antimatter_production_optimization_roadmap.py

#!/usr/bin/env python3
"""
Antimatter Production Optimization Roadmap
==========================================

Production-ready implementation using the existing LQG-polymer-corrected framework
to drive down antimatter pair-production costs and design efficient energy conversion cycles.

This module integrates:
1. 3D parameter sweeps over (μ_g, b, S_inst) for optimal yields
2. Critical field optimization with E_crit^poly reduction
3. Prototype production facility design with beam geometry
4. Matter-to-energy conversion cycles with thermalization efficiency
5. Closed-loop energy management with UQ Monte-Carlo optimization

Mathematical Framework:
- Enhanced Schwinger rate: Γ_total = Γ_Sch^poly + Γ_inst^poly
- Critical field reduction: E_crit^poly = F(μ_g) × E_crit^Sch
- Cost ratio optimization: (Γ_total^poly/Γ_Sch) × (E_Sch²/E_poly²)
- Annihilation efficiency: η_tot = η_th × η_mech × η_elec
"""

import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as optimize
from scipy.interpolate import griddata
from typing import Dict, List, Tuple, Optional
import json
from dataclasses import dataclass
from pathlib import Path

# Constants
ALPHA_EM = 1/137.036  # Fine structure constant
C_LIGHT = 299792458   # m/s
E_CRIT_SCHWINGER = 1.32e18  # V/m
M_ELECTRON = 9.109e-31  # kg
E_ELECTRON = 0.511e6   # eV
HBAR = 1.055e-34      # J·s
E_CHARGE = 1.602e-19   # C

@dataclass
class OptimizationConfig:
    """Configuration for antimatter production optimization"""
    # Parameter ranges for 3D sweep
    mu_g_range: Tuple[float, float] = (0.05, 0.6)
    b_range: Tuple[float, float] = (0.0, 15.0)
    S_inst_range: Tuple[float, float] = (0.0, 10.0)
    
    # Grid resolution
    n_mu_g: int = 25
    n_b: int = 20
    n_S_inst: int = 15
    
    # Test field for calculations
    E_test_field: float = 1e17  # V/m
    
    # Conversion efficiency targets
    target_cost_reduction: float = 100.0  # 100x reduction
    min_conversion_efficiency: float = 0.20  # 20%

class AntimatterProductionOptimizer:
    """
    Advanced antimatter production optimization using LQG-polymer corrections
    """
    
    def __init__(self, config: OptimizationConfig):
        self.config = config
        
        # Generate parameter grid
        self.mu_g_values = np.linspace(*config.mu_g_range, config.n_mu_g)
        self.b_values = np.linspace(*config.b_range, config.n_b)
        self.S_inst_values = np.linspace(*config.S_inst_range, config.n_S_inst)
        
        # Results storage
        self.optimization_results = {}
        self.optimal_parameters = None
        self.cost_reduction_matrix = None
        
        print(f"🎯 Antimatter Production Optimizer Initialized")
        print(f"   Parameter space: {config.n_mu_g} × {config.n_b} × {config.n_S_inst} = {config.n_mu_g * config.n_b * config.n_S_inst:,} points")
        print(f"   Target cost reduction: {config.target_cost_reduction}×")

    def polymer_critical_field_factor(self, mu_g: float) -> float:
        """
        Calculate polymer correction factor F(μ_g) for critical field reduction
        
        E_crit^poly(μ_g) = F(μ_g) × E_crit^Sch
        where F(μ_g) = sin(μ_g√s)/(μ_g√s)
        """
        if mu_g < 1e-6:
            return 1.0  # Limit as μ_g → 0
        
        # Use field energy scale
        sqrt_s = np.sqrt(self.config.E_test_field / E_CRIT_SCHWINGER)
        argument = mu_g * sqrt_s
        
        if argument < 1e-6:
            return 1.0
        
        return np.sin(argument) / argument

    def enhanced_schwinger_rate(self, E_field: float, mu_g: float, b: float) -> float:
        """
        Calculate polymer-corrected Schwinger rate with running coupling
        
        Γ_Sch^poly = (α_eff eE)²/(4π³ℏc) × exp(-πm²c³/(eEℏ) × F(μ_g))
        """
        # Running coupling with β-function
        alpha_eff = ALPHA_EM / (1 - (b / (2 * np.pi)) * ALPHA_EM * np.log(E_field / 1e6))
        alpha_eff = max(alpha_eff, ALPHA_EM)  # Prevent unphysical values
        
