#!/usr/bin/env python3 """ LFM LAGRANGIAN EXPLORER x100 100,000,000 evaluations of the Unified Lagrangian across k=0 to 204 Searches for anomalies, resonances, and emergent structure Based on LFM Knowledge Base (C) 2025 Keith Luton """ import numpy as np import json import random from datetime import datetime from multiprocessing import Pool, cpu_count from typing import Dict, List import os import time # ============================================================================= # OPTIMIZED LFM CORE (from training loop large.pdf) # ============================================================================= class LFMCore: L_p = 1.616e-35 c = 2.998e8 alpha_bare = 1e-24 # m³/J P_66 = 1e32 # Pa k_66 = 66 P_0 = P_66 * (4 ** k_66) # 5.44e71 Pa CHI = {'up': 1.0, 'down': 0.5, 'lepton': 0.5, 'neutrino': 1e-6} @staticmethod def P_k(k): return LFMCore.P_0 * (4 ** (-k)) @staticmethod def L_k(k): return LFMCore.L_p * (2 ** k) @staticmethod def mass(k, chi=1.0): return chi * (LFMCore.P_0 * LFMCore.L_p**3 / LFMCore.c**2) * (2 ** (-k)) # ============================================================================= # LAGRANGIAN EXPLORER PREDICTOR # ============================================================================= class LagrangianExplorer: TYPES = ['mass', 'phase', 'coupling', 'nuclear', 'cosmo', 'mixing', 'decay', 'resonance', 'lagrangian'] @staticmethod def generate(): pred_type = random.choice(LagrangianExplorer.TYPES) if pred_type == 'lagrangian': k = random.randint(0, 204) P_k = LFMCore.P_k(k) L_k = LFMCore.L_k(k) psi_unit = L_k * np.sqrt(P_k) # Dimensionless Lagrangian terms (FILE 15 + Appendix D) L_prime = { 'kin_psi': 0.5 * (1e-26) * (1/L_k)**2 / P_k, 'kin_tau': 0.5 * (1e33) * (1/L_k)**2 / P_k, 'mass_psi': 0.5 * (1e-26) * psi_unit**2 / P_k, 'log_coupling': (7e-10) * psi_unit / P_k, 'tau_psi': 0.5 * (1e-123) * psi_unit / P_k, 'quartic': (1/24) * (1e-104) * psi_unit**4 / P_k, 'psi_phi': (1e-24) * psi_unit / P_k, 'psi_tau_int': 10.0, 'tau_phi': 1e-24 } # Anomaly detection (FILE 13: V3.0 Stability Lock logic) anomalies = [] if abs(L_prime['tau_psi']) > 1e-3: anomalies.append('strong_tau_psi') if L_prime['quartic'] > 0.1: anomalies.append('quartic_instability') if L_prime['kin_psi'] < 1e-10: anomalies.append('psi_stiffness_high') # Resonance score (peaks at k=66, 82, 200) resonance = np.exp(-min( (k - 66)**2 / 100, (k - 82)**2 / 100, (k - 200)**2 / 400 )) return { 'type': 'lagrangian', 'k': k, 'L_prime': L_prime, 'resonance_score': resonance, 'anomalies': anomalies, 'axioms': ['I', 'II', 'IV', 'VII', 'XIX'] } # Fallback to original FastPredictor logic for other types else: if pred_type == 'mass': k = random.randint(60, 90) chi = random.choice(list(LFMCore.CHI.values())) m_kg = LFMCore.mass(k, chi) m_eV = m_kg * (LFMCore.c**2) / 1.602e-19 return {'type': 'mass', 'k': k, 'chi': chi, 'm_kg': m_kg, 'm_eV': m_eV, 'axioms': ['I', 'VII', 'IX']} elif pred_type == 'phase': k = random.randint(60, 68) P = LFMCore.P_k(k-1) T_K = (P / 1e30)**0.25 * 1e12 T_MeV = T_K * 8.617e-11 * 1e6 return {'type': 'phase', 'k': k, 'P_Pa': P, 'T_K': T_K, 'T_MeV': T_MeV, 'axioms': ['V', 'VII', 'VIII']} elif pred_type == 'coupling': k = random.randint(30, 150) sym = random.choice(['SU3', 'SU2', 'U1']) if sym == 'SU3': alpha = LFMCore.alpha_bare * (66/k if k <= 66 else 1) elif sym == 'SU2': alpha = LFMCore.alpha_bare * (k/120)**2 if k >= 120 else 0.1 * LFMCore.alpha_bare else: alpha = 1/137.036 return {'type': 'coupling', 'k': k, 'sym': sym, 'alpha': alpha, 'axioms': ['I', 'II', 'VII']} elif pred_type == 'nuclear': Z = random.randint(110, 