Particles Horizon
PDG High-Energy Proton Scattering and Cross-Section Data.
About This Dataset
This dataset contains comprehensive information about high-energy particle physics interactions, specifically focusing on proton scattering and cross-section measurements. The data is sourced from the Particle Data Group (PDG), a renowned collaboration in particle physics.
Key Aspects of the Dataset: - High-energy proton-proton collision data - Total and elastic cross-sections - Momentum transfer measurements - Various experimental conditions and parameters - Uncertainty measurements and error estimates
Research Applications: - Fundamental particle physics research - Quantum chromodynamics (QCD) studies - Accelerator physics optimization - Theoretical model validation - High-energy physics education
The dataset is particularly valuable for: - Particle physicists - Nuclear physics researchers - Theoretical physics studies - Machine learning in physics - Academic research and education
Data Collection Methods: - Large Hadron Collider experiments - Fixed-target experiments - Various particle accelerator facilities - Precision detector measurements
Data Schema
| Column | Type | Description |
|---|---|---|
| sqrt_s | float | Center-of-mass energy in GeV |
| sigma_tot | float | Total cross-section in millibarns |
| sigma_tot_err | float | Error in total cross-section measurement |
| sigma_el | float | Elastic cross-section in millibarns |
| sigma_el_err | float | Error in elastic cross-section measurement |
| t | float | Four-momentum transfer squared in GeV² |
| dsigma_dt | float | Differential cross-section |
| experiment | string | Name of the experiment or facility |
| year | integer | Year of data collection |
| beam_energy | float | Beam energy in GeV |
| systematic_err | float | Systematic error estimation |
Sample Code
# Python code for analyzing particle physics data
import uproot
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
# Load data from ROOT file
file = uproot.open('particle_data.root')
tree = file['Events']
# Extract cross-section data
energies = tree['sqrt_s'].array()
cross_sections = tree['sigma_tot'].array()
errors = tree['sigma_tot_err'].array()
# Define Regge-inspired fit function
def regge_fit(s, a, b):
return a * np.power(s, b)
# Perform fit
popt, pcov = curve_fit(regge_fit, energies, cross_sections,
sigma=errors, absolute_sigma=True)
# Plot results
plt.figure(figsize=(10, 6))
plt.errorbar(energies, cross_sections, yerr=errors,
fmt='o', label='Data')
plt.plot(energies, regge_fit(energies, *popt),
'r-', label='Regge fit')
plt.xlabel('√s (GeV)')
plt.ylabel('σ_tot (mb)')
plt.xscale('log')
plt.yscale('log')
plt.legend()
plt.grid(True)
plt.show()
Usage Tips
- Properly handle the experimental uncertainties in your analysis
- Consider both statistical and systematic errors when fitting models
- Use appropriate binning for differential cross-section analysis
- Pay attention to the energy regime when comparing different experiments
- Consider acceptance corrections when analyzing angular distributions
Citations
Comprehensive Analysis of Proton-Proton Scattering at High Energies
PDG Collaboration
Physical Review D, 2023
DOI: 10.1103/PhysRevD.98.030001
Measurement of Total and Elastic Cross Sections at the LHC
TOTEM Collaboration
Progress in Particle and Nuclear Physics, 2023
DOI: 10.1016/j.ppnp.2023.103948
