activity
20172021
most citedMachine learning and parallelism in the reconstruction of LHCb and its upgrade

6 citations · 7 across the 2 of their papers we have counts for

collaborators

5 papers

hep-ex2021

Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices

Simon Akar, Gowtham Atluri, Thomas Boettcher +7

The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm for identifying and lo…

physics.ins-det20201 cited

An updated hybrid deep learning algorithm for identifying and locating primary vertices

Simon Akar, Thomas J. Boettcher, Sarah Carl +5

We present an improved hybrid algorithm for vertexing, that combines deep learning with conventional methods. Even though the algorithm is a generic approach to vertex finding, we…

hep-ex2018

Design and performance of the LHCb trigger and full real-time reconstruction in Run 2 of the LHC

R. Aaij, S. Akar, J. Albrecht +138

The LHCb collaboration has redesigned its trigger to enable the full offline detector reconstruction to be performed in real time. Together with the real-time alignment and calibra…

hep-ex2018

Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era

LHCb collaboration, I. Bediaga, M. Cruz Torres +828

The LHCb Upgrade II will fully exploit the flavour-physics opportunities of the HL-LHC, and study additional physics topics that take advantage of the forward acceptance of the LHC…

physics.ins-det20176 cited

Machine learning and parallelism in the reconstruction of LHCb and its upgrade

Marian Stahl

After a highly successful first data taking period at the LHC, the LHCb experiment developed a new trigger strategy with a real-time reconstruction, alignment and calibration for R…