import numpy as np from time import perf_counter as pc from time import sleep import matplotlib.pyplot as plt from datetime import datetime # ===================== # CBHE-TD: Black Hole Timing Detector # Uses adaptive timing to sense gravitational anomalies # ===================== class SpacetimeDetector: def __init__(self, duration=3600, sample_rate=100): """ duration: seconds to collect data (1 hour minimum for annual effects) sample_rate: Hz (must be ~100 for π-timing) """ self.duration = duration self.sample_rate = sample_rate self.d = 0 self.dl = [] self.timestamps = [] self.start_time = datetime.now() def collect_data(self): """Collect timing error data""" iterations = self.duration * self.sample_rate print(f"Collecting {iterations} samples over {self.duration}s...") for i in range(iterations): t = pc() sleep(np.pi/100 - self.d) pi = 100 * (pc() - t) err = np.pi - pi self.d -= 0.01 * err self.dl.append(self.d) self.timestamps.append((datetime.now() - self.start_time).total_seconds()) if i % 10000 == 0: print(f"Progress: {100*i/iterations:.1f}%") return np.array(self.dl) def fft_analysis(self): """Perform FFT to find periodic gravitational signatures""" data = np.array(self.dl) # Remove DC component (mean) data = data - np.mean(data) # Apply window function window = np.hanning(len(data)) data_windowed = data * window # FFT fft_result = np.fft.fft(data_windowed) freqs = np.fft.fftfreq(len(data), 1/self.sample_rate) # Only positive frequencies pos_mask = freqs > 0 freqs_pos = freqs[pos_mask] magnitude = np.abs(fft_result[pos_mask]) return freqs_pos, magnitude, data def identify_black_hole_signatures(self, freqs, magnitude): """Identify potential BH signatures in frequency domain""" signatures = [] # Known frequencies of interest targets = { 'Annual (Earth orbit)': 1/(365.25*24*3600), # 1/year 'Semi-annual': 2/(365.25*24*3600), 'Lunar month': 1/(27.32*24*3600), 'Solar day': 1/(24*3600), 'Sidereal day': 1/(23.9345*3600), 'PBH passage (0.001 Hz)': 0.001, 'PBH passage (0.0001 Hz)': 0.0001, 'Gravitational wave (LIGO)': 100, } print("\n" + "="*60) print("BLACK HOLE SIGNATURE ANALYSIS") print("="*60) for name, target_freq in targets.items(): # Find closest frequency in spectrum idx = np.argmin(np.abs(freqs - target_freq)) detected_freq = freqs[idx] power = magnitude[idx] # Signal detection threshold threshold = np.mean(magnitude) + 3*np.std(magnitude) status = "⚠ DETECTED" if power > threshold else " normal" print(f"{name:30} | freq: {detected_freq:.2e} | power: {power:.2e} | {status}") if power > threshold: signatures.append({ 'name': name, 'frequency': detected_freq, 'power': power, 'threshold': threshold }) return signatures def time_domain_analysis(self, data): """Analyze time-domain for transient BH events""" print("\n" + "="*60) print("TIME-DOMAIN ANOMALY DETECTION") print("="*60) # Rolling statistics window = 1000 rolling_mean = np.convolve(data, np.ones(window)/window, mode='same') rolling_std = np.array([np.std(data[max(0,i-window):i+1]) for i in range(len(data))]) # Find anomalies (points > 3 std from mean) anomaly_mask = np.abs(data - rolling_mean) > 3 * rolling_std anomaly_indices = np.where(anomaly_mask)[0] print(f"Anomalous timing deviations detected: {len(anomaly_indices)}") if len(anomaly_indices) > 0: # Group consecutive anomalies groups = [] current_group = [anomaly_indices[0]] for idx in anomaly_indices[1:]: if idx - current_group[-1] < 100: # Within 1 second current_group.append(idx) else: groups.append(current_group) current_group = [idx] groups.append(current_group) print(f"Possible BH passage events: {len(groups)}") for i, group in enumerate(groups[:5]): # Show first 5 center = group[len(group)//2] time_sec = self.timestamps[center] if center < len(self.timestamps) else 0 hours = time_sec / 3600 magnitude = np.mean(np.abs(data[group])) print(f" Event {i+1}: t={hours:.2f}h, magnitude={magnitude:.4f}") # Spectral power in different bands print("\nSpectral Power Distribution:") bands = [ ('Ultra-low (PBH) < 0.0001 Hz', 0, 0.0001), ('Very low (PBH) < 0.001 Hz', 0.0001, 0.001), ('Low (solar system) < 0.01 Hz', 0.001, 0.01), ('Medium (local) < 1 Hz', 0.01, 1), ('High (GW LIGO) < 100 Hz', 1, 100), ] freqs, magnitude, _ = self.fft_analysis() for name, f_min, f_max in bands: mask = (freqs >= f_min) & (freqs < f_max) power = np.sum(magnitude[mask]**2) print(f" {name:40} | power: {power:.4e}") return anomaly_indices def