decay_accelerator.py

#!/usr/bin/env python3
"""
LV-Accelerated Decay Module
===========================

Lorentz violation enhanced nuclear decay acceleration for element-agnostic production.
Speeds up β-decay and electron capture transitions by modifying nuclear matrix
elements and phase space factors through LV field engineering.

Key capabilities:
- Accelerate β⁻ decay (e.g. precursor → target + e⁻ + ν̄ₑ)
- Accelerate electron capture (e.g. precursor + e⁻ → target + νₑ)
- Modify decay constants by factors of 10³-10⁶
- Convert hours/days half-lives to seconds/minutes
- Element-agnostic: configurable for any target isotope
"""

import numpy as np
import logging
import json
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
try:
    from .energy_ledger import EnergyLedger
except ImportError:
    from energy_ledger import EnergyLedger

# Physical constants
HBAR = 1.055e-34      # J·s
C_LIGHT = 2.998e8     # m/s
ELECTRON_MASS = 0.511 # MeV
NUCLEON_MASS = 938.3  # MeV

@dataclass
class DecayConfig:
    """Configuration for LV-accelerated decay."""
    # Target configuration
    precursor_isotope: str = "Ru-103"    # Precursor isotope to decay
    target_isotope: str = "Au-197"       # Target isotope to produce
    
    # LV parameters
    mu_lv: float = 1e-17      # CPT violation coefficient
    alpha_lv: float = 1e-14   # Spatial anisotropy coefficient
    beta_lv: float = 1e-11    # Temporal variation coefficient
    
    # Decay acceleration parameters
    field_strength: float = 1e8    # V/m (strong electric field)
    magnetic_field: float = 10.0   # Tesla
    confinement_volume: float = 1e-9  # m³ (1 mm³)
    acceleration_time: float = 1.0    # seconds
    
    # Collection efficiency
    collection_efficiency: float = 0.95  # 95% collection

class DecayAccelerator:
    """
    LV-enhanced nuclear decay accelerator for element-agnostic transmutation.
    """
    
    def __init__(self, config: DecayConfig, energy_ledger: Optional[EnergyLedger] = None):
        self.config = config
        self.energy_ledger = energy_ledger or EnergyLedger()
        self.logger = logging.getLogger(__name__)
        
        # Load configuration
        self._load_config()
        
        # Calculate enhancement factors
        self.lv_factors = self._calculate_lv_enhancements()
        
        self.logger.info(f"DecayAccelerator initialized for {config.target_isotope}")
        self.logger.info(f"LV acceleration factor: {self.lv_factors['total']:.2e}×")
    
    def _load_config(self):
        """Load configuration from config.json if available."""
        try:
            with open("config.json", "r") as f:
                cfg = json.load(f)
                self.config.target_isotope = cfg.get("target_isotope", self.config.target_isotope)
        except FileNotFoundError:
            self.logger.info("No config.json found, using default configuration")
        except Exception as e:
            self.logger.warning(f"Error loading config.json: {e}")
    
    def _calculate_lv_enhancements(self) -> Dict[str, float]:
        """Calculate LV enhancement factors for decay acceleration."""
        # Matrix element enhancement from μ coefficient
        matrix_enhancement = 1.0 + abs(self.config.mu_lv) / 1e-18 * 100  # Up to 100× enhancement
        
        # Phase space enhancement from β coefficient  
        phase_space_enhancement = 1.0 + abs(self.config.beta_lv) / 1e-12 * 50  # Up to 50× enhancement
        
        # Field enhancement from α coefficient
        field_enhancement = 1.0 + abs(self.config.alpha_lv) / 1e-15 * 20  # Up to 20× enhancement
        
        total_enhancement = matrix_enhancement * phase_space_enhancement * field_enhancement
        
        return {
            'matrix': matrix_enhancement,
            'phase_space': phase_space_enhancement,
            'field': field_enhancement,
            'total': total_enhancement
        }
    
    def simulate_decay(self, nuclei_yields: Dict[str, float], t: float = 1.0) -> Dict[str, float]:
        """
        Simulate LV-accelerated decay process.
        
        Args:
            nuclei_yields: Input nuclei from spallation
            t: Decay time (seconds)
            
        Returns:
            Dict of final nucleus counts after decay
        """
        print(f"\n=== DECAY ACCELERATION ===")
        print(f"Target: {self.config.target_isotope}")
        print(f"Acceleration time: {t:.1f} s")
        print(f"LV enhancement: {self.lv_factors['total']:.2e}×")
        
        final_yields = {}
        
        for isotope, initial_nuclei in nuclei_yields.items():
            if isotope == "total":
                continue
            
            # For simplicity, assume input nuclei are already target isotopes
            # In a real implementation, we would model specific decay chains
            if isotope == self.config.target_isotope:
                # No decay needed
                final_yields[isotope] = initial_nuclei
            else:
                # Simple decay model: convert to target isotope
                decay_efficiency = self._calculate_decay_efficiency(t)
                converted_nuclei = initial_nuclei * decay_efficiency
                
                if self.config.target_isotope not in final_yields:
                    final_yields[self.config.target_isotope] = 0
                final_yields[self.config.target_isotope] += converted_nuclei
                
                print(f"\n{isotope} -> {self.config.target_isotope}:")
                print(f"  Initial nuclei: {initial_nuclei:.2e}")
                print(f"  Decay efficiency: {decay_efficiency:.2%}")
                print(f"  Converted nuclei: {converted_nuclei:.2e}")
        
        return final_yields
    
    def _calculate_decay_efficiency(self, time: float) -> float:
        """Calculate decay efficiency over given time."""
        # Base decay constant (assume 1 hour natural half-life)
        natural_lambda = np.log(2) / 3600  # 1/s
        
        # LV-enhanced decay constant
        enhanced_lambda = natural_lambda * self.lv_factors['total']
        
        # Decay probability
        decay_probability = 1 - np.exp(-enhanced_lambda * time)
        
        # Apply collection efficiency
        efficiency = decay_probability * self.config.collection_efficiency
        
        return min(0.99, efficiency)  # Cap at 99%