        # Polymer threshold reduction factor
        F_polymer = self.polymer_critical_field_factor(mu_g)
        
        # Standard Schwinger exponent with polymer correction
        exponent = -np.pi * M_ELECTRON**2 * C_LIGHT**3 / (E_CHARGE * E_field * HBAR) * F_polymer
        
        # Enhanced rate
        prefactor = (alpha_eff * E_CHARGE * E_field)**2 / (4 * np.pi**3 * HBAR * C_LIGHT)
        
        return prefactor * np.exp(exponent)

    def instanton_contribution(self, E_field: float, mu_g: float, S_inst: float) -> float:
        """
        Calculate polymer-enhanced instanton contribution
        
        Γ_inst^poly = S_inst × Λ_QCD⁴ × exp(-8π²/α_s × sinc²(μ_g Λ_QCD))
        """
        # QCD scale
        Lambda_QCD = 200e6  # eV
        alpha_s = 0.3  # Strong coupling at QCD scale
        
        # Polymer enhancement of instanton action
        sinc_factor = np.sinc(mu_g * Lambda_QCD / np.pi) if mu_g > 0 else 1.0
        instanton_action = 8 * np.pi**2 / alpha_s * sinc_factor**2
        
        # Instanton density with strength parameter
        instanton_rate = S_inst * (Lambda_QCD * 1.602e-19)**4 * np.exp(-instanton_action)
        
        return instanton_rate

    def total_production_rate(self, E_field: float, mu_g: float, b: float, S_inst: float) -> float:
        """
        Total production rate: Γ_total = Γ_Sch^poly + Γ_inst^poly
        """
        schwinger_rate = self.enhanced_schwinger_rate(E_field, mu_g, b)
        instanton_rate = self.instanton_contribution(E_field, mu_g, S_inst)
        
        return schwinger_rate + instanton_rate

    def cost_reduction_factor(self, mu_g: float, b: float, S_inst: float) -> float:
        """
        Calculate cost reduction factor:
        Cost Ratio ≈ (Γ_total^poly/Γ_Sch) × (E_Sch²/E_poly²)
        """
        E_field = self.config.E_test_field
        
        # Rates
        gamma_total_poly = self.total_production_rate(E_field, mu_g, b, S_inst)
        gamma_sch_standard = self.enhanced_schwinger_rate(E_field, 0.0, 0.0)  # Standard case
        
        # Field ratio
        F_polymer = self.polymer_critical_field_factor(mu_g)
        E_poly = E_field * F_polymer
        field_ratio = E_field**2 / E_poly**2 if E_poly > 0 else 1.0
        
        # Total cost reduction
        rate_ratio = gamma_total_poly / gamma_sch_standard if gamma_sch_standard > 0 else 1.0
        cost_reduction = rate_ratio * field_ratio
        
        return cost_reduction

    def execute_3d_parameter_sweep(self) -> Dict:
        """
        Execute comprehensive 3D parameter sweep over (μ_g, b, S_inst)
        """
        print(f"\n🔍 Executing 3D Parameter Sweep")
        print(f"   μ_g ∈ [{self.config.mu_g_range[0]:.2f}, {self.config.mu_g_range[1]:.2f}]")
        print(f"   b ∈ [{self.config.b_range[0]:.1f}, {self.config.b_range[1]:.1f}]")
        print(f"   S_inst ∈ [{self.config.S_inst_range[0]:.1f}, {self.config.S_inst_range[1]:.1f}]")
        
        results = []
        best_cost_reduction = 0.0
        optimal_params = None
        
        total_points = len(self.mu_g_values) * len(self.b_values) * len(self.S_inst_values)
        current_point = 0
        
        for i, mu_g in enumerate(self.mu_g_values):
            for j, b in enumerate(self.b_values):
                for k, S_inst in enumerate(self.S_inst_values):
                    current_point += 1
                    
                    # Calculate metrics
                    cost_reduction = self.cost_reduction_factor(mu_g, b, S_inst)
                    total_rate = self.total_production_rate(self.config.E_test_field, mu_g, b, S_inst)
                    critical_field_factor = self.polymer_critical_field_factor(mu_g)
                    
                    result = {
                        'mu_g': mu_g,
                        'b': b,
                        'S_inst': S_inst,
                        'cost_reduction': cost_reduction,
                        'total_rate': total_rate,
                        'critical_field_factor': critical_field_factor,
                        'grid_indices': (i, j, k)
                    }
                    
                    results.append(result)
                    
                    # Track best result
                    if cost_reduction > best_cost_reduction:
                        best_cost_reduction = cost_reduction
                        optimal_params = (mu_g, b, S_inst)
                    
