End-to-end Deep Learning Reconstruction for CMS experiment
Description
One of the important aspects of searches for new physics at the Large Hadron Collider (LHC) involves the identification and reconstruction of single particles, jets and event topologies of interest in collision events. The End-to-End Deep Learning (E2E) project in the CMS experiment focuses on the development of these reconstruction and identification tasks with innovative deep learning approaches.
This project will focus on the integration of E2E code with the CMSSW inference engine for use in reconstruction algorithms in offline and high-level trigger systems of the CMS experiment.
Task ideas
- Integration/interface of E2E with the CMSSW inference engine
- Test and optimization of the E2E inference for given reconstruction task
- Integration of prototype with CMSSW Particle Flow (PF) classes
- Test and benchmarking of inference on GPUs
Expected results
- Integrated code within CMSSW classes
- Benchmark of end-to-end deep learning inference on cpu and gpu
Requirements
Python, Keras, PyTorch, C++, and some previous experience in Machine Learning.
Mentors
Please DO NOT contact mentors directly by email, and instead please send project inquiries to MLSFT-GSOC@cern.ch with Project Title in the subject and relevant mentors will get in touch with you.