EPJ Web Conf.
Volume 127, 2016Connecting the Dots 2016
|Number of page(s)||13|
|Published online||15 November 2016|
Online Reconstruction and Calibration with Feedback Loop in the ALICE High Level Trigger
1 Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
2 CERN, Geneva, Switzerland
3 INFN Sezione Bologna, Italy
4 Goethe University, Frankfurt, Germany
5 Technical University, Munich, Germany
6 University of Heidelberg, Germany
a e-mail: firstname.lastname@example.org
Published online: 15 November 2016
ALICE (A Large Heavy Ion Experiment) is one of the four large scale experiments at the Large Hadron Collider (LHC) at CERN. The High Level Trigger (HLT) is an online computing farm, which reconstructs events recorded by the ALICE detector in real-time. The most computing-intensive task is the reconstruction of the particle trajectories. The main tracking devices in ALICE are the Time Projection Chamber (TPC) and the Inner Tracking System (ITS). The HLT uses a fast GPU-accelerated algorithm for the TPC tracking based on the Cellular Automaton principle and the Kalman filter. ALICE employs gaseous subdetectors which are sensitive to environmental conditions such as ambient pressure and temperature and the TPC is one of these. A precise reconstruction of particle trajectories requires the calibration of these detectors. As our first topic, we present some recent optimizations to our GPU-based TPC tracking using the new GPU models we employ for the ongoing and upcoming data taking period at LHC. We also show our new approach to fast ITS standalone tracking. As our second topic, we present improvements to the HLT for facilitating online reconstruction including a new flat data model and a new data flow chain. The calibration output is fed back to the reconstruction components of the HLT via a feedback loop. We conclude with an analysis of a first online calibration test under real conditions during the Pb-Pb run in November 2015, which was based on these new features.
© The Authors, published by EDP Sciences, 2016
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