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import numpy as np
import json
import os
# Constants
c = 299792458 # Speed of light in m/s
E_mc2 = c**2 # Mass-energy equivalence in J/kg
dark_energy_density = 5.96e-27 # Density of dark energy in kg/m^3
collision_distance = 1e-10 # Distance for collision detection
# Initial conditions
temperature_initial = 1.42e32 # Planck temperature in K
particle_speed_initial = c # Initially at the speed of light
# Simulation time
t_planck = 5.39e-44 # Planck time in s
t_simulation = t_planck * 1e5 # Shorter timescale for simulation
# Quark masses (in GeV)
quark_masses = {
"up": 2.3e-3,
"down": 4.8e-3,
"charm": 1.28,
"strange": 0.095,
"top": 173.0,
"bottom": 4.18,
"electron": 5.11e-4,
"muon": 1.05e-1,
"tau": 1.78,
"photon": 0,
}
# Conversion factor from GeV to J
GeV_to_J = 1.60217662e-10
# Simulation setup
num_steps = int(t_simulation / t_planck)
# Create a directory to store the data
data_dir = "big_bang_simulation_data"
os.makedirs(data_dir, exist_ok=True)
# Functions to incorporate relativistic effects and collisions
def relativistic_momentum(particle_speed, particle_mass):
if particle_speed >= c:
return np.inf
return (
particle_mass
* particle_speed
/ np.sqrt(max(1e-10, 1 - (particle_speed / c) ** 2))
)
def update_speed(current_speed, current_temperature, particle_mass):
# Placeholder for speed update logic
return current_speed # This should be replaced with actual logic
def check_collision(particle_speeds, collision_distance):
for j in range(len(particle_speeds)):
for k in range(j + 1, len(particle_speeds)):
if np.abs(particle_speeds[j] - particle_speeds[k]) < collision_distance:
return True, j, k
return False, -1, -1
def handle_collision(particle_speeds, idx1, idx2):
# Exchange momentum for a simplified collision response
p1 = relativistic_momentum(particle_speeds[idx1], particle_masses[idx1])
p2 = relativistic_momentum(particle_speeds[idx2], particle_masses[idx2])
# Simplified exchange of speeds based on momentum
particle_speeds[idx1] = p2 / particle_masses[idx1] # Update speed of particle 1
particle_speeds[idx2] = p1 / particle_masses[idx2] # Update speed of particle 2
# Simulate the Big Bang with Dark Energy, Dark Matter, Tunneling, and Relativistic Effects
for tunneling_probability in np.arange(0.01, 2.5, 0.1):
print(f"Simulating for tunneling probability: {tunneling_probability}")
# Initialize arrays for simulation
particle_speeds = np.zeros((len(quark_masses), num_steps)) # 2D array for speeds
particle_temperatures = np.zeros((len(quark_masses), num_steps)) # 2D array for temperatures
np.zeros((len(quark_masses), num_steps)) # 2D array for temperatures
particle_masses_evolution = np.zeros((len(quark_masses), num_steps)) # 2D array for mass evolution
tunneling_steps = np.zeros((len(quark_masses), num_steps), dtype=bool) # 2D array for tunneling steps
# Create an array of masses for each quark
particle_masses = np.array([mass * GeV_to_J for mass in quark_masses.values()])
for j, (quark, mass) in enumerate(quark_masses.items()):
# Initialize particle speeds and temperatures
particle_speeds[j, 0] = particle_speed_initial
particle_temperatures[j, 0] = temperature_initial
particle_masses_evolution[j, 0] = mass * GeV_to_J # Convert to Joules
# Time evolution loop
for step in range(1, num_steps):
for j in range(len(quark_masses)):
# Update temperature based on some model (placeholder)
particle_temperatures[j, step] = particle_temperatures[j, step - 1] * 0.99 # Cooling down
# Update speed based on temperature and mass
particle_speeds[j, step] = update_speed(
particle_speeds[j, step - 1],
particle_temperatures[j, step],
particle_masses[j]
)
# Check for collisions
collision_detected, idx1, idx2 = check_collision(particle_speeds[:, step], collision_distance)
if collision_detected:
handle_collision(particle_speeds[:, step], idx1, idx2)
# Tunneling effect (placeholder for actual physics)
if np.random.rand() < tunneling_probability:
tunneling_steps[j, step] = True
# Modify mass or speed based on tunneling (placeholder)
particle_masses[j] *= 1.1 # Increase mass as an example
# Store mass evolution
particle_masses_evolution[:, step] = particle_masses
# Save the simulation data for this tunneling probability
simulation_data = {
"particle_speeds": particle_speeds.tolist(), # Convert to list for JSON serialization
"particle_temperatures": particle_temperatures.tolist(), # Convert to list for JSON serialization
"particle_masses_evolution": particle_masses_evolution.tolist(), # Convert to list for JSON serialization
"tunneling_steps": tunneling_steps.tolist(), # Convert to list for JSON serialization
}
with open(os.path.join(data_dir, f"simulation_tunneling_{tunneling_probability:.2f}.json"), "w") as f:
json.dump(simulation_data, f)
print("Simulation completed and data saved.")
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