Speaker
Description
We describe the principles and performance of the first-level ("L1") hardware track trigger of Belle II, based on neural networks. The networks use as input the results from the standard \belleii trigger, which provides ``2D'' track candidates in the plane transverse to the electron-positron beams. The networks then provide estimates for the origin of the 2D track candidates in direction of the colliding beams (z-vertex), as well as their polar emission angles theta. Given the z-vertices of the neural tracks allows identifying events coming from the collision region (z ~ 0), and suppressing the overwhelming background from outside by a suitable cut d. Requiring |z| < d for at least one neural track in an event with two or more 2D candidates will set an L1 trigger. The networks also enable a minimum bias trigger, requiring a single 2D track candidate validated by a neural track with a momentum larger than 0.7 GeV in addition to the |z| condition. The momentum of the neural track is derived with the help of the polar angle theta.
Significance
The Level 1 Neural Network Track Trigger is the first of its kind operating in a high energy physics experiment. It provides even a minimum bias single track trigger, also the first of its kind in an electron-positron experiment.
References
Talk given by collaborator on last year's ACAT conference, giving the status of the hardware development. A publication for NIMA is in preparation.
| Experiment context, if any | The neural trigger is operating at the Belle II experiment at KEK, Japan |
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