Speaker
Description
Precise reconstruction of high-energy neutrino interactions at the LHC is critical for the physics program of the proposed FASERCal detector, an off-axis neutrino detector for the FASER experiment during LHC Run 4, enabling precision measurements of TeV-scale neutrino interactions in the far-forward region. The detector's highly granular, 3D voxelized geometry produces sparse data that challenges conventional reconstruction techniques. By leveraging a masked autoencoder (MAE) pre-training scheme, our model learns robust representations of particle shower development, overcoming the limitations of standard supervised learning. It uses submanifold sparse convolutions for patching, is trained via a multi-task objective combining patch energy reconstruction and semantic segmentation, and the resulting encoder is fine-tuned for multi-task classification and kinematic regression with task-specific cross-attention heads. The framework achieves high-purity identification of electron and muon neutrino events, and provides first indications of sensitivity to tagging rare tau neutrino events. We demonstrate that this approach achieves highly accurate reconstruction of particle showers, providing precise estimates of the energy and missing transverse momentum, as well as reliable reconstruction of lepton and jet momenta for every event, even in complex topologies.
| I read the instructions above | Yes |
|---|