                    # Progress update
                    if current_point % 500 == 0 or current_point == total_points:
                        progress = 100 * current_point / total_points
                        print(f"   Progress: {progress:5.1f}% | Best cost reduction: {best_cost_reduction:.2e}")
        
        self.optimization_results = {
            'sweep_results': results,
            'optimal_parameters': optimal_params,
            'best_cost_reduction': best_cost_reduction,
            'parameter_ranges': {
                'mu_g': self.config.mu_g_range,
                'b': self.config.b_range,
                'S_inst': self.config.S_inst_range
            }
        }
        
        print(f"\n✅ 3D Parameter Sweep Complete")
        print(f"   Optimal parameters: μ_g={optimal_params[0]:.3f}, b={optimal_params[1]:.1f}, S_inst={optimal_params[2]:.1f}")
        print(f"   Maximum cost reduction: {best_cost_reduction:.2e}×")
        
        return self.optimization_results

    def find_inexpensive_regimes(self, min_cost_reduction: float = None) -> List[Dict]:
        """
        Identify parameter regimes that minimize critical field while maximizing yield
        """
        if not self.optimization_results:
            raise ValueError("Must run 3D parameter sweep first")
        
        min_reduction = min_cost_reduction or self.config.target_cost_reduction
        
        print(f"\n🎯 Finding Inexpensive Parameter Regimes")
        print(f"   Minimum cost reduction threshold: {min_reduction}×")
        
        results = self.optimization_results['sweep_results']
        
        # Filter results meeting cost reduction criteria
        good_regimes = [r for r in results if r['cost_reduction'] >= min_reduction]
        
        if not good_regimes:
            print(f"   ⚠️  No regimes found meeting {min_reduction}× threshold")
            # Relax criteria
            min_reduction = max([r['cost_reduction'] for r in results]) * 0.8
            good_regimes = [r for r in results if r['cost_reduction'] >= min_reduction]
            print(f"   📉 Relaxed threshold to {min_reduction:.2e}× - found {len(good_regimes)} regimes")
        
        # Sort by cost reduction (best first)
        good_regimes.sort(key=lambda x: x['cost_reduction'], reverse=True)
        
        print(f"   ✅ Found {len(good_regimes)} parameter regimes meeting criteria")
        
        # Report top 5 regimes
        print(f"\n   🏆 Top 5 Parameter Regimes:")
        for i, regime in enumerate(good_regimes[:5]):
            print(f"   {i+1}. μ_g={regime['mu_g']:.3f}, b={regime['b']:.1f}, S_inst={regime['S_inst']:.1f}")
            print(f"      Cost reduction: {regime['cost_reduction']:.2e}×")
            print(f"      Total rate: {regime['total_rate']:.2e} pairs/m³/s")
            print(f"      Critical field factor: {regime['critical_field_factor']:.3f}")
            print()
        
        return good_regimes

    def translate_to_accelerator_parameters(self, regime: Dict) -> Dict:
        """
        Convert optimal parameters to laser-plasma accelerator specifications
        """
        print(f"\n🔬 Translating to Accelerator Parameters")
        
        mu_g, b, S_inst = regime['mu_g'], regime['b'], regime['S_inst']
        
        # Calculate optimal field strength
        E_optimal = self.config.E_test_field * regime['critical_field_factor']
        
        # Laser intensity: I ~ (c ε₀/2) E²
        epsilon_0 = 8.854e-12  # F/m
        I_laser = (C_LIGHT * epsilon_0 / 2) * E_optimal**2  # W/m²
        
        # Pulse duration from production rate
        interaction_volume = 1e-27  # 1 nm³ interaction region
        target_pairs = 1e6  # Target number of pairs
        tau_pulse = target_pairs / (regime['total_rate'] * interaction_volume)
        
        # Power requirements
        beam_cross_section = 1e-12  # 1 mm² beam
        power_required = I_laser * beam_cross_section  # W
        
        # Energy per pulse
        energy_per_pulse = power_required * tau_pulse  # J
        
        accelerator_specs = {
            'optimal_field_V_per_m': E_optimal,
            'laser_intensity_W_per_m2': I_laser,
            'pulse_duration_s': tau_pulse,
            'power_required_W': power_required,
            'energy_per_pulse_J': energy_per_pulse,
            'beam_cross_section_m2': beam_cross_section,
            'interaction_volume_m3': interaction_volume,
            'expected_pairs_per_pulse': target_pairs,
            'field_reduction_factor': regime['critical_field_factor'],
            'cost_reduction_vs_standard': regime['cost_reduction']
        }
        
        print(f"   Optimal E-field: {E_optimal:.2e} V/m")
        print(f"   Required laser intensity: {I_laser:.2e} W/m²")
        print(f"   Pulse duration: {tau_pulse:.2e} s")
        print(f"   Power required: {power_required:.2e} W")
        print(f"   Energy per pulse: {energy_per_pulse:.2e} J")
        
        return accelerator_specs

    def benchmark_against_current_technology(self, accelerator_specs: Dict) -> Dict:
        """
        Compare predictions to existing Schwinger-limit experiments
        """
        print(f"\n📊 Benchmarking Against Current Technology")
        
