Foundations

Computational High Energy Physics

Simulation, numerical methods, and computational tools for exploring particle physics.

Overview

Understand the project, then explore the work.

Resources open inside this site. Guides, papers, figures, notebooks, and source files are organized around the project’s story.

About this project

Computational High Energy Physics is a field of study that focuses on using computational techniques and tools to understand and analyze the fundamental nature of the universe and the fundamental particles that make it up. This field is particularly relevant to the study of high energy physics, which involves the study of particles and phenomena that occur at extremely high energies, such as those found in particle accelerators and cosmic rays.

In Computational High Energy Physics, researchers use a range of techniques and tools, including numerical simulations, data analysis, and machine learning, to study and model the behavior of these fundamental particles and the forces that govern them. These techniques allow researchers to make predictions about the behavior of particles and forces under different conditions, and to test these predictions against experimental data.

Computational High Energy Physics plays a vital role in our understanding of the fundamental laws of the universe and the forces that shape it. It is also an important tool for the development of new technologies and advances in a wide range of fields, including medicine, materials science, and energy production.

This repository contains projects and code relating to the field of Computational High Energy Physics, ranging from the simulation of Lattice Quantum Chromodynamics to Machine Learning and Quantum Machine Learning projects in the field of Experimental High Energy physics.

Collections

Module

Coursera Course Solutions

12 curated items

Section notes

In this programming assignment your task is to analyse the mass spectrum of particle decaying into muon-antimuon (dimuon) pairs and figure out the mass of Z boson. Of course, the mass of Z boson can be looked up at the PDG, but it would much more fun to measure it yourself!

At week1 release, you can find the CSV file with dimuon events. If you make a histogram of mass distribution, you can fit a mixture of parametrised signal and background distributions into the shape of the histogram. The signal corresponds to a peak-like shape. The background for simplicity we estimate as a flat level. The parameters of the mixture will be the mass of Z boson and other estimations of the significance and uncertainty of the measurement.

To pass this assignment you have to fit the mixture of 1) Gaussian distribution and 2) flat distribution using scikit-optimise toolkit as it is described in the notebook here. Thus you obtain parameters of those distributions: 1) centre of the Gaussian, it's peak height, and deviation; 2) level of the background. All those parameters you have to submit for the grading as described in the notebook.

The dataset for this assignment is taken from CERN opendata portal http://opendata.cern.ch/record/545

NotebookMass Of Z Boson NotebookParticle Identification NotebookWeek 3 NotebookWeek 4 NotebookWeek 5 PaperLevel 1 Lecture

Supporting material in Coursera Course Solutions6 items

PaperLecture 2 PaperLecture 3 SourceUtils PaperLecture 4 PaperLecture 5 SourceUtils

Module

Deep learning for HEP

4 curated items · 33 result figures

Section notes

![Binder](http://mybinder.org/repo/YaleATLAS/CERNDeepLearningTutorial)

NotebookData Handling NotebookDeeplearning Intro NotebookJet Images UnavailableCombinedrnn Tutorial Progress

Module

Equation of state: critical QCD

26 curated items

Section notes

Lattice QCD simulations and finding critical point in QCD transitions

This is an updated version of the BEST Collaboration program producing an EoS matching lattice QCD at muB=0, and containing a critical point in the 3D Ising universality class, in a parametrized form. It allows for different choices of constraints on the shape of the critical line, which reduce the number of parameters, as well as the inclusion of no critical point, corresponding to a Taylor expansion of lattice QCD result only.

Moreover, it allows for the choice of strangeness neutrality conditions: nS = 0 && nQ = 0.4 nB or the case of only baryon number: muS = muQ = 0

THE INPUT

UnavailableEo S UnavailableFunctions SourceFunctions SourceNr D SourceNrutil D SourceVariables

Supporting material in Equation of state: critical QCD20 items

Module

Lattice equation of state

21 curated items

Section notes

Equation of State with Baryon Number, Electric charge and Strangeness Chemical Potential

Lattice Based QCD

Quantum Chromodynamics Equation of State using all three conserved quantities of Baryon number, Electric charge and Strangeness Chemical Potential.

#### INPUT : We need to provide the lattice simulated data of the coefficients of the taylor expansion, to calculate the value of pressure , that can be used to calculate other thermodynamic values.

The required input is a .dat file name "coefficients.dat" which contains the parameters for the parameterization of the taylor coefficients up to an order 4, in total there are 22 coefficients.