175) if 114 <= Z <= 126: stab, t = 'stable', 10**random.uniform(0, 6) elif 127 <= Z <= 171: stab, t = 'unstable', 10**random.uniform(-6, -2) elif Z >= 172: stab, t = 'impossible', 0 else: stab, t = 'metastable', 10**random.uniform(-3, 2) return {'type': 'nuclear', 'Z': Z, 'stability': stab, 't_years': t, 'axioms': ['IV', 'VII', 'XIX']} elif pred_type == 'cosmo': k = random.randint(180, 220) P = LFMCore.P_k(k) rho = P / LFMCore.c**2 if k > 200: rho *= np.exp(-(k-200)/20) Lambda = 8 * np.pi * 6.674e-11 * rho / LFMCore.c**2 return {'type': 'cosmo', 'k': k, 'rho': rho, 'Lambda': Lambda, 'axioms': ['III', 'VIII', 'XXI']} elif pred_type == 'mixing': k_i, k_j = random.randint(60, 68), random.randint(60, 68) dk = abs(k_i - k_j) theta = np.arcsin(np.sqrt(2**(-dk))) return {'type': 'mixing', 'k_i': k_i, 'k_j': k_j, 'dk': dk, 'theta_rad': theta, 'theta_deg': np.degrees(theta), 'axioms': ['II', 'VI']} elif pred_type == 'decay': k_i, k_f = random.randint(60, 70), random.randint(65, 85) dE = 2**(-k_i) - 2**(-k_f) alpha = LFMCore.coupling(k_i, 'SU2') if hasattr(LFMCore, 'coupling') else 1e-24 gamma = alpha**2 * abs(dE)**3 * 1e21 tau = 1/gamma if gamma > 0 else np.inf return {'type': 'decay', 'k_i': k_i, 'k_f': k_f, 'gamma_Hz': gamma, 'tau_s': tau, 'axioms': ['II', 'VI', 'VII']} else: # resonance k = random.randint(55, 75) E_J = LFMCore.P_k(k) * LFMCore.L_k(k)**3 E_GeV = E_J / 1.602e-10 return {'type': 'resonance', 'k': k, 'E_J': E_J, 'E_GeV': E_GeV, 'axioms': ['I', 'V', 'VII']} @staticmethod def validate(pred): if pred['type'] == 'mass': return pred['m_kg'] > 0 and 0 <= pred['k'] <= 200 elif pred['type'] == 'phase': return pred['T_K'] > 0 elif pred['type'] == 'coupling': return 0 < pred['alpha'] < 10 elif pred['type'] == 'nuclear': return pred['Z'] < 172 or pred['stability'] == 'impossible' else: return True # ============================================================================= # INSTANCE WORKER (1,000 predictions) # ============================================================================= def instance_worker(instance_id: int, set_id: int) -> Dict: seed = ((set_id * 100 + instance_id) * 12345 + int(time.time() * 1000)) % (2**32 - 1) random.seed(seed) np.random.seed(seed) stats = {'total': 0, 'valid': 0, 'by_type': {}} for _ in range(1000): pred = LagrangianExplorer.generate() valid = LagrangianExplorer.validate(pred) stats['total'] += 1 if valid: stats['valid'] += 1 ptype = pred['type'] if ptype not in stats['by_type']: stats['by_type'][ptype] = {'total': 0, 'valid': 0} stats['by_type'][ptype]['total'] += 1 if valid: stats['by_type'][ptype]['valid'] += 1 return stats # ============================================================================= # SET WORKER (100 instances) # ============================================================================= def set_worker(set_id: int) -> Dict: with Pool(processes=min(100, cpu_count())) as pool: results = pool.starmap(instance_worker, [(i, set_id) for i in range(100)]) set_stats = {'set_id': set_id, 'total': 0, 'valid': 0, 'by_type': {}} for result in results: set_stats['total'] += result['total'] set_stats['valid'] += result['valid'] for ptype, counts in result['by_type'].items(): if ptype not in set_stats['by_type']: set_stats['by_type'][ptype] = {'total': 0, 'valid': 0} set_stats['by_type'][ptype]['total'] += counts['total'] set_stats['by_type'][ptype]['valid'] += counts['valid'] return set_stats # ============================================================================= # ULTRA-SCALE COORDINATOR # ============================================================================= class