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‎_gsocorgs/2024/ucl.md‎

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---
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title: "University College London"
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author: "Jon Butterworth"
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layout: default
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organization: UCLondon
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logo: UCL-logo.png
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description: |
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Founded in 1826 in the heart of London, [University College London](https://www.gla.ac.uk/) is London's leading multidisciplinary university, with more than 13,000 staff and 42,000 students from 150 different countries. It is one of the UK's leading groups in the field of particle physics.
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---
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{% include gsoc_proposal.ext %}

‎_gsocorgs/2024/uglasgow.md‎

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---
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title: "University of Glasgow"
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author: "Andy Buckley"
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layout: default
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organization: UofGlasgow
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logo: UGlasgow-logo.png
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description: |
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The [University of Glasgow](https://www.gla.ac.uk/) is a leading UK research university based in Scotland's largest city. We work on [particle physics experiments](https://www.gla.ac.uk/schools/physics/research/groups/particlephysicsexperiment/) from the LHC to neutrinos, particle theory from interpretations of Higgs and top-quark measurements to the strong force, and high-performance distributed computing.
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---
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{% include gsoc_proposal.ext %}

‎_gsocprojects/2024/project_MCnet.md‎

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---
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project: MCnet
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title: MCnet
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layout: default
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logo: mcnet-logo.png
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description: |
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[MCnet](http://montecarlonet.org) is a collaboration between the developers of
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the major collision-event modelling tools used at the [Large Hadron
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Collider](http://home.web.cern.ch/topics/large-hadron-collider) (LHC) and
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beyond. These codes are so-called "MC event generators", theoretical physics
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packages that perform quantum mechanics calculations and create simulated
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particle collisions. MC generators are an essential component
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of both modern particle physics experiments and theory, and provide a crucial
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bridge between those two communities. MCnet projects include both event
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generators themselves, and tools that connect them to both experiment and
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theory.
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summary: |
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[MCnet](http://montecarlonet.org) is a community linking the developers of
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particle-theory simulations with experimentalists and others who use and
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make tools for analysing the simulated events. Projects may be available
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from both aspects of the community.
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---
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{% include gsoc_project.ext %}

‎_gsocproposals/2024/proposal_MCnetLHAPDF.md‎

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---
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title: MCnet/LHAPDF - online dashboard and data-visualisation for parton density functions
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layout: gsoc_proposal
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project: MCnet
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year: 2024
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organization:
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- UofGlasgow
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difficulty: medium
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duration: 175
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mentor_avail: June-October
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---
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# Description
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The Large Hadron Collider smashes protons into each other at the highest
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energies humanity has ever engineered. Protons are a very convenient type of
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particle for our high-energy beams : they are plentiful, and they don't lose
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(lots of) energy like electrons do when accelerated around the LHC ring. But
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they are not *fundamental* particles: they are made up of a tightly bound
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collection of smaller particles, and to make the most out of LHC experiments we
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need to understand both what we do and don't know about the internal structure
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of the proton that these objects induce. We encode this through so-called parton
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density functions, or PDFs.
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The [LHAPDF](https://lhapdf.hepforge.org) C++ library, wrapped with a
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Python interface, is the LHC's standard system for supplying PDF data
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to both experiments and theory calculations. Over 1000 sets of PDF fits
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are available in the LHAPDF data collection.
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## Task ideas
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This project will build on the LHAPDF test suite to report the current status
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of PDF data files and validation, and allow online exploration, comparison
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and plotting of PDF functions.
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## Expected results and milestones
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* Familiarise with the LHAPDF framework from interface to calculation components;
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* Familiarise with current LHAPDF CI and regression tests;
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* Design and present a Web dashboard of PDF datasets and their validation status;
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* Add a live PDF plotting tool to the Web interface, including 1D and 2D
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comparisons of PDF functions.
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## Requirements
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* Python
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* Web coding and visualisation libraries
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* CI testing
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* git
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## Mentors
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* **[Andy Buckley](mailto:[email protected])**
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## Links
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* [LHAPDF](https://lhapdf.hepforge.org)

‎_gsocproposals/2024/proposal_MCnetRivet.md‎

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---
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title: MCnet/Rivet - multiarch Docker images from CI
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layout: gsoc_proposal
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project: MCnet
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year: 2024
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organization:
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- UofGlasgow
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difficulty: medium
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duration: 175
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mentor_avail: June-October
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---
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# Description
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[Rivet](https://rivet.hepforge.org) is a toolkit for analysing
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simulated collider events. It reconstructs the main physics processes
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underlying the many particles in Monte Carlo event graphs, in a way
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that allows new theories to be easily compared to experimental
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data. This is a key way in which we test Large Hadron Collider (LHC)
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data against ever-improving theory models, including proposals of new
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physics beyond the Standard Model.
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## Task ideas
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This project will improve the Rivet documentation and release system
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by automating generation and sharing of multi-architecture Docker images
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from the GitLab continuous integration system, along with updated,
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automatically generated documentation and web pages.
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## Expected results and milestones
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* Familiarise with the Rivet framework;
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* Familiarise with the Rivet release process, including current CI, documentation, and Docker build scripts;
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* Migrate Docker build scripts into CI, automating and removing the need for local builds;
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* Extend Docker builds to multi-architecture images optimised for x86 and Mac M1/2 processors;
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* Complete web docs migration into CI builds, enabling new website deployment.
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## Requirements
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* Python
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* Docker
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* CI testing
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* git
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## Mentors
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* **[Andy Buckley](mailto:[email protected])**
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* [Chris Gutschow](mailto:[email protected])
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## Links
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* [Rivet](https://rivet.hepforge.org)

‎gsoc/2024/mentors.md‎

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## Full Mentor List (Name, Email, Org)
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* Parth Arora [[email protected]](mailto:[email protected]) CompRes
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* Jakob Blomer [[email protected]](mailto:[email protected]) CERN
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* Andy Buckley [[email protected]](mailto:[email protected]) UofGlasgow
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* Peter Van Gemmeren [[email protected]](mailto:[email protected]) ANL
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* Chris Gutschow [[email protected]](mailto:[email protected]) UCLondon
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* Benedikt Hegner [[email protected]](mailto:[email protected]) CERN
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* Aaron Jomy [[email protected]](mailto:[email protected]) CompRes
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* Wim Lavrijsen [[email protected]](mailto:[email protected]) CompRes

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