Dimuon mass histogram around the Z peak

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import numpy as np
import pandas as pd

# Load the data
data = pd.read_csv('data/cms_dimuon_2011.csv')

# Select events with opposite-sign muons (Q1*Q2 < 0)
opposite_charge = data[data['Q1'] * data['Q2'] < 0]

# Calculate the invariant mass M
opposite_charge['M'] = np.sqrt(2 * (opposite_charge['E1'] * opposite_charge['E2'] - 
                                    opposite_charge['px1'] * opposite_charge['px2'] - 
                                    opposite_charge['py1'] * opposite_charge['py2']))

# Histogram the dimuon invariant mass in the range [60, 120] GeV with 60 bins
histogram = np.histogram(opposite_charge['M'], bins=60, range=(60, 120))

# Save the histogram data to hist.json
hist_data = {
    'bin_edges': histogram[1].tolist(),
    'counts': histogram[0].tolist(),
    'n_in_range': len(opposite_charge[(opposite_charge['M'] >= 60) & (opposite_charge['M'] <= 120)]),
    'n_opposite_charge': len(opposite_charge)
}

with open('hist.json', 'w') as f:
    f.write(str(hist_data))

# Find the peak bin center and count
peak_bin_index = np.argmax(hist_data['counts'])
peak_bin_center = (hist_data['bin_edges'][peak_bin_index] + hist_data['bin_edges'][peak_bin_index + 1]) / 2
peak_count = hist_data['counts'][peak_bin_index]

# Save the result to result.json
result_data = {
    'peak_bin_center': peak_bin_center,
    'peak_count': peak_count
}

with open('result.json', 'w') as f:
    f.write(str(result_data))

# Plot the histogram and save it as figures/mass_hist.svg
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.hist(opposite_charge['M'], bins=60, range=(60, 120), edgecolor='black', alpha=0.7)
plt.xlabel('Invariant Mass (GeV)')
plt.ylabel('Counts')
plt.title('Dimuon Invariant Mass Distribution Around Z Peak')
plt.savefig('figures/mass_hist.svg')
plt.close()

# Write the solution.py file
solution_code = """
import numpy as np
import pandas as pd

# Load the data
data = pd.read_csv('data/cms_dimuon_2011.csv')

# Select events with opposite-sign muons (Q1*Q2 < 0)
opposite_charge = data[data['Q1'] * data['Q2'] < 0]

# Calculate the invariant mass M
opposite_charge['M'] = np.sqrt(2 * (opposite_charge['E1'] * opposite_charge['E2'] - 
                                    opposite_charge['px1'] * opposite_charge['px2'] - 
                                    opposite_charge['py1'] * opposite_charge['py2']))

# Histogram the dimuon invariant mass in the range [60, 120] GeV with 60 bins
histogram = np.histogram(opposite_charge['M'], bins=60, range=(60, 120))

# Save the histogram data to hist.json
hist_data = {
    'bin_edges': histogram[1].tolist(),
    'counts': histogram[0].tolist(),
    'n_in_range': len(opposite_charge[(opposite_charge['M'] >= 60) & (opposite_charge['M'] <= 120)]),
    'n_opposite_charge': len(opposite_charge)
}

with open('hist.json', 'w') as f:
    f.write(str(hist_data))

# Find the peak bin center and count
peak_bin_index = np.argmax(hist_data['counts'])
peak_bin_center = (hist_data['bin_edges'][peak_bin_index] + hist_data['bin_edges'][peak_bin_index + 1]) / 2
peak_count = hist_data['counts'][peak_bin_index]

# Save the result to result.json
result_data = {
    'peak_bin_center': peak_bin_center,
    'peak_count': peak_count
}

with open('result.json', 'w') as f:
    f.write(str(result_data))

# Plot the histogram and save it as figures/mass_hist.svg
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.hist(opposite_charge['M'], bins=60, range=(60, 120), edgecolor='black', alpha=0.7)
plt.xlabel('Invariant Mass (GeV)')
plt.ylabel('Counts')
plt.title('Dimuon Invariant Mass Distribution Around Z Peak')
plt.savefig('figures/mass_hist.svg')
plt.close()
"""

with open('solution.py', 'w') as f:
    f.write(solution_code)

svg content here

{
 "summary": "Generated the dimuon mass histogram around the Z peak. The histogram includes bin edges, counts, and event counts for opposite charge and in range. The peak bin center and count are identified, and the figure is saved.",
 "assumptions": [
  "The invariant mass calculation uses the correct formula.",
  "The charge selection is correctly implemented.",
  "The histogram range and bins are as specified."
 ]
}