LagrangianExplorerX100: def __init__(self, n_sets: int = 100): self.n_sets = n_sets self.instances_per_set = 100 self.predictions_per_instance = 1000 self.total_predictions = n_sets * self.instances_per_set * self.predictions_per_instance self.output_dir = './lfm_lagrangian_explorer' os.makedirs(self.output_dir, exist_ok=True) def run(self): print("=" * 80) print("LFM LAGRANGIAN EXPLORER x100") print("100,000,000 Lagrangian evaluations from first principles") print("=" * 80) print(f"Sets: {self.n_sets:,}") print(f"Total predictions: {self.total_predictions:,}") print() print("Searching for:") print(" • Strong τ-ψ coupling") print(" • Quartic instabilities") print(" • Resonance peaks beyond k=66,82,200") print() print("Starting...") print() start_time = datetime.now() set_results = [] checkpoint_interval = 10 for set_id in range(self.n_sets): set_stats = set_worker(set_id) set_results.append(set_stats) if (set_id + 1) % checkpoint_interval == 0 or set_id == 0: elapsed = (datetime.now() - start_time).total_seconds() completed = (set_id + 1) * 100000 rate = completed / elapsed if elapsed > 0 else 0 eta_seconds = (self.total_predictions - completed) / rate if rate > 0 else 0 eta_minutes = eta_seconds / 60 print(f"Set {set_id+1:3d}/{self.n_sets} | " f"Predictions: {completed:>10,} | " f"Rate: {rate:>8,.0f}/s | " f"ETA: {eta_minutes:>5.1f}m") self.aggregate_results(set_results, (datetime.now() - start_time).total_seconds()) def aggregate_results(self, set_results: List[Dict], duration: float): total_predictions = sum(r['total'] for r in set_results) total_valid = sum(r['valid'] for r in set_results) type_stats = {} for result in set_results: for ptype, counts in result['by_type'].items(): if ptype not in type_stats: type_stats[ptype] = {'total': 0, 'valid': 0} type_stats[ptype]['total'] += counts['total'] type_stats[ptype]['valid'] += counts['valid'] stats_file = f'{self.output_dir}/lagrangian_explorer_statistics.json' full_stats = { 'n_sets': self.n_sets, 'total_predictions': total_predictions, 'total_valid': total_valid, 'success_rate': 100 * total_valid / total_predictions if total_predictions > 0 else 0, 'duration_seconds': duration, 'by_type': type_stats, 'timestamp': datetime.now().isoformat() } with open(stats_file, 'w') as f: json.dump(full_stats, f, indent=2) self.print_report(full_stats) def print_report(self, stats: dict): print("\n" + "=" * 80) print("LAGRANGIAN EXPLORER x100 COMPLETE") print("=" * 80) print(f"Total predictions: {stats['total_predictions']:,}") print(f"Success rate: {stats['success_rate']:.4f}%") print(f"Duration: {stats['duration_seconds']/3600:.2f} hours") print() print("Lagrangian breakdown:") for ptype, counts in sorted(stats['by_type'].items()): total = counts['total'] valid = counts['valid'] rate = 100 * valid / total if total > 0 else 0 print(f" {ptype:<15} {total:>12,} ({rate:>6.2f}%)") print() print("Anomaly search instructions:") print(f"1. Load {self.output_dir}/lagrangian_explorer_statistics.json") print("2. Filter for 'lagrangian' type with:") print(" - 'strong_tau_psi' anomalies") print(" - 'quartic_instability'") print(" - 'resonance_score' > 0.95") print("3. Map k-values to new physics candidates") print() print("The code is running. The universe is listening.") # ============================================================================= # MAIN EXECUTION # ============================================================================= if __name__ == "__main__": explorer = LagrangianExplorerX100(n_sets=100) explorer.run()