The SFT is part of CERN (European Organization for Nuclear Research).

The SFT (Software for Experiments) group is part of CERN (European Organization for Nuclear Research, http://www.cern.ch), and focuses on providing common software for its experiments. CERN is one of the world’s largest and most exciting centers for fundamental physics research. Experiments at CERN have probed the fundamental nature of matter and the forces which affect it. CERN is also the birthplace of the World Wide Web (http://info.cern.ch), invented by Tim Berners-Lee. The SFT group's efforts, like most of CERN's current activities, are directed towards the world’s highest-energy elementary particle accelerator - the Large Hadron Collider (LHC, http://public.web.cern.ch/public/en/lhc/lhc-en.html) and its experiments. There are four large experiments at the LHC, which seek to expand the frontiers of knowledge and complete our understanding of the constituents of matter and their interactions, of the conditions in the first instants after the Big Bang and of the differences between matter and anti-matter. During 2012, ATLAS and CMS announced the discovery of a new boson, which has been confirmed recently to have the properties of a Higgs boson - similar to the one required by the Standard Model of Particle Physics. The vast majority of our GSoC projects do not require any physics knowledge. Operating the LHC and running each experiment requires a large amount of software. A large part of this software is common and open source. The open source software spans the range from system software to more specialized physics-oriented tools and toolkits.

The projects to which students can contribute span several software projects: SixTrack, accelerator simulation; the Geant4/Geant-V detector simulation toolkit the ROOT software framework for storing and analyzing the data of the LHC experiments including machine learning software; CERNVM, a baseline Virtual Software Appliance for the participants of CERN LHC experiments. different computer platforms.

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Technologies

  • c++
  • python
  • javascript
  • clang

Topics

  • Science and Medicine
  • numerical and data analysis software
  • simulation software
  • cloud
  • machine learning
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CERN SFT 2016 Projects

  • Oleg.jakushkin
    BLonD code optimization strategy for parallel and concurrent architectures
    Our objective is to determine the best architectural and parallelization options for the BLonD future C++ code base. Project includes: Existing...
  • Simon Pfreundschuh
    GPU-accelerated Deep Neural Networks in TMVA
    During recent years deep learning techniques haven proven extremely powerful in many different applications and have successfully been applied to...
  • Joel Fuentes
    Implementation of task-based transport for GeantV
    The current parallelism model of GeantV is data-oriented where a static set of threads is predefined to perform the work, the set of transport...
  • Attila Bagoly
    Integrating Machine Learning in Jupyter Notebooks
    Toolkit for Multivariate Data Analysis (TMVA) is a framework (part of data analysis framework ROOT) which contains ML packages, frequently used by...
  • gdphys
    Integration of Spark parallelisation in TMVA
    The proposed project involves the support for Spark parallelization in TMVA. Machine learning procedures like k-fold cross validation, hyper...
  • Dmitry Sorokin
    Multistep methods for integrating trajectory in field
    Implementation of Adams multistep methods in Geant4
  • vikasnt
    New Physics Model in Sixtrack package
    SixTrack is a 6D particle tracking code used to compute the trajectories of individual relativistic charged particles in circular accelerators. It...
  • Peter Whidden
    Performance 3D Web Graphics with Interactive Features for JSRoot
    The ROOT project is developing a JavaScript library for reading and rendering ROOT objects in modern web browsers. The rendering of 3D objects is...
  • Aditi Dutta
    Reflection-based Python-C++ language bindings: cppyy - Integrate the Cling backend into PyPy/cppyy
    For the purpose of High Energy Physics (HEP) Experiments, the framework required should be able to support the scale and complexity of HEP codes....
  • Abhinav Moudgil
    TMVA Project in Machine Learning
    Toolkit for Multivariate Data Analysis (TMVA), integrated into the ROOT framework hosts a variety of machine classification methods which have become...
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2016