Deep Learning Approach to Track Reconstruction in the upgraded VELO

Rinnert, Kurt and Cristoforetti, Marco
(2019) Deep Learning Approach to Track Reconstruction in the upgraded VELO. .

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<jats:p>The LHCb experiment will undergo a major upgrade for LHC Run III, scheduled to start taking data in 2021. In order to exploit the full physics potential of the upgraded detector, LHCb will employ a high level event filter entirely implemented in software. The event filter has to operate in real time at the 40 MHz LHC bunch crossing rate. The LHCb collaboration is currently exploring a variety of new new computing paradigms in order to cope with the challenges posed by the high data taking rates.</jats:p> <jats:p>One contribution to this effort is the application of Machine Learning (ML) techniques to particle track reconstruction.</jats:p> <jats:p>We explore an ML approach to the track reconstruction in the upgraded LHCb Vertex Locator (VELO). The reconstruction algorithm is evaluated with fully simulated Minimum-Bias data that reflects the input to the real time event filter. We have achieved a reasonable track reconstruction efficiency and low ghost rate with our first approach and are currently exploring several methods to further quality of the track reconstruction.</jats:p>

Item Type: Conference or Workshop Item (Unspecified)
Depositing User: Symplectic Admin
Date Deposited: 22 Oct 2020 09:04
Last Modified: 18 Jan 2023 23:26
DOI: 10.1051/epjconf/201921406038
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