Dimuon mass histogram around the Z peak

{
 "path": "README.md",
 "max_chars": 3000,
 "offset": 0
}

{
 "path": "data/cms_dimuon_2011.csv",
 "max_chars": 3000,
 "offset": 0
}

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os

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

# Select opposite-sign muons
opposite_charge_mask = (df['Q1'] * df['Q2']) < 0
opposite_charge_df = df[opposite_charge_mask]

# Calculate invariant mass
# M = sqrt(2*(E1*E2*(1 - cos(delta_phi)) + p_z1*p_z2))
delta_phi = df['phi1'] - df['phi2']
cos_delta_phi = np.cos(delta_phi)
M = np.sqrt(2 * (df['E1'] * df['E2'] * (1 - cos_delta_phi) + df['pz1'] * df['pz2']))

# Apply mass range cut
mass_range_mask = (M >= 60) & (M <= 120)
mass_in_range_df = opposite_charge_df[mass_range_mask]

# Create histogram
hist, bin_edges = np.histogram(M, bins=60, range=(60, 120))

# Save histogram data to hist.json
hist_data = {
    'bin_edges': bin_edges.tolist(),
    'counts': hist.tolist(),
    'n_in_range': len(mass_in_range_df),
    'n_opposite_charge': len(opposite_charge_df)
}

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

# Identify peak bin
peak_bin_index = np.argmax(hist)
peak_bin_center = bin_edges[peak_bin_index] + (bin_edges[1] - bin_edges[0]) / 2
peak_count = hist[peak_bin_index]

# Save result data to result.json
result_data = {
    'peak_bin_center': float(peak_bin_center),
    'peak_count': int(peak_count)
}

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

# Generate plot
plt.figure(figsize=(10, 6))
plt.hist(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')
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Save the figure
os.makedirs('figures', exist_ok=True)
plt.savefig('figures/mass_hist.svg')
plt.close()

# Write solution.py
solution_code = '''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

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

# Select opposite-sign muons
opposite_charge_mask = (df['Q1'] * df['Q2']) < 0
opposite_charge_df = df[opposite_charge_mask]

# Calculate invariant mass
# M = sqrt(2*(E1*E2*(1 - cos(delta_phi)) + p_z1*p_z2))
delta_phi = df['phi1'] - df['phi2']
cos_delta_phi = np.cos(delta_phi)
M = np.sqrt(2 * (df['E1'] * df['E2'] * (1 - cos_delta_phi) + df['pz1'] * df['pz2']))

# Apply mass range cut
mass_range_mask = (M >= 60) & (M <= 120)
mass_in_range_df = opposite_charge_df[mass_range_mask]

# Create histogram
hist, bin_edges = np.histogram(M, bins=60, range=(60, 120))

# Save histogram data to hist.json
hist_data = {
    'bin_edges': bin_edges.tolist(),
    'counts': hist.tolist(),
    'n_in_range': len(mass_in_range_df),
    'n_opposite_charge': len(opposite_charge_df)
}

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

# Identify peak bin
peak_bin_index = np.argmax(hist)
peak_bin_center = bin_edges[peak_bin_index] + (bin_edges[1] - bin_edges[0]) / 2
peak_count = hist[peak_bin_index]

# Save result data to result.json
result_data = {
    'peak_bin_center': float(peak_bin_center),
    'peak_count': int(peak_count)
}

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

# Generate plot
plt.figure(figsize=(10, 6))
plt.hist(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')
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Save the figure
os.makedirs('figures', exist_ok=True)
plt.savefig('figures/mass_hist.svg')
plt.close()
'''

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

# Print summary and assumptions
print('Analysis completed successfully.')
print('Assumptions made:')
print('- The data file contains the required columns for analysis.')
print('- The charge values are correctly represented as -1 or 1.')
print('- The invariant mass calculation uses the correct formula.')
print('- The histogram is created with the specified parameters.')
print('- The plot is generated with appropriate labels and formatting.')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

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

# Select opposite-sign muons
opposite_charge_mask = (df['Q1'] * df['Q2']) < 0
opposite_charge_df = df[opposite_charge_mask]

