Jul 6 – 8, 2021
Europe/Zurich timezone

Lightweight Jet Reconstruction as an Object Detection Task

Jul 7, 2021, 3:40 PM
20m

Speaker

Adrian Alan Pol (CERN)

Description

We apply object detection techniques based on convolutional blocks to jet reconstruction and identification at the CERN Large Hadron Collider. We use particles reconstructed through a Particle Flow algorithm to represent each event as an image composed of a calorimeter and tracker cells as input and a Single Shot Detection network, called PFJet-SSD. The network performs simultaneous localization, classification and auxiliary regression tasks to measure jet features. We investigate Ternary Weight Networks with weights quantized to {-1, 0, 1} set, times a layer- and channel-dependent scaling factors for reducing memory and latency constraints. We show that the quantized version of the network closely matches the performance of its full-precision equivalent while both outperform the physics baseline. Finally, we report the inference latency on Nvidia Tesla T4.

Affiliation CERN

Primary author

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Presentation materials