Reconstructing hadronically decaying tau leptons with a jet foundation model

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Abstract
The limited availability and accuracy of simulated data has motivated the use of foundation models in high energy physics, with the idea to first train a task-agnostic model on large and potentially unlabeled datasets. This enables the subsequent fine-tuning of the learned representation for specific downstream tasks, potentially requiring much smaller datasets to achieve performance comparable to models trained from scratch on larger datasets. We study how OmniJet- \alpha α , one of the proposed foundation models for particle jets, can be used on a new set of tasks, and on a new dataset, in order to reconstruct hadronically decaying \tau τ leptons. We show that the pretraining can successfully be utilized for this multi-task problem, improving the resolution of momentum reconstruction by about 50\% 50 % when the pretrained weights are fine-tuned, compared to training the model from scratch. While much work remains ahead to develop generic foundation models for high-energy physics, this early result of generalizing an existing model to a new dataset and to previously unconsidered tasks highlights the importance of testing the approaches on a diverse set of datasets and tasks.
Funding Information
  • Deutsche Forschungsgemeinschaft (390833306)
  • Deutsches Elektronen-Synchrotron
  • Eesti Teadusagentuur (PSG864)
  • Eesti Teadusagentuur (RVTT3-KBFI)
  • European Regional Development Fund (TK202)

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