plot_results(self, freqs, magnitude, data): """Generate comprehensive plots""" fig, axes = plt.subplots(3, 2, figsize=(14, 12)) # 1. Time series ax1 = axes[0, 0] times_hours = np.array(self.timestamps) / 3600 ax1.plot(times_hours, data, 'b-', alpha=0.5, linewidth=0.5) ax1.set_xlabel('Time (hours)') ax1.set_ylabel('Timing Deviation (d)') ax1.set_title('Timing Error Accumulation') ax1.grid(True, alpha=0.3) # 2. FFT magnitude ax2 = axes[0, 1] ax2.semilogy(freqs, magnitude, 'r-', linewidth=0.5) ax2.set_xlabel('Frequency (Hz)') ax2.set_ylabel('Magnitude') ax2.set_title('FFT: Frequency Domain Analysis') ax2.set_xscale('log') ax2.grid(True, alpha=0.3) # 3. Log-log FFT (reveals power laws) ax3 = axes[1, 0] valid = freqs > 0 ax3.loglog(freqs[valid], magnitude[valid], 'g-', linewidth=0.5) ax3.set_xlabel('Frequency (Hz)') ax3.set_ylabel('Magnitude') ax3.set_title('Log-Log FFT (Power Law Detection)') ax3.grid(True, alpha=0.3) # 4. Spectrogram ax4 = axes[1, 1] window_size = len(data) // 50 spectrogram = [] for i in range(0, len(data) - window_size, window_size//2): segment = data[i:i+window_size] fft_seg = np.abs(np.fft.fft(segment * np.hanning(window_size))) spectrogram.append(fft_seg[:len(fft_seg)//2]) spectrogram = np.array(spectrogram) im = ax4.imshow(spectrogram.T, aspect='auto', origin='lower', extent=[0, len(data)/self.sample_rate/3600, 0, self.sample_rate/2]) ax4.set_xlabel('Time (hours)') ax4.set_ylabel('Frequency (Hz)') ax4.set_title('Time-Frequency Spectrogram') plt.colorbar(im, ax=ax4) # 5. Histogram of deviations ax5 = axes[2, 0] ax5.hist(data, bins=100, alpha=0.7, color='purple', edgecolor='black') ax5.axvline(np.mean(data), color='red', linestyle='--', label=f'Mean: {np.mean(data):.4f}') ax5.axvline(np.mean(data) + 3*np.std(data), color='orange', linestyle='--', label='+3σ') ax5.axvline(np.mean(data) - 3*np.std(data), color='orange', linestyle='--', label='-3σ') ax5.set_xlabel('Timing Deviation') ax5.set_ylabel('Count') ax5.set_title('Distribution of Timing Errors') ax5.legend() # 6. Cumulative spectral power ax6 = axes[2, 1] sorted_idx = np.argsort(freqs) cumulative = np.cumsum(magnitude[sorted_idx]**2) cumulative = cumulative / cumulative[-1] # Normalize ax6.plot(freqs[sorted_idx], cumulative, 'b-', linewidth=1) ax6.set_xlabel('Frequency (Hz)') ax6.set_ylabel('Cumulative Power') ax6.set_title('Cumulative Spectral Power') ax6.set_xscale('log') ax6.grid(True, alpha=0.3) plt.tight_layout() plt.savefig('black_hole_timing_analysis.png', dpi=150) plt.show() print("\nPlot saved to 'black_hole_timing_analysis.png'") def run_detection_experiment(duration_seconds=60): """ Run the black hole detection experiment duration: For meaningful results, run at least: - 1 hour for solar system effects - 24 hours for Earth rotation effects - 1 week for annual effects """ print("="*60) print("CBHE-TD: Conjugate Black Hole Timing Detector") print("Using Heisenberg Uncertainty for BH Detection") print("="*60) detector = SpacetimeDetector(duration=duration_seconds) # Collect data print("\n[1/4] Collecting timing data...") data = detector.collect_data() # FFT analysis print("\n[2/4] Performing FFT analysis...") freqs, magnitude, data_centered = detector.fft_analysis() # Identify signatures signatures = detector.identify_black_hole_signatures(freqs, magnitude) # Time-domain analysis print("\n[3/4] Time-domain anomaly detection...") anomalies = detector.time_domain_analysis(data) # Plot results print("\n[4/4] Generating plots...") detector.plot_results(freqs, magnitude, data) return detector, signatures, anomalies # ===================== # Quick test / demonstration # ===================== if __name__ == "__main__": # For demo, run 60 seconds # For real detection, use duration_seconds=3600 or more duration = 10 # seconds print(f"\nRunning {duration}s detection experiment...") print("Note: Longer runs (>1 hour) detect more phenomena\n") detector, sigs, anom = run_detection_experiment(duration) print("\n" + "="*60) print("DETECTION SUMMARY") print("="*60) if sigs: print("\n✓ POTENTIAL BLACK HOLE SIGNATURES DETECTED:") for s in sigs: print(f" • {s['name']}: freq={s['frequency']:.2e} Hz, power={s['power']:.2e}") else: print("\n○ No strong BH signatures in this run.") print(" Try longer duration for better sensitivity.") if len(anom) > 0: print(f"\n✓ {len(anom)} ANOMALOUS TIMING DEVIATIONS:") print(" Could indicate transient BH passage or gravitational anomaly")