Self-Supervised Learning for End-to-End Particle Reconstruction for the CMS Experiment


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.

Transfer learning and Self-Supervised learning techniques often outperform traditional models that are trained from scratch. State-of-the-art SSL (Self-Supervised Learning) techniques, when applied to unlabelled data, learn useful features without annotation. Later, when these models are finetuned with the limited labelled data, a higher level of generality is observed. This project explores Self-Supervised Contrastive/Clustering/Distillation based Learning techniques to build robust models that perform better than the existing supervised baselines.


Total project length: 175/350 hours.

Task ideas

Expected results

Difficulty level



C++, Python, PyTorch, Tensorflow and some previous experience in Deep Learning.


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