commit d6c029901c6a8c68381745e470aad621e370a76c parent 15acf086f69d2efc8a522a924e3372e9c7d5241c Author: miksa <milutin@popovic.xyz> Date: Wed, 7 Apr 2021 21:45:34 +0200 done with subborn data fit Diffstat:
14 files changed, 47 insertions(+), 24 deletions(-)
diff --git a/sesh1/fit_single.py b/sesh1/fit_single.py @@ -1,24 +0,0 @@ -#!/usr/bin/env python3.9 - -import numpy as np -import matplotlib.pyplot as plt - -sig_p = lambda x: np.sqrt(1 - 4*m_p**2/x) -g_s = lambda s, m_q, g_q: g_q*s/m_q**2 * (sig_p(s)/sig_p(m_q**2))**2 * np.heaviside(s, 4*m_p**2) - -def model(s, m_q, g_q, m_w, g_w, e_w, a, b): - part1 = (m_q)**4/((m_q**2 - s)** + m_q**2*g_s(s, m_q, g_q)**2) - part2 = 1 + (e_w * 2*s * (m_w**2 - s))/((m_w**2 - s)**2 + m_w**2*g_w**2) - part3 = (1 + a*s + b*s)**2 - return part1 * part2 * part3 - -def main(): - text = open('./data/KLOE-VFF.txt').read() - data = np.loadtxt('./data/KLOE-VFF.txt') - cov_syst = np.loadtxt('./data/KLOE-RelSystCov.txt') - cov_stat = np.loadtxt('./data/KLOE-StatCov.txt') - s = data[:,0] - F2 = data[:, 1] - - m_p = 0.13957 - diff --git a/sesh1/iterative.md b/sesh1/iterative.md @@ -5,6 +5,7 @@ * Construct System covariance matrix with the guess y0 ## Iterate + * Find minimum of the chi^2 function * Take minimum \hat{p} and construct the system covariance matrix diff --git a/sesh1/prog/automated.png b/sesh1/prog/automated.png Binary files differ. diff --git a/sesh1/data/BABAR-RelSystCov.txt b/sesh1/prog/data/BABAR-RelSystCov.txt diff --git a/sesh1/data/BABAR-StatCov.txt b/sesh1/prog/data/BABAR-StatCov.txt diff --git a/sesh1/data/BABAR-VFF.txt b/sesh1/prog/data/BABAR-VFF.txt diff --git a/sesh1/data/CMD2-VFF.txt b/sesh1/prog/data/CMD2-VFF.txt diff --git a/sesh1/data/KLOE-RelSystCov.txt b/sesh1/prog/data/KLOE-RelSystCov.txt diff --git a/sesh1/data/KLOE-StatCov.txt b/sesh1/prog/data/KLOE-StatCov.txt diff --git a/sesh1/data/KLOE-VFF.txt b/sesh1/prog/data/KLOE-VFF.txt diff --git a/sesh1/data/SND-VFF.txt b/sesh1/prog/data/SND-VFF.txt diff --git a/sesh1/prog/fit_single.py b/sesh1/prog/fit_single.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3.9 + +import numpy as np +from scipy.optimize import curve_fit +import matplotlib.pyplot as plt + +global m_p; m_p = 0.13957 + +sig_p = lambda x: np.sqrt(1 - 4*m_p**2/x) +g_s = lambda s, m_q, g_q: g_q*s/m_q**2 * (sig_p(s)/sig_p(m_q**2))**2 * np.heaviside(s, 4*m_p**2) + +def model(s, m_q, g_q, m_w, g_w, e_w, a, b): + part1 = (m_q)**4/((m_q**2 - s)**2 + m_q**2*g_s(s, m_q, g_q)**2) + part2 = 1 + (e_w * 2*s * (m_w**2 - s))/((m_w**2 - s)**2 + m_w**2*g_w**2) + part3 = (1 + a*s + b*s)**2 + return part1 * part2 * part3 + +def main(): + data = np.loadtxt('./data/SND-VFF.txt') + s = data[:,0] + F2 = data[:, 1] + + p0 = [0.7, 0.2, 0.8, 0.2, 2e-3, 1e4, -1e5] # in GeV + popt, pcov = curve_fit(model, s, F2, p0) + popt, pcov = np.round(popt, 3), np.round(pcov, 3) + + plt.figure(figsize=[10, 7]) + plt.title('SND DATA FIT') + plt.scatter(s, F2, marker='.', c='black') + plt.plot(s, model(s, *popt), color='red') + plt.annotate('Very Bad, A guessing game with the parameters', (0.15, 40)) + plt.annotate(r'No $\omega$ resonance recognized by the fit', (0.15, 35)) + + plt.annotate(r'$M_{\rho} = $' + f'({popt[0]}' + r'$\pm$' + f'{np.sqrt(pcov[0][0])}) GeV', (0.7, 40)) + plt.annotate(r'$\Gamma_{\rho} = $' + f'({popt[1]}' + r'$\pm$' + f'{np.sqrt(pcov[1][1])}) GeV', (0.7, 36)) + plt.annotate(r'$M_{\omega} = $' + f'({popt[2]}' + r'$\pm$' + f'{np.sqrt(pcov[2][2])}) GeV', (0.7, 34)) + plt.annotate(r'$\Gamma_{\omega} = $' + f'({popt[3]}' + r'$\pm$' + f'{np.sqrt(pcov[3][3])}) GeV', (0.7, 32)) + plt.annotate(r'$\varepsilon_{\omega} = $' + f'({popt[4]}' + f'$\pm$' + f'{np.sqrt(pcov[4][4])}) GeV', (0.7, 30)) + plt.annotate(f'a = ({popt[5]}' + r'$\pm$' + f'{np.sqrt(pcov[5][5])}) GeV', (0.7, 28)) + plt.annotate(f'b = ({popt[6]}' + r'$\pm$' + f'{np.sqrt(pcov[6][6])}) GeV', (0.7, 26)) + + plt.savefig('automated.png') + + +if __name__ == "__main__": + main() diff --git a/sesh1/tp43.py b/sesh1/prog/tp43.py diff --git a/sesh1/tp5.1py b/sesh1/prog/tp51py
