Quantum Pressure

[Jordanbinda]

import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
from scipy.integrate import quad

Load the Pantheon+ dataset (after downloading it)

pantheon_data = pd.read_csv("path_to_pantheon_data.csv") # Update with actual path
z_pantheon = pantheon_data['z'] # Redshift values
mu_pantheon = pantheon_data['mu'] # Distance modulus

Define the distance modulus function

def distance_modulus(z, Omega_q, w_q):
# Calculate the luminosity distance in Mpc
dL_values = np.array([dL(z_val, Omega_q, w_q) for z_val in z])
return 5 * np.log10(dL_values) + 25 # Convert luminosity distance to distance modulus

Fit the quantum pressure model to the Pantheon+ data

popt, pcov = curve_fit(lambda z, Omega_q, w_q: distance_modulus(z, Omega_q, w_q), z_pantheon, mu_pantheon, bounds=([0.6, -1], [0.8, -0.5]))

Output the best-fit parameters

Omega_q_fit, w_q_fit = popt
print(f"Best-fit Omega_q: {Omega_q_fit}, Best-fit w_q: {w_q_fit}")

Calculate the residuals and chi-squared

mu_model = distance_modulus(z_pantheon, Omega_q_fit, w_q_fit)
residuals = mu_pantheon - mu_model
chi_squared = np.sum((residuals / pantheon_data['mu_error'])**2)
print(f"Chi-squared: {chi_squared}")