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The knowledge of the material budget with a high precision is fundamental for measurements of direct photon production using the photon conversion method due to its direct impact on the total systematic uncertainty. Moreover, it influences many aspects of the charged-particle reconstruction performance. In this article, two procedures to determine data-driven corrections to the material-budget description in ALICE simulation software are developed. One is based on the precise knowledge of the gas composition in the Time Projection Chamber. The other is based on the robustness of the ratio between the produced number of photons and charged particles, to a large extent due to the approximate isospin symmetry in the number of produced neutral and charged pions. Both methods are applied to ALICE data allowing for a reduction of the overall material budget systematic uncertainty from 4.5% down to 2.5%. Using these methods, a locally correct material budget is also achieved. The two proposed methods are generic and can be applied to any experiment in a similar fashion.
In LHC Run 3, ALICE will increase the data taking rate significantly to 50 kHz continuous read-out of minimum bias Pb—Pb collisions. The reconstruction strategy of the online-offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration and data compression, and a posterior calibrated asynchronous reconstruction stage. Many new challenges arise, among them continuous TPC read-out, more overlapping collisions, no a priori knowledge of the primary vertex and of location-dependent calibration in the synchronous phase, identification of low-momentum looping tracks, and sophisticated raw data compression. The tracking algorithm for the Time Projection Chamber (TPC) will be based on a Cellular Automaton and the Kalman filter. The reconstruction shall run online, processing 50 times more collisions per second than today, while yielding results comparable to current offline reconstruction. Our TPC track finding leverages the potential of hardware accelerators via the OpenCL and CUDA APIs in a shared source code for CPUs and GPUs for both reconstruction stages. We give an overview of the status of Run 3 tracking including performance on processors and GPUs and achieved compression ratios.
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 Time Projection Chamber (TPC) of the ALICE experiment at the CERN LHC was upgraded for Run 3 and Run 4. Readout chambers based on Gas Electron Multiplier (GEM) technology and a new readout scheme allow continuous data taking at the highest interaction rates expected in Pb-Pb collisions. Due to the absence of a gating grid system, a significant amount of ions created in the multiplication region is expected to enter the TPC drift volume and distort the uniform electric field that guides the electrons to the readout pads. Analytical calculations were considered to correct for space-charge distortion fluctuations but they proved to be too slow for the calibration and reconstruction workflow in Run 3. In this paper, we discuss a novel strategy developed by the ALICE Collaboration to perform distortion-fluctuation corrections with machine learning and convolutional neural network techniques. The results of preliminary studies are shown and the prospects for further development and optimization are also discussed.