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View article: Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC
Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC Open
In this work, we present a study on ways that tracking algorithms can be improved with machine learning (ML). We base this study on the line segment tracking (LST) algorithm that we have designed to be naturally parallelized and vectorized…
View article: Generalizing mkFit and its Application to HL-LHC
Generalizing mkFit and its Application to HL-LHC Open
mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and data-level parallelism. In the past few years the project transitioned from the R&D phase to deployment in the Run-3 offlin…
View article: CTD2022: Line Segment Tracking in the HL-LHC
CTD2022: Line Segment Tracking in the HL-LHC Open
The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use m…
View article: Line Segment Tracking in the HL-LHC
Line Segment Tracking in the HL-LHC Open
The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use m…