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aNEOS User Workflow Guide

Getting Started

System Launch

cd aneos-project
python3 aneos_menu.py

The system will initialize and display the main mission control interface.

Primary Workflows

🎯 Quick Analysis: Single NEO Investigation

Navigation: Main Menu → 1 (NEO Detection) → 1 (Individual Object Analysis)

Purpose: Deep analysis of a specific NEO with comprehensive validation

Steps:

  1. Enter NEO designation (e.g., "2024 AB1", "Apophis")
  2. System fetches orbital data from multiple sources
  3. XVIII SWARM performs automated anomaly scoring
  4. Multi-stage validation pipeline processes the object
  5. Results displayed with confidence scores and detailed analysis

Expected Output:

  • Overall anomaly score (0.0-1.0)
  • Detailed breakdown across 6 clue categories
  • Validation results from all analysis systems
  • Professional report with visualizations

🔄 Continuous Monitoring: Complete Pipeline Analysis

Navigation: Main Menu → 1 (NEO Detection) → 3 (Continuous Monitoring)

Purpose: Large-scale analysis with 200-year historical polling and automatic review

Process Overview:

📊 Historical Data Polling     ████████████ 100% ✅ Processing chunks...
🧠 XVIII SWARM First-Stage    ████████████ 100% ✅ Analyzing candidates...  
🔬 Multi-Stage Validation     ████████████ 100% ✅ Validating objects...
👨‍🔬 Expert Review Queue       ████████████ 100% ✅ Preparing final list...

What Happens:

  1. Historical Polling: System retrieves 200 years of NEO data in intelligent chunks
  2. XVIII SWARM Review: Automatic scoring identifies ~5,000 candidates from ~50,000 objects
  3. Multi-Stage Validation: Comprehensive analysis reduces to ~500 validated candidates
  4. Expert Queue: Final refinement produces ~50 high-priority investigation targets

Results Location: neo_data/pipeline_results/pipeline_result_[timestamp].json

📈 System Status & Health Monitoring

Navigation: Main Menu → 7 (System Diagnostics) → Various options

Available Diagnostics:

  • Component Status: Check all SWARM systems availability
  • API Health: Verify NASA/ESA data source connectivity
  • Performance Metrics: Review processing speed and efficiency
  • Cache Status: Monitor data storage and cleanup needs

🎓 Learning Mode: Educational Workflows

Navigation: Main Menu → 8 (Learning Center)

Features:

  • System Overview: Interactive introduction to aNEOS capabilities
  • Anomaly Glossary: Definitions of orbital characteristics and analysis terms
  • Workflow Tutorials: Step-by-step guides for different user types
  • Sample Analysis: Pre-configured examples with known anomalous objects

User Types & Recommended Workflows

🔬 Research Scientists

Primary Workflow: Continuous Monitoring for population studies Navigation: Menu → 1 → 3 (200-year analysis) Benefits:

  • Publication-ready statistical validation
  • Comprehensive false positive prevention
  • Reproducible results with audit trails
  • Professional visualizations and reports

Advanced Options:

  • Custom time period analysis: Menu → 1 → 4
  • Individual object deep-dive: Menu → 1 → 1
  • Database export: Menu → 5 → Various export options

🎓 Graduate Students & Amateur Astronomers

Primary Workflow: Individual Object Analysis for learning Navigation: Menu → 1 → 1 (Single object analysis) Benefits:

  • Educational explanations of analysis methods
  • Detailed scoring breakdowns
  • Clear visualization of anomaly indicators
  • Safe learning environment with guided tutorials

Learning Path:

  1. Start with Learning Center (Menu → 8)
  2. Analyze famous NEOs (Apophis, Bennu, Ryugu)
  3. Progress to small population studies
  4. Advanced: Custom analysis workflows

🛡️ Planetary Defense Researchers

Primary Workflow: Targeted object analysis with threat assessment Navigation: Menu → 2 (Mission Intelligence) → Various threat analysis tools Benefits:

  • Rapid assessment of newly discovered objects
  • Automated threat scoring and prioritization
  • Integration with existing planetary defense databases
  • Real-time monitoring capabilities

Data Management Workflows

📁 Results & Data Export

Navigation: Main Menu → 5 (Data Management)