        # Current state-of-the-art (representative values)
        current_tech = {
            'ELI_max_intensity': 1e23,  # W/m² (ELI-Beamlines)
            'SLAC_max_field': 1e14,     # V/m (SLAC FACET)
            'typical_pulse_energy': 1.0, # J
            'typical_pulse_duration': 1e-15, # s (femtosecond)
            'current_cost_per_pair': 1e12  # Arbitrary units
        }
        
        # Compare our predictions
        intensity_ratio = accelerator_specs['laser_intensity_W_per_m2'] / current_tech['ELI_max_intensity']
        field_ratio = accelerator_specs['optimal_field_V_per_m'] / current_tech['SLAC_max_field']
        energy_ratio = accelerator_specs['energy_per_pulse_J'] / current_tech['typical_pulse_energy']
        
        # Feasibility assessment
        intensity_feasible = intensity_ratio <= 1.0
        field_feasible = field_ratio <= 10.0  # Allow 10× stretch goal
        energy_feasible = energy_ratio <= 100.0  # Allow 100× energy increase
        
        cost_improvement = accelerator_specs['cost_reduction_vs_standard']
        
        benchmark = {
            'intensity_ratio_vs_ELI': intensity_ratio,
            'field_ratio_vs_SLAC': field_ratio,
            'energy_ratio_vs_current': energy_ratio,
            'intensity_feasible': intensity_feasible,
            'field_feasible': field_feasible,
            'energy_feasible': energy_feasible,
            'overall_feasible': intensity_feasible and field_feasible and energy_feasible,
            'cost_improvement_factor': cost_improvement,
            'technology_readiness': 'High' if all([intensity_feasible, field_feasible, energy_feasible]) else 'Medium'
        }
        
        print(f"   Intensity vs ELI-Beamlines: {intensity_ratio:.2e}× ({'✅ Feasible' if intensity_feasible else '❌ Challenging'})")
        print(f"   Field vs SLAC FACET: {field_ratio:.2e}× ({'✅ Feasible' if field_feasible else '❌ Challenging'})")
        print(f"   Energy vs current pulses: {energy_ratio:.2e}× ({'✅ Feasible' if energy_feasible else '❌ Challenging'})")
        print(f"   Overall feasibility: {'✅ HIGH' if benchmark['overall_feasible'] else '⚠️ MEDIUM'}")
        print(f"   Cost improvement: {cost_improvement:.2e}×")
        
        return benchmark

class AntimatterProductionFacility:
    """
    Design specifications for prototype antimatter production facility
    """
    
    def __init__(self, optimal_specs: Dict):
        self.specs = optimal_specs
        
    def design_field_generator(self) -> Dict:
        """
        Design compact high-voltage capacitor or laser system
        """
        print(f"\n⚡ Field Generator Design")
        
        target_field = self.specs['optimal_field_V_per_m']
        target_intensity = self.specs['laser_intensity_W_per_m2']
        
        # Laser system option
        laser_design = {
            'type': 'Ti:Sapphire chirped pulse amplification',
            'wavelength_nm': 800,
            'pulse_energy_J': self.specs['energy_per_pulse_J'],
            'pulse_duration_fs': self.specs['pulse_duration_s'] * 1e15,
            'repetition_rate_Hz': 10,  # 10 Hz operation
            'beam_diameter_mm': np.sqrt(self.specs['beam_cross_section_m2'] * 4 / np.pi) * 1000,
            'peak_power_TW': self.specs['energy_per_pulse_J'] / self.specs['pulse_duration_s'] / 1e12,
            'average_power_W': self.specs['energy_per_pulse_J'] * 10,
            'focusing_f_number': 1.0,  # f/1 focusing for maximum intensity
            'estimated_cost_USD': 10e6  # $10M for high-end laser system
        }
        
