154 research outputs found

    Time-based Reconstruction of Free-streaming Data in CBM

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    Traditional latency-limited trigger architectures typical for conventional experiments are inapplicable for the CBM experiment. Instead, CBM will ship and collect time-stamped data into a readout buffer in a form of a time-slice of a certain length and deliver it to a large computer farm, where online event reconstruction and selection will be performed. Grouping measurements into physical collisions must be performed in software and requires reconstruction not only in space, but also in time, the so-called 4-dimensional track reconstruction and event building. The tracks, reconstructed with 4D Cellular Automaton track finder, are combined into event-corresponding clusters according to the estimated time in the target position and the errors, obtained with the Kalman Filter method. The reconstructed events are given as inputs to the KF Particle Finder package for short-lived particle reconstruction. The results of time-based reconstruction of simulated collisions in CBM are presented and discussed in details

    Online Event Reconstruction in the CBM Experiment at FAIR

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    Targeting for rare observables, the CBM experiment will operate at high interaction rates of up to 10 MHz, which is unprecedented in heavy-ion experiments so far. It requires a novel free-streaming readout system and a new concept of data processing. The huge data rates of the CBM experiment will be reduced online to the recordable rate before saving the data to the mass storage. Full collision reconstruction and selection will be performed online in a dedicated processor farm. In order to make an efficient event selection online a clean sample of particles has to be provided by the reconstruction package called First Level Event Selection (FLES). The FLES reconstruction and selection package consists of several modules: track finding, track fitting, event building, short-lived particles finding, and event selection. Since detector measurements contain also time information, the event building is done at all stages of the reconstruction process. The input data are distributed within the FLES farm in a form of time-slices. A time-slice is reconstructed in parallel between processor cores. After all tracks of the whole time-slice are found and fitted, they are collected into clusters of tracks originated from common primary vertices, which then are fitted, thus identifying the interaction points. Secondary tracks are associated with primary vertices according to their estimated production time. After that short-lived particles are found and the full event building process is finished. The last stage of the FLES package is a selection of events according to the requested trigger signatures. The event reconstruction procedure and the results of its application to simulated collisions in the CBM detector setup are presented and discussed in detail

    Four-dimensional event reconstruction in the CBM experiment

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    The future heavy-ion experiment CBM (FAIR/GSI, Darmstadt, Germany) will focus on the measurements of very rare probes, which require the experiment to operate under extreme interaction rates of up to 10 MHz. Due to high multiplicity of charged particles in heavy-ion collisions, this will lead to the data rates of up to 1 TB/s. In order to meet the modern achievable archival rate, this data ow has to be reduced online by more than two orders of magnitude. The rare observables are featured with complicated trigger signatures and require full event topology reconstruction to be performed online. The huge data rates together with the absence of simple hardware triggers make traditional latency limited trigger architectures typical for conventional experiments inapplicable for the case of CBM. Instead, CBM will employ a novel data acquisition concept with autonomous, self-triggered front-end electronics. While in conventional experiments with event-by-event processing the association of detector hits with corresponding physical event is known a priori, it is not true for the CBM experiment, where the reconstruction algorithms should be modified in order to process non-event-associated data. At the highest interaction rates the time difference between hits belonging to the same collision will be larger than the average time difference between two consecutive collisions. Thus, events will overlap in time. Due to a possible overlap of events one needs to analyze time-slices rather than isolated events. The time-stamped data will be shipped and collected into a readout buffer in a form of a time-slice of a certain length. The time-slice data will be delivered to a large computer farm, where the archival decision will be obtained after performing online reconstruction. In this case association of hit information with physical events must be performed in software and requires full online event reconstruction not only in space, but also in time, so-called 4-dimensional (4D) track reconstruction. Within the scope of this work the 4D track finder algorithm for online reconstruction has been developed. The 4D CA track finder is able to reproduce performance and speed of the traditional event-based algorithm. The 4D CA track finder is both vectorized (using SIMD instructions) and parallelized (between CPU cores). The algorithm shows strong scalability on many-core systems. The speed-up factor of 10.1 has been achieved on a CPU with 10 hyper-threaded physical cores. The 4D CA track finder algorithm is ready for the time-slice-based reconstruction in the CBM experiment

    Online Event Reconstruction in the CBM Experiment at FAIR

    No full text
    Targeting for rare observables, the CBM experiment will operate at high interaction rates of up to 10 MHz, which is unprecedented in heavy-ion experiments so far. It requires a novel free-streaming readout system and a new concept of data processing. The huge data rates of the CBM experiment will be reduced online to the recordable rate before saving the data to the mass storage. Full collision reconstruction and selection will be performed online in a dedicated processor farm. In order to make an efficient event selection online a clean sample of particles has to be provided by the reconstruction package called First Level Event Selection (FLES). The FLES reconstruction and selection package consists of several modules: track finding, track fitting, event building, short-lived particles finding, and event selection. Since detector measurements contain also time information, the event building is done at all stages of the reconstruction process. The input data are distributed within the FLES farm in a form of time-slices. A time-slice is reconstructed in parallel between processor cores. After all tracks of the whole time-slice are found and fitted, they are collected into clusters of tracks originated from common primary vertices, which then are fitted, thus identifying the interaction points. Secondary tracks are associated with primary vertices according to their estimated production time. After that short-lived particles are found and the full event building process is finished. The last stage of the FLES package is a selection of events according to the requested trigger signatures. The event reconstruction procedure and the results of its application to simulated collisions in the CBM detector setup are presented and discussed in detail