# Calculate invariant mass
# M = sqrt(2*(E1*E2*(1 - cos(delta_phi)) + p_z1*p_z2))
delta_phi = df['phi1'] - df['phi2']
cos_delta_phi = np.cos(delta_phi)
M = np.sqrt(2 * (df['E1'] * df['E2'] * (1 - cos_delta_phi) + df['pz1'] * df['pz2']))

# Apply mass range cut
mass_range_mask = (M >= 60) & (M <= 120)
mass_in_range_df = opposite_charge_df[mass_range_mask]

# Create histogram
hist, bin_edges = np.histogram(M, bins=60, range=(60, 120))

# Save histogram data to hist.json
hist_data = {
    'bin_edges': bin_edges.tolist(),
    'counts': hist.tolist(),
    'n_in_range': len(mass_in_range_df),
    'n_opposite_charge': len(opposite_charge_df)
}

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

# Identify peak bin
peak_bin_index = np.argmax(hist)
peak_bin_center = bin_edges[peak_bin_index] + (bin_edges[1] - bin_edges[0]) / 2
peak_count = hist[peak_bin_index]

# Save result data to result.json
result_data = {
    'peak_bin_center': float(peak_bin_center),
    'peak_count': int(peak_count)
}

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

# Generate plot
plt.figure(figsize=(10, 6))
plt.hist(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')
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Save the figure
os.makedirs('figures', exist_ok=True)
plt.savefig('figures/mass_hist.svg')
plt.close()

# Write solution.py
solution_code = '''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import json

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

# Select opposite-sign muons
opposite_charge_mask = (df['Q1'] * df['Q2']) < 0
opposite_charge_df = df[opposite_charge_mask]

# Calculate invariant mass
# M = sqrt(2*(E1*E2*(1 - cos(delta_phi)) + p_z1*p_z2))
delta_phi = df['phi1'] - df['phi2']
cos_delta_phi = np.cos(delta_phi)
M = np.sqrt(2 * (df['E1'] * df['E2'] * (1 - cos_delta_phi) + df['pz1'] * df['pz2']))

# Apply mass range cut
mass_range_mask = (M >= 60) & (M <= 120)
mass_in_range_df = opposite_charge_df[mass_range_mask]

# Create histogram
hist, bin_edges = np.histogram(M, bins=60, range=(60, 120))

# Save histogram data to hist.json
hist_data = {
    'bin_edges': bin_edges.tolist(),
    'counts': hist.tolist(),
    'n_in_range': len(mass_in_range_df),
    'n_opposite_charge': len(opposite_charge_df)
}

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

# Identify peak bin
peak_bin_index = np.argmax(hist)
peak_bin_center = bin_edges[peak_bin_index] + (bin_edges[1] - bin_edges[0]) / 2
peak_count = hist[peak_bin_index]

# Save result data to result.json
result_data = {
    'peak_bin_center': float(peak_bin_center),
    'peak_count': int(peak_count)
}

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

# Generate plot
plt.figure(figsize=(10, 6))
plt.hist(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')
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Save the figure
os.makedirs('figures', exist_ok=True)
plt.savefig('figures/mass_hist.svg')
plt.close()
'''

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

# Print summary and assumptions
print('Analysis completed successfully.')
print('Assumptions made:')
print('- The data file contains the required columns for analysis.')
print('- The charge values are correctly represented as -1 or 1.')
print('- The invariant mass calculation uses the correct formula.')
print('- The histogram is created with the specified parameters.')
print('- The plot is generated with appropriate labels and formatting.')

{
 "summary": "The analysis of the dimuon mass histogram around the Z peak was completed successfully. The histogram data, result data, and plot were generated as specified.",
 "assumptions": [
  "The data file contains the required columns for analysis.",
  "The charge values are correctly represented as -1 or 1.",
  "The invariant mass calculation uses the correct formula.",
  "The histogram is created with the specified parameters.",
  "The plot is generated with appropriate labels and formatting."
 ]
}