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code to do a minimised chi squared fit on a function by varying two variables in python for a function with a maximum point not working correctly

I have written a piece of code intended to perform a minimised chi squared fit by varying two parameters, the problem section of code is below:

conversion_factor = 0.3894 * 10**9

def cross_section(parameters, E, Ī“ee):

    mZ, Ī“Z = parameters

    return conversion_factor * (12 * np.pi / (mZ ** 2)) * (
                (E ** 2) / ((E ** 2 - mZ ** 2) ** 2) + (mZ ** 2) * (
                    Ī“Z ** 2)) * (Ī“ee ** 2)

def chi_squared(parameters, E, sigma, sigma_error, mZ_start, Ī“Z_start, Ī“ee):

    mZ, Ī“Z = parameters

    model = cross_section(parameters, E,Ī“ee)

    return np.sum(((model - sigma) / sigma_error) ** 2)

E = combined_data['% centre-of-mass energy (GeV)']
sigma = combined_data['cross section (nb)']
sigma_error = combined_data['uncertainty (nb)']

mZ_start, Ī“Z_start = [90, 3]

Ī“ee = 83.91

result = fmin(chi_squared, (mZ_start, Ī“Z_start), 
              args=(E, sigma, sigma_error, mZ_start, Ī“Z_start, Ī“ee),
              full_output=True, disp=False)

print(result[0][0])
print(result[0][1])

The code only returns my initial values of mZ_start and Ī“Z_start, not the maximum point of my chi squared fit. Can anybody help me understand why?



source https://stackoverflow.com/questions/77578836/code-to-do-a-minimised-chi-squared-fit-on-a-function-by-varying-two-variables-in

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