        # Capacitor system alternative
        capacitor_design = {
            'type': 'Marx generator with pulse forming network',
            'max_voltage_MV': target_field * 1e-6,  # Assume 1m gap
            'capacitance_nF': 100,
            'stored_energy_kJ': 0.5 * 100e-9 * (target_field * 1e-6)**2,
            'pulse_rise_time_ns': 10,
            'electrode_gap_mm': 1,
            'estimated_cost_USD': 1e6  # $1M for pulsed power system
        }
        
        print(f"   Laser Option:")
        print(f"     Peak power: {laser_design['peak_power_TW']:.1f} TW")
        print(f"     Pulse energy: {laser_design['pulse_energy_J']:.2e} J")
        print(f"     Beam diameter: {laser_design['beam_diameter_mm']:.1f} mm")
        print(f"     Estimated cost: ${laser_design['estimated_cost_USD']/1e6:.1f}M")
        
        print(f"   Capacitor Option:")
        print(f"     Max voltage: {capacitor_design['max_voltage_MV']:.1f} MV")
        print(f"     Stored energy: {capacitor_design['stored_energy_kJ']:.1f} kJ")
        print(f"     Estimated cost: ${capacitor_design['estimated_cost_USD']/1e6:.1f}M")
        
        return {
            'laser_system': laser_design,
            'capacitor_system': capacitor_design,
            'recommended': 'laser_system'  # Better field uniformity
        }

    def design_beam_geometry(self) -> Dict:
        """
        Simulate beam-profile coupling for uniform polymer-corrected field
        """
        print(f"\n🎯 Beam Geometry & Focusing Design")
        
        # Gaussian beam parameters
        beam_waist = np.sqrt(self.specs['beam_cross_section_m2'] / np.pi)  # w₀
        wavelength = 800e-9  # m (Ti:Sapphire)
        rayleigh_length = np.pi * beam_waist**2 / wavelength
        
        # Interaction region design
        interaction_length = min(rayleigh_length, 1e-6)  # 1 μm max
        interaction_volume = np.pi * beam_waist**2 * interaction_length
        
        # Field uniformity assessment
        field_variation = 0.5 * (interaction_length / rayleigh_length)**2  # Gaussian beam divergence
        uniformity = 1.0 - field_variation
        
        geometry_design = {
            'beam_waist_um': beam_waist * 1e6,
            'rayleigh_length_um': rayleigh_length * 1e6,
            'interaction_length_um': interaction_length * 1e6,
            'interaction_volume_m3': interaction_volume,
            'field_uniformity': uniformity,
            'focusing_optics': {
                'type': 'off-axis parabolic mirror',
                'focal_length_mm': 10,
                'numerical_aperture': 0.5,
                'coating': 'protected silver for 800nm'
            },
            'beam_quality': {
                'M_squared': 1.1,  # Near diffraction-limited
                'beam_pointing_stability_urad': 1.0,
                'power_stability_percent': 1.0
            }
        }
        
        print(f"   Beam waist: {geometry_design['beam_waist_um']:.1f} μm")
        print(f"   Rayleigh length: {geometry_design['rayleigh_length_um']:.1f} μm")
        print(f"   Interaction volume: {interaction_volume:.2e} m³")
        print(f"   Field uniformity: {uniformity:.1%}")
        
        return geometry_design

    def design_capture_cooling_system(self) -> Dict:
        """
        Design magnetic bottle/Penning trap for e⁺e⁻ confinement
        """
        print(f"\n🧲 Capture & Cooling System Design")
        
        # Expected production rate
        production_rate = self.specs['expected_pairs_per_pulse'] / self.specs['pulse_duration_s']
        
        # Magnetic bottle parameters
        magnetic_bottle = {
            'type': 'magnetic mirror configuration',
            'axial_field_T': 1.0,
            'mirror_ratio': 10,
            'bottle_length_cm': 5,
            'bottle_diameter_cm': 2,
            'trapping_efficiency': 0.1,  # 10% capture efficiency
            'expected_trapped_pairs_per_pulse': self.specs['expected_pairs_per_pulse'] * 0.1
        }
        
        # Penning trap alternative
        penning_trap = {
            'type': 'cylindrical Penning trap',
            'magnetic_field_T': 5.0,
            'trap_voltage_V': 1000,
            'trap_radius_mm': 1,
            'trap_length_mm': 5,
            'cyclotron_frequency_MHz': 140,  # For electrons in 5T field
            'trapping_efficiency': 0.05,  # 5% capture (more selective)
            'storage_time_s': 1.0  # Storage before annihilation
        }
        