Export Options:

  • CSV Export: Spreadsheet-compatible candidate lists
  • JSON Export: Machine-readable analysis results
  • Scientific Format: Publication-ready data with metadata
  • Visualization Export: High-quality plots and charts

Data Locations:

neo_data/
├── pipeline_results/         # Complete analysis results
├── historical_cache/         # Chunked polling cache (for performance)
├── historical_results/       # Long-term analysis storage
└── exports/                  # User-requested data exports

🧹 Cache & Storage Management

Automatic Management:

  • System automatically manages cache for optimal performance
  • Old results archived with timestamps
  • Intelligent cleanup prevents storage overflow

Manual Management:

  • Menu → 7 (System Diagnostics) → Cache Management
  • Clear specific date ranges or analysis types
  • Export before cleanup for permanent storage

Advanced Workflows

🔧 Custom Analysis Configuration

Navigation: Main Menu → 6 (Advanced Tools) → Pipeline Configuration

Configurable Parameters:

  • Scoring Thresholds: Adjust XVIII SWARM sensitivity
  • Time Periods: Custom historical polling ranges
  • Analysis Depth: Enable/disable specific validation systems
  • Output Formats: Customize result reporting

🌐 API & Integration Workflows

RESTful API (if enabled):

# Start API server
python3 -m aneos_core.api.server

# Example API calls
curl http://localhost:8000/api/analyze/2024AB1
curl http://localhost:8000/api/pipeline/status

Jupyter Integration:

from aneos_core.integration.pipeline_integration import PipelineIntegration
integration = PipelineIntegration()
result = await integration.run_historical_polling_workflow(years_back=10)

Understanding Results

Anomaly Scores

  • 0.0-0.3: Natural orbital characteristics
  • 0.3-0.6: Potentially interesting anomalies
  • 0.6-0.8: Significant anomalies requiring investigation
  • 0.8-1.0: Highly anomalous objects warranting immediate analysis

XVIII SWARM Categories

  1. Orbital Eccentricity: Unusual elliptical patterns
  2. Inclination Anomalies: Non-ecliptic orbital planes
  3. Velocity Patterns: Acceleration/deceleration inconsistencies
  4. Close Approach Regularity: Suspiciously precise return cycles
  5. Thermal Signatures: Unusual heat emission patterns
  6. Radar Characteristics: Anomalous material composition indicators

Validation Pipeline Results

  • KAPPA SWARM: Radar polarization analysis results
  • LAMBDA SWARM: Thermal-IR signature validation
  • CLAUDETTE SWARM: Statistical false positive assessment
  • MU SWARM: Astrometric precision verification
  • Spectral Analysis: Multi-wavelength signature validation

Troubleshooting

Common Issues

"No objects found":

  • Check internet connectivity for NASA API access
  • Verify date ranges are valid
  • Try smaller time periods first

"Pipeline components not available":

  • Ensure all dependencies are installed: pip install -r requirements.txt
  • Check Python version: requires 3.8+
  • Verify file permissions for neo_data/ directory

"Slow performance":

  • System processes large datasets - 200-year polls take 10-30 minutes
  • Use smaller time periods for testing: Menu → 1 → 4 (Custom Analysis)
  • Monitor system resources during large analysis sessions

Performance Optimization

  • First Run: May take longer due to cache building
  • Subsequent Runs: Cache provides significant speedup
  • Memory: 8GB+ recommended for 200-year analysis
  • Storage: 10GB+ recommended for historical caching

Best Practices

For New Users

  1. Start Small: Begin with Learning Center and individual object analysis
  2. Understand Scoring: Review anomaly categories before large-scale analysis
  3. Verify Results: Cross-reference findings with known astronomical databases
  4. Build Experience: Progress from single objects to population studies

For Researchers

  1. Document Parameters: Record analysis configurations for reproducibility
  2. Validate Methods: Understand statistical methods and limitations
  3. Peer Review: Share methodology and results for external validation
  4. Contribute: Report bugs and suggest improvements to the research community

This workflow guide provides comprehensive coverage of the aNEOS platform capabilities, enabling users to effectively leverage the sophisticated analysis systems for scientific research and discovery.