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This work represents every object in a jet in a single shared token vocabulary and train one model to map any subset of these modalities to any other, with the autoregressive model particularly faithful to output from Geant4.
We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling task. Kalman filters and linearized least-squares fits have been the classical standard approach for this task: they are optimal estimators for sparsely sampled linear-Ga...
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