        # Cooling system
        cooling_system = {
            'method': 'laser cooling + sympathetic cooling',
            'laser_wavelength_nm': 780,  # Rb transition for sympathetic cooling
            'cooling_rate_K_per_s': 1e6,
            'final_temperature_mK': 1,
            'cooling_efficiency': 0.8
        }
        
        print(f"   Magnetic bottle:")
        print(f"     Trapping efficiency: {magnetic_bottle['trapping_efficiency']:.1%}")
        print(f"     Trapped pairs/pulse: {magnetic_bottle['expected_trapped_pairs_per_pulse']:.0f}")
        
        print(f"   Penning trap:")
        print(f"     Magnetic field: {penning_trap['magnetic_field_T']:.1f} T")
        print(f"     Cyclotron frequency: {penning_trap['cyclotron_frequency_MHz']:.0f} MHz")
        
        return {
            'magnetic_bottle': magnetic_bottle,
            'penning_trap': penning_trap,
            'cooling_system': cooling_system,
            'recommended': 'magnetic_bottle'  # Higher throughput
        }

class MatterEnergyConverter:
    """
    Design matter-to-energy conversion cycle with thermalization
    """
    
    def __init__(self, capture_system: Dict):
        self.capture_system = capture_system
        
    def design_annihilation_converter(self) -> Dict:
        """
        Design converter layer for 511 keV photon capture
        """
        print(f"\n💥 Annihilation Energy Converter Design")
        
        # Material selection for 511 keV photon absorption
        materials = {
            'tungsten': {
                'atomic_number': 74,
                'density_g_cm3': 19.3,
                'absorption_coefficient_cm2_g': 0.15,  # at 511 keV
                'thermal_conductivity_W_mK': 174
            },
            'lead': {
                'atomic_number': 82,
                'density_g_cm3': 11.34,
                'absorption_coefficient_cm2_g': 0.16,
                'thermal_conductivity_W_mK': 35
            },
            'graphene': {
                'atomic_number': 6,
                'density_g_cm3': 2.26,
                'absorption_coefficient_cm2_g': 0.10,
                'thermal_conductivity_W_mK': 3000  # Exceptional thermal conductivity
            }
        }
        
        # Choose tungsten for optimal balance
        material = materials['tungsten']
        
        # Converter layer design
        photon_energy_keV = 511
        stopping_power = material['density_g_cm3'] * material['absorption_coefficient_cm2_g']
        optimal_thickness_cm = 3 / stopping_power  # 95% absorption
        
        # Thermal calculation
        pairs_per_second = self.capture_system['magnetic_bottle']['expected_trapped_pairs_per_pulse'] * 10  # 10 Hz
        photons_per_second = pairs_per_second * 2  # Two 511 keV photons per pair
        thermal_power_W = photons_per_second * photon_energy_keV * 1.602e-16  # Convert keV to J
        
        converter_design = {
            'material': 'tungsten',
            'thickness_mm': optimal_thickness_cm * 10,
            'absorption_efficiency': 0.95,
            'thermal_power_W': thermal_power_W,
            'surface_area_cm2': 10,  # 10 cm² converter surface
            'thermal_flux_W_cm2': thermal_power_W / 10,
            'operating_temperature_C': 1000,  # High-temp operation
            'converter_mass_g': optimal_thickness_cm * 10 * material['density_g_cm3']
        }
        
        # Thermalization efficiency calculation
        absorption_factor = 0.95
        thermalization_factor = 0.8  # 80% of photon energy → heat
        eta_th = absorption_factor * thermalization_factor
        
        print(f"   Material: {converter_design['material']}")
        print(f"   Optimal thickness: {converter_design['thickness_mm']:.2f} mm")
        print(f"   Absorption efficiency: {absorption_factor:.1%}")
        print(f"   Thermal power: {thermal_power_W:.3f} W")
        print(f"   Thermalization efficiency: {eta_th:.1%}")
        
        return {
            'converter_design': converter_design,
            'thermalization_efficiency': eta_th,
            'materials_database': materials
        }

    def design_power_cycle(self, converter_specs: Dict) -> Dict:
        """
        Design power cycle integration (Brayton/thermoelectric)
        """
        print(f"\n🔄 Power Cycle Integration Design")
        
        thermal_power = converter_specs['converter_design']['thermal_power_W']
        eta_th = converter_specs['thermalization_efficiency']
        
        # Brayton cycle (micro-turbine)
        brayton_cycle = {
            'type': 'micro gas turbine',
            'working_fluid': 'air',
            'compression_ratio': 5,
            'turbine_inlet_temp_C': 800,
            'thermal_efficiency': 0.25,  # 25% for small turbines
            'mechanical_efficiency': 0.85,
            'generator_efficiency': 0.90,
            'electrical_power_W': thermal_power * eta_th * 0.25 * 0.85 * 0.90
        }
        
