Speaker
Malte Algren
(UNIGE)
Description
We present a novel method for pile-up removal of pp interactions using variational inference with diffusion models, called Vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed. This results in an estimate of the full posterior over hard-scatter jet constituents, which has not yet been explored in the context of pile-up removal. We evaluate the performance of Vipr in a sample of jets from simulated tt¯ events overlain with pile-up contamination. Vipr outperforms SoftDrop in predicting the substructure of the hard-scatter jets over a wide range of pile-up scenarios.
| AI keywords | Diffusion; Transformers; variational inference |
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Primary authors
Chris Pollard
(University of Warwick)
Dr
John Raine
Malte Algren
(UNIGE)
Tobias Golling
(University of Geneva)
