Logo image
返回
Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network
資料集

Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network

Georges Aad, Erlend Aakvaag, Braden Keim Abbott, Kira Abeling, Nils Julius Abicht, Haider Abidi, Asmaa Aboulhorma, Halina Abramowicz, Henso Abreu, Yiming Abulaiti, …
Deutsches Elektronen-Synchrotron, DESY, Hamburg
2023
DOI: https://doi.org/10.3204/pubdb-2023-07196

摘要

CERN LHC Coll energy resolution GeV kinematics, phase space mass resolution mass, calibration network neural network transverse momentum
The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta $p_{\text{T}}>500$ GeV.

相關連結

指標

1 檢視次數

詳細資料

Logo image