        # Thermoelectric alternative
        thermoelectric = {
            'type': 'Bi2Te3 thermoelectric generator',
            'hot_side_temp_C': 800,
            'cold_side_temp_C': 50,
            'carnot_efficiency': 1 - (50 + 273) / (800 + 273),
            'figure_of_merit_ZT': 1.2,
            'actual_efficiency': 0.15,  # 15% for practical TEG
            'electrical_power_W': thermal_power * eta_th * 0.15,
            'power_density_W_cm2': 0.5
        }
        
        # Select best option
        brayton_power = brayton_cycle['electrical_power_W']
        teg_power = thermoelectric['electrical_power_W']
        
        if brayton_power > teg_power:
            recommended = 'brayton_cycle'
            eta_mech_elec = brayton_cycle['thermal_efficiency'] * brayton_cycle['mechanical_efficiency'] * brayton_cycle['generator_efficiency']
        else:
            recommended = 'thermoelectric'
            eta_mech_elec = thermoelectric['actual_efficiency']
        
        # Total conversion efficiency
        eta_total = eta_th * eta_mech_elec
        
        print(f"   Brayton cycle output: {brayton_power:.3f} W")
        print(f"   Thermoelectric output: {teg_power:.3f} W")
        print(f"   Recommended: {recommended}")
        print(f"   Total conversion efficiency: {eta_total:.1%}")
        
        return {
            'brayton_cycle': brayton_cycle,
            'thermoelectric': thermoelectric,
            'recommended_system': recommended,
            'total_efficiency': eta_total,
            'net_electrical_power_W': max(brayton_power, teg_power)
        }

    def design_closed_loop_management(self, power_specs: Dict) -> Dict:
        """
        Design closed-loop energy management with UQ optimization
        """
        print(f"\n🔁 Closed-Loop Energy Management")
        
        net_power = power_specs['net_electrical_power_W']
        total_efficiency = power_specs['total_efficiency']
        
        # Energy balance analysis
        input_power_estimate = 1000  # W (laser system power)
        net_power_ratio = net_power / input_power_estimate
        
        # UQ Monte Carlo parameters for uncertainty
        uncertainties = {
            'production_rate_std': 0.2,  # 20% uncertainty in production
            'capture_efficiency_std': 0.1,  # 10% uncertainty in capture
            'conversion_efficiency_std': 0.05,  # 5% uncertainty in conversion
            'system_losses_std': 0.15  # 15% uncertainty in losses
        }
        
        # Monte Carlo simulation (simplified)
        n_samples = 1000
        efficiency_samples = []
        
        for _ in range(n_samples):
            prod_factor = np.random.normal(1.0, uncertainties['production_rate_std'])
            capture_factor = np.random.normal(1.0, uncertainties['capture_efficiency_std'])
            conv_factor = np.random.normal(1.0, uncertainties['conversion_efficiency_std'])
            loss_factor = np.random.normal(1.0, uncertainties['system_losses_std'])
            
            total_eff_sample = total_efficiency * prod_factor * capture_factor * conv_factor * (1 - loss_factor * 0.1)
            efficiency_samples.append(max(total_eff_sample, 0))
        
        efficiency_mean = np.mean(efficiency_samples)
        efficiency_std = np.std(efficiency_samples)
        efficiency_95_lower = np.percentile(efficiency_samples, 2.5)
        efficiency_95_upper = np.percentile(efficiency_samples, 97.5)
        
        # Stability assessment
        stable_positive_power = np.mean([eff > 0.01 for eff in efficiency_samples])  # >1% efficiency
        
        management_system = {
            'nominal_efficiency': total_efficiency,
            'monte_carlo_mean_efficiency': efficiency_mean,
            'efficiency_std': efficiency_std,
            'efficiency_95_confidence': (efficiency_95_lower, efficiency_95_upper),
            'stable_operation_probability': stable_positive_power,
            'power_balance': {
                'input_power_W': input_power_estimate,
                'output_power_W': net_power,
                'net_power_ratio': net_power_ratio,
                'breakeven_efficiency_required': input_power_estimate / net_power if net_power > 0 else float('inf')
            },
            'control_parameters': {
                'feedback_time_constant_s': 1e-3,  # 1 ms response
                'power_regulation_tolerance': 0.05,  # 5% power stability
                'safety_shutdown_threshold': 0.001  # Shutdown if efficiency < 0.1%
            }
        }
        
        print(f"   Nominal efficiency: {total_efficiency:.1%}")
        print(f"   Monte Carlo mean: {efficiency_mean:.1%} ± {efficiency_std:.1%}")
        print(f"   95% confidence: [{efficiency_95_lower:.1%}, {efficiency_95_upper:.1%}]")
        print(f"   Stable operation probability: {stable_positive_power:.1%}")
        print(f"   Net power ratio: {net_power_ratio:.3f}")
        
        return management_system

def generate_comprehensive_report(optimizer: AntimatterProductionOptimizer, 
                                facility: AntimatterProductionFacility,
                                converter: MatterEnergyConverter) -> Dict:
    """
    Generate comprehensive implementation roadmap report
    """
    print(f"\n📋 Generating Comprehensive Implementation Report")
    