    Parallel 4-Dimensional Cellular Automaton Track Finder for the CBM Experiment

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    The CBM experiment (FAIR/GSI, Darmstadt, Germany) will focus on the measurement of rare probes at interaction rates up to 10 MHz with data flow of up to 1 TB/s. It requires a novel read-out and data-acquisition concept with self-triggered electronics and free-streaming data. In this case resolving different collisions is not a trivial task and event building must be performed in software online. That requires full online event reconstruction and selection not only in space, but also in time, so-called 4D event building and selection. This is a task of the First-Level Event Selection (FLES).The FLES reconstruction and selection package consists of several modules: track finding, track fitting, short-lived particles finding, event building and event selection. The Cellular Automaton (CA) track finder algorithm was adapted towards time-slice-based reconstruction and included into the CBMROOT framework. In this article, we describe the modification done to the algorithm, as well as the performance of the developed time-based approach

    Online Event Reconstruction in the CBM Experiment at FAIR

    No full text
    Targeting for rare observables, the CBM experiment will operate at high interaction rates of up to 10 MHz, which is unprecedented in heavy-ion experiments so far. It requires a novel free-streaming readout system and a new concept of data processing. The huge data rates of the CBM experiment will be reduced online to the recordable rate before saving the data to the mass storage. Full collision reconstruction and selection will be performed online in a dedicated processor farm. In order to make an efficient event selection online a clean sample of particles has to be provided by the reconstruction package called First Level Event Selection (FLES). The FLES reconstruction and selection package consists of several modules: track finding, track fitting, event building, short-lived particles finding, and event selection. Since detector measurements contain also time information, the event building is done at all stages of the reconstruction process. The input data are distributed within the FLES farm in a form of time-slices. A time-slice is reconstructed in parallel between processor cores. After all tracks of the whole time-slice are found and fitted, they are collected into clusters of tracks originated from common primary vertices, which then are fitted, thus identifying the interaction points. Secondary tracks are associated with primary vertices according to their estimated production time. After that short-lived particles are found and the full event building process is finished. The last stage of the FLES package is a selection of events according to the requested trigger signatures. The event reconstruction procedure and the results of its application to simulated collisions in the CBM detector setup are presented and discussed in detail

    Online event reconstruction in the CBM experiment at FAIR

    No full text
    Targeting for rare observables, the CBM experiment will operate at high interaction rates of up to 10 MHz, which is unprecedented in heavy-ion experiments so far. It requires a novel free-streaming readout system and a new concept of data processing. The huge data rates of the CBM experiment will be reduced online to the recordable rate before saving the data to the mass storage. Full collision reconstruction and selection will be performed online in a dedicated processor farm. In order to make an efficient event selection online a clean sample of particles has to be provided by the reconstruction package called First Level Event Selection (FLES). The FLES reconstruction and selection package consists of several modules: track finding, track fitting, event building, short-lived particles finding, and event selection. Since detector measurements contain also time information, the event building is done at all stages of the reconstruction process. The input data are distributed within the FLES farm in a form of time-slices. A time-slice is reconstructed in parallel between processor cores. After all tracks of the whole time-slice are found and fitted, they are collected into clusters of tracks originated from common primary vertices, which then are fitted, thus identifying the interaction points. Secondary tracks are associated with primary vertices according to their estimated production time. After that short-lived particles are found and the full event building process is finished. The last stage of the FLES package is a selection of events according to the requested trigger signatures. The event reconstruction procedure and the results of its application to simulated collisions in the CBM detector setup are presented and discussed in detail

    4D Cellular Automaton Track Finder in the CBM Experiment

    No full text
    The CBM experiment (FAIR/GSI, Darmstadt, Germany) will focus on the measurement of rare probes at interaction rates up to 10MHz with data flow of up to 1 TB/s. It requires a novel read-out and data-acquisition concept with self-triggered electronics and free-streaming data. In this case resolving different collisions is a non-trivial task and event building must be performed in software online. That requires full online event reconstruction and selection not only in space, but also in time, so-called 4D event building and selection. This is a task of the First-Level Event Selection (FLES). The FLES reconstruction and selection package consists of several modules: track finding, track fitting, short-lived particles finding, event building and event selection. The Cellular Automaton (CA) track finder algorithm was adapted towards time-based reconstruction. In this article, we describe in detail the modification done to the algorithm, as well as the performance of the developed time-based CA approach

    4D Cellular Automaton Track Finder in the CBM Experiment

    No full text
    The CBM experiment (FAIR/GSI, Darmstadt, Germany) will focus on the measurement of rare probes at interaction rates up to 10MHz with data flow of up to 1 TB/s. It requires a novel read-out and data-acquisition concept with self-triggered electronics and free-streaming data. In this case resolving different collisions is a non-trivial task and event building must be performed in software online. That requires full online