CERN-HSF
Umbrella for Particle Physics-related projects
Umbrella for Particle Physics-related projects
CERN-HSF (High-Energy Physics Software Foundation) is the umbrella organization for high-energy physics-related projects in GSoC. The HEP Software Foundation (http://hepsoftwarefoundation.org/) facilitates the coordination of common international efforts in high-energy physics software and computing.
CERN (European Organization for Nuclear Research, https://home.cern) has participated in GSoC since 2011 as the CERN-SFT group, which provides common software for CERN's experiments. In 2017, the program expanded to include many software projects from the whole field of high-energy physics. The vast majority of our GSoC projects do not require any physics knowledge.
The experiments at CERN, such as the Large Hadron Collider, the world’s largest and most powerful particle accelerator (http://home.cern/topics/large-hadron-collider) try to answer fundamental questions about the Universe. For example, what is the nature of mass? What are the elementary building blocks of the Universe? What was the early Universe like? What is the nature of dark matter and dark energy? Why is there an asymmetry between matter and antimatter? In 2012, LHC experiments announced the discovery of a new particle, the Higgs Boson, that helps explain how particles obtain mass. Also, CERN is the birthplace of the World Wide Web. Today, particle physicists are working on analyzing the data from the experiments to study the properties of the newly discovered particle and to search for new physics, such as dark matter or extra dimensions. This requires a lot of sophisticated software.
The open-source high-energy physics projects to which students can contribute during GSoC span many high-energy physics software projects: data analysis, detector and accelerator simulation, event reconstruction, data management and many others. We look forward to your contributions!
2023 Program
Successful Projects
Contributor
Somya Bansal
Mentor
Edward Moyse, Juraj Smieško
Organization
CERN-HSF
Improving the sense of scale and navigation in high energy physics event visualization
This project aims to improve the scale and navigation inside the Phoenix application which is basically an Angular-based web application written in...
Contributor
Chenguang Guan
Mentor
Dalila Salamani, Piyush Raikwar
Organization
CERN-HSF
Geant4-FastSim: Transformer-based architecture for fast shower simulation
Calorimeter is one of the most important components of the Large Hadron Collider (LHC) experiments, detecting energy loss of particles after...
Contributor
Smit Shah
Mentor
Baidyanath Kundu, Vassil Vassilev
Organization
CERN-HSF
Enable cross-talk between Python and C++ kernels in xeus-clang-REPL by using Cppyy
xeus-clang-REPL is a C++ kernel for Jupyter notebooks using clang-REPL as its C++ Interpreter. Cppyy is an automatic, run-time, Python-C++ bindings...
Contributor
Muhammad Aditya Hilmy
Mentor
Diogo Castro, Enric Tejedor, Luca Canali
Organization
CERN-HSF
Leverage Spark Connect for interactive data analysis in Jupyter Notebooks
CERN uses a service called Service for Web Analysis (SWAN) to perform analyses on scientific data, which is built on top of Jupyter notebook....
Contributor
Neel Shah
Mentor
Sanjiban Sengupta, Moneta
Organization
CERN-HSF
ROOT - TMVA SOFIE Developments - Inference Code Generation for Deep Learning models
Toolkit for Multivariate Analysis (TMVA) is a multi-purpose machine learning toolkit integrated into the ROOT scientific software framework, used in...
Contributor
Daemond Zhang
Mentor
Vassil Vassilev, parth_07
Organization
CERN-HSF
Improve automatic differentiation of object-oriented paradigms using Clad
I would like to apply for the Improve automatic differentiation of object-oriented paradigms using Clad project. This project is mainly focused on...
Contributor
Vaibhav Thakkar
Mentor
Vassil Vassilev, parth_07
Organization
CERN-HSF
Implement vector mode in forward mode automatic differentiation in Clad
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to numerically evaluate the derivative of a function...