Speaker
Description
We describe a PU-suppression algorithm for the Global trigger using convolutional neural networks. The network operates on cell towers, exploiting both cluster topology and $E_T$ to correct for the contribution of PU. The algorithm is optimised for firmware deployment, demonstrating high throughput and low resource usage. The small size of the input and lightweight implementation enable a high degree of scalability and parallelisation. We benchmark the physics performance of our algorithm by reconstructing and calibrating small-$R$ central jets, and comparing to a range of existing algorithms. Trigger rates and thresholds are estimated, with the CNN producing the lowest thresholds for central multi-jet, jet $H_T$ and $E_T^\text{miss}$ triggers. We apply these thresholds to an SM VBF $HH\rightarrow b\bar{b}b\bar{b}$ sample and find that the highest acceptance is obtained using our algorithm.
| Would you like to be considered for an oral presentation? | Yes |
|---|
