Summer Projects 24

Git repo for Summer Projects 2024. We will be investigating various ways of improving the discrimination between observed and unobserved Standard Model processes, and improving the kinematic reconstruction for such processes.

Contact Details

Ethan Simpson - ethan.simpson@manchester.ac.uk

Zihan Zhang - zihan.zhang@manchester.ac.uk Skype name: live:exapples_3

Yvonne Peters - yvonne.peters@manchester.ac.uk

Physics Primer

I wrote a quick physics primer, with overleaf here

Technical Primer

Some computer skills like information about command-line interfaces can be found in TechnicalPrereqs.md

Possible requirements:

• A place to store and manipulate data: own workshop, CSF, Noether.
• A terminal
• Jupyter notebook environment

Where/How to work

Best practice is to work in way that makes you feel most comfortable: this will probably be on your own laptop. Here you can run code in Jupyter / Google Colab notebooks (in a browser), or run scripts from the terminal. One of the best modern tools is VSCode: https://code.visualstudio.com/, which comes itself with a built in terminal, plus you cna build Jupyter notebooks in VSCode which is how I do most code development.

HEP Software Foundation

HEP software skills: https://hsf-training.github.io/analysis-essentials/#

HEP data analysis frameworks:

These are used to analyse the data structures we store particle collision information in: reading/writing that data, processing it and applying transformations to it, making histograms and plotting results. Particle physics data is generally stored in .root files

ROOT

https://root.cern/ - C++ and Python through PyROOT. ROOT is the main tool people use to do ATLAS analyses. ROOT installation guide: https://root.cern/install/#install-via-a-package-manager. ROOT is slightly harder to pick up from a Python background. If you want to stay in particle physics, you will probably have to use "proper ROOT" eventually.

Scikit-HEP

https://scikit-hep.org/ - Python-based, "modern" alternative to ROOT. More pythonic syntax. More aligned with "data science" software stack, so arguably more applicable for more general data science. Machine-learning tools in general have to interface to this method. Short example of doing a quick analysis using Scikit-HEP tools from Andy Pilkington available here. This uses uproot to load the ROOT file, uses vector to create 4-momentum objects which can manipulated, uses matplotlib to create a histogram and plot it (and in the background uses awkward-arrays as the array type).

Simulated Data

In time we can store data on shared diskspace on Manchester CSF.

For now I have put some ttbar samples in the following Google Drive: https://drive.google.com/drive/folders/1qIEkxLa28mjkq9DHc2bAvHzpoY0LPj-I?usp=sharing

Currently it contains:

• ttbar_200k_dilep.root = ROOT file with 200,000 ttbar dilepton events. This sample contains final-state particles only (after everything decayed), no top quarks.
• ttbar_1M_parton_dilep.root = ROOT file with 1million ttbar dilepton events. This sample contains the truth particles.

Useful Software Links

HyPER

We will use this to generate the simulated data MadLAD