Learning the Latent Structure with Diffusion Models


In the search for new physics at the Large Hadron Collider (LHC) it is necessary to simulate billions of high-energy collision events at high fidelity. One approach is to use accurate generative modeling to sample from latent space distribution. This project focuses on diffusion models as means of learning the latent structure to produce accurate multidimensional distribution of point cloud data of hits produced by particle interactions with the detectors.


Total project length: 175/350 hours.

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Please use this link to access the test for this project.



Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.

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