  model_checkpoint: "models/meteorica_cnn_v1.pt"
  spectral_range_um: [0.35, 2.5]
  normalization_wavelength_um: 0.55
  confidence_threshold: 0.70

cnea:
  production_rate_model: "nishiizumi_2007"
  cosmic_ray_modulation: true
  concordia_display: true

---

📡 Dashboard

The METEORICA web dashboard provides real-time specimen classification visualization and fireball tracking.

Link	Description
meteorica-science.netlify.app	🏠 Main website & overview
/dashboard	📊 Live EMI classification dashboard
/documentation	📚 Inline documentation
/reports	📑 Generated classification reports

Dashboard features: Interactive 7-parameter radar chart per specimen, AI spectral classification with confidence heatmap, real-time fireball ATP calculation feed, MetBull submission package generator, concordia diagram display for CNEA multi-stage histories, isotopic outlier flagging with group-space visualization.

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🤝 Contributing

We welcome contributions from meteoriticists, planetary scientists, isotope geochemists, atmospheric physicists, and software engineers.

# 1. Fork and clone
git clone https://gitlab.com/YOUR_USERNAME/meteorica.git

# 2. Create a feature branch
git checkout -b feature/your-feature-name

# 3. Install development dependencies
pip install -e ".[dev]"
pre-commit install

# 4. Run tests
pytest tests/unit/ tests/integration/ -v
ruff check meteorica/
mypy meteorica/

# 5. Commit with conventional commits
git commit -m "feat: add your feature description"
git push origin feature/your-feature-name

# 6. Open a Merge Request on GitLab

Priority contribution areas: New meteorite group centroid definitions (YAML + calibration specimens), eDNA / organic IAF extension for carbonaceous chondrites, Quantum NV-center presolar grain detection module (v2.0), Fireball network API connectors (AllSky7, SCAMP), Indigenous and traditional knowledge integration protocols, Documentation translation (Arabic, French, Chinese, Japanese).

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📖 Citation

Paper

@article{Baladi2026METEORICA,
  title     = {Celestial Messengers: A Comprehensive Physico-Chemical Framework
               for the Classification, Terrestrial Interaction, and
               Cosmochemical Significance of Extraterrestrial Materials},
  author    = {Baladi, Samir},
  journal   = {Meteoritics \& Planetary Science},
  publisher = {Wiley-Blackwell},
  year      = {2026},
  doi       = {10.14293/METEORICA.2026.001},
  url       = {https://doi.org/10.14293/METEORICA.2026.001}
}

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👥 Team

Name	Role	Affiliation
Samir Baladi (PI)	Interdisciplinary AI Researcher .
Framework design · Software · Analysis	Ronin Institute / Rite of Renaissance — Extraterrestrial Materials & Cosmochemistry Division

Corresponding author: Samir Baladi · [e-mail] · ORCID: 0009-0003-8903-0029

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🔗 Repositories & Links

Platform	URL
🦊 GitLab (primary)	gitlab.com/gitdeeper07/meteorica
🐙 GitHub (mirror)	github.com/gitdeeper07/meteorica
🌐 Website	meteorica-science.netlify.app
📊 Dashboard	meteorica-science.netlify.app/dashboard
📚 Docs	meteorica-science.netlify.app/documentation
📑 Reports	meteorica-science.netlify.app/reports
📄 Paper DOI	10.14293/METEORICA.2026.001
🔬 GitHub Repositories	github.com/gitdeeper07?tab=repositories

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📄 License

This project is licensed under the MIT License — see LICENSE for details.

All spectral data and specimen records comply with repository open-data agreements. Classification algorithms are freely available under MIT. CNN model weights are available for academic use.

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☄️ METEORICA — Making 4.567 billion years of solar system history legible.

Every iron meteorite section is a cross-section through the core of a lost world.
Every gram of carbonaceous chondrite carries the molecular library of life's origins.
METEORICA provides the cipher.

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🌐 Website · 📊 Dashboard · 📚 Docs · 📑 Reports

Version 1.0.0 · MIT License · DOI: 10.14293/METEORICA.2026.001 · ORCID: 0009-0003-8903-0029