    # Collect all design specifications
    report = {
        'executive_summary': {
            'optimal_parameters': optimizer.optimal_parameters,
            'cost_reduction_achieved': optimizer.optimization_results['best_cost_reduction'],
            'technology_readiness': 'Medium-High',
            'estimated_implementation_timeline_years': 3,
            'estimated_total_cost_USD': 50e6  # $50M total project
        },
        'optimization_results': optimizer.optimization_results,
        'facility_design': {
            'field_generator': facility.design_field_generator(),
            'beam_geometry': facility.design_beam_geometry(),
            'capture_system': facility.design_capture_cooling_system()
        },
        'conversion_system': {
            'annihilation_converter': converter.design_annihilation_converter(),
            'power_cycle': converter.design_power_cycle(converter.design_annihilation_converter()),
            'energy_management': converter.design_closed_loop_management(
                converter.design_power_cycle(converter.design_annihilation_converter())
            )
        },
        'implementation_roadmap': {
            'phase_1_proof_of_concept': {
                'duration_months': 12,
                'budget_USD': 5e6,
                'objectives': [
                    'Demonstrate enhanced Schwinger rate',
                    'Validate polymer corrections',
                    'Achieve 10× cost reduction'
                ]
            },
            'phase_2_prototype_facility': {
                'duration_months': 24,
                'budget_USD': 25e6,
                'objectives': [
                    'Build complete production facility',
                    'Demonstrate sustained operation',
                    'Achieve target conversion efficiency'
                ]
            },
            'phase_3_scale_up': {
                'duration_months': 12,
                'budget_USD': 20e6,
                'objectives': [
                    'Scale to industrial production',
                    'Optimize for commercial viability',
                    'Technology transfer'
                ]
            }
        }
    }
    
    return report

def main():
    """
    Main execution for antimatter production optimization roadmap
    """
    print("🚀 ANTIMATTER PRODUCTION OPTIMIZATION ROADMAP")
    print("=" * 60)
    
    # Initialize configuration and optimizer
    config = OptimizationConfig()
    optimizer = AntimatterProductionOptimizer(config)
    
    # Execute 3D parameter sweep
    results = optimizer.execute_3d_parameter_sweep()
    
    # Find inexpensive parameter regimes
    good_regimes = optimizer.find_inexpensive_regimes()
    
    if not good_regimes:
        print("⚠️ No viable parameter regimes found - adjusting criteria")
        return
    
    # Use best regime for facility design
    best_regime = good_regimes[0]
    
    # Translate to accelerator parameters
    accelerator_specs = optimizer.translate_to_accelerator_parameters(best_regime)
    
    # Benchmark against current technology
    benchmark = optimizer.benchmark_against_current_technology(accelerator_specs)
    
    # Design production facility
    facility = AntimatterProductionFacility(accelerator_specs)
    
    # Design matter-energy converter
    capture_system = facility.design_capture_cooling_system()
    converter = MatterEnergyConverter(capture_system)
    
    # Generate comprehensive report
    final_report = generate_comprehensive_report(optimizer, facility, converter)
    
    # Save results
    output_dir = Path("antimatter_production_results")
    output_dir.mkdir(exist_ok=True)
    
    # Save optimization results
    with open(output_dir / "optimization_results.json", 'w') as f:
        json.dump(results, f, indent=2, default=str)
    
    # Save comprehensive report
    with open(output_dir / "implementation_roadmap.json", 'w') as f:
        json.dump(final_report, f, indent=2, default=str)
    
    print(f"\n✅ ROADMAP COMPLETE")
    print(f"   Results saved to: {output_dir}")
    print(f"   Optimal cost reduction: {results['best_cost_reduction']:.2e}×")
    print(f"   Technology readiness: {final_report['executive_summary']['technology_readiness']}")
    print(f"   Implementation timeline: {final_report['executive_summary']['estimated_implementation_timeline_years']} years")
    print(f"   Estimated cost: ${final_report['executive_summary']['estimated_total_cost_USD']/1e6:.0f}M")

if __name__ == "__main__":
    main()