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!
2024 Program
Successful Projects
Contributor
atell krasnopolsky
Mentor
Vassil Vassilev, Vaibhav Thakkar, Petro Zarytskyi
Organization
CERN-HSF
Implement Differentiating of the Kokkos Framework in Clad
The goal is to implement the differentiation of the Kokkos framework including the support of Kokkos functors, lambdas, methods such as parallel_for,...
Contributor
Vedant Mehra
Mentor
Neel Shah, Lorenzo Moneta, Sanjiban Sengupta
Organization
CERN-HSF
SOFIE Developments - Inference Code Generation for Deep Learning models
SOFIE aims to streamline the integration of deep learning models into the ROOT scientific software framework through efficient inference code...
Contributor
Isaac Morales
Mentor
Vassil Vassilev, Lukas Breitwieser
Organization
CERN-HSF
Improving performance of BioDynaMo using ROOT C++ Modules
BioDynaMo, a powerful agent-based simulation platform, utilizes ROOT for functionalities like statistical analysis, random number generation, and IO....
Contributor
Ishan Darji
Mentor
Maciej Szymański, Peter van Gemmeren
Organization
CERN-HSF
Lossless compression of raw data for the ATLAS experiment at CERN
The goal of this project is to study the performance and effectiveness of various compression algorithms, specifically on ATLAS RAW data. The ATLAS...
Contributor
Christina Koutsou
Mentor
Vassil Vassilev, parth_07
Organization
CERN-HSF
Reverse-mode automatic differentiation of GPU kernels using Clad
Nowadays, the rise of AI has shed light into the power of GPUs. The notion of General Purpose GPU Programming is becoming more and more popular and...
Contributor
Aryabhatta Dey
Mentor
Alexander Richards, Ulrik, Mark Smith
Organization
CERN-HSF
Incorporating a Large Language Model in Ganga to assist users
Ganga is a tool for composing, running, and tracking computing jobs across a variety of backends and application types. Ganga primarily runs as a...
Contributor
Arnab Mukherjee
Mentor
agbuckley
Organization
CERN-HSF
(MCnet/LHAPDF) Online dashboard and data-visualisation for parton density functions
At the Large Hadron Collider (LHC), protons collide at the highest energies achieved by humanity, unravelling the particles within. To decode these...