| Autor: |
Pol Adrian Alan, Aarrestad Thea, Govorkova Katya, Halily Roi, Kopetz Tal, Klempner Anat, Loncar Vladimir, Ngadiuba Jennifer, Pierini Maurizio, Sirkin Olya, Summers Sioni |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
EPJ Web of Conferences, Vol 251, p 04027 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2100-014X |
| DOI: |
10.1051/epjconf/202125104027 |
| Popis: |
We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of calorimeter cells and using a Single Shot Detection network, called Jet-SSD. The model performs simultaneous localization and classification and additional regression tasks to measure jet features. We investigate TernaryWeight Networks with weights constrained to {-1, 0, 1} times a layer- and channel-dependent scaling factors. We show that the quantized version of the network closely matches the performance of its full-precision equivalent. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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