This git page has been created to gather all the "hands-on" material of the course Research-based particle physics (FYS5555). Se more details below.
To copy all the material on your PC, in the terminal do git clone https://github.com/eirikgr/FYS5555.git
Installation instructions (FOR ALL EXCEPT SUSYPHENO - see below for how to setup SUSYPheno software)
To get all the software needed to run the programs the easiest is to install anaconda as described below.
For other OSs see: https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html#regular-installation
The following only needs to be done once:
wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
bash Anaconda3-2020.11-Linux-x86_64.sh
Follow the instructions on the screen.
Recomandation: When the installer asks Do you wish the installer to initialize Anaconda3 answer no to avoid the conda environment to be enabled by default whenever you start a new shell.
More details on installation can be found here:
Linux: https://docs.anaconda.com/anaconda/install/linux/
Activate anaconda in your terminal
source <path-to-where-anaconda-is-installed>/etc/profile.d/conda.sh
Build the environment (using the python3 yml file in the github repo) - this may take some time
conda env create -f environment_fys5555_py3.yml
Then load the environement, and your good to go!
conda activate fys5555_py3
If you have installed conda as described above, when you start a new shell, all you have to do is
source <path-to-where-anaconda-is-installed>/etc/profile.d/conda.sh
conda activate fys5555_py3
The SUSYPheno package uses python2 and thus a slightly different setup is needed. It follows similar steps as above.
wget https://repo.anaconda.com/archive/Anaconda2-2018.12-Linux-x86_64.sh
bash Anaconda2-2018.12-Linux-x86_64.sh
Follow the instructions on the screen.
Recomandation: When the installer asks Do you wish the installer to initialize Anaconda2 answer no to avoid the conda environment to be enabled by default whenever you start a new shell.
More details on installation can be found here:
Linux: https://docs.anaconda.com/anaconda/install/linux/
Build the environment (using the python2 yml file in the githug repo)
conda env create -f environment_fys5555_py2.yml
Then load the environement, and your good to go!
conda activate fys5555_py2
If you have installed conda as described above, when you start a new shell, all you have to do is
source <path-to-where-anaconda-is-installed>/etc/profile.d/conda.sh
conda activate fys5555_py2
This git page has been created to gather all the "hands-on" material of the course Research-based particle physics (FYS5555).
To copy all the material on your PC, in the terminal do git clone https://github.com/eirikgr/FYS5555.git
- CompHEP is a software package and event generator used to compute cross-sections. Follow the instructions on the following readme file for its installation: https://github.com/eirikgr/FYS5555/blob/master/CompHEP/README.md.
- You will need ROOT to do any analysis so it's better to install it once and for all. The best and tested way to install ROOT (which works with C++ and Python3) is via Anaconda which also contains all the machine learning packages; forget about other complicated ways to install ROOT. Follow the instructions given here: https://github.com/eirikgr/FYS5555/blob/master/Anaconda_setup/README.md
- An example dilepton (cut-and-count) analysis on the open data has been provided in the folder "Conventional_Analysis". This can be used as a skeleton to plot histograms and for further analyses. Instructions are found in https://github.com/eirikgr/FYS5555/blob/master/Conventional_Analysis/README.md
- The statistical analysis on cross-section limits can be found in the "Statistics" folder. Instructions are found in https://github.com/eirikgr/FYS5555/blob/master/Statistics/README.md.
- The SUSYPheno folder contains all the materials for the Supersymmetry part of the course. The instructions can be found in https://github.com/eirikgr/FYS5555/blob/master/SUSYPheno/README.md.
- The machine learning analysis is found in the folder "ML_analysis". This section is still work in progress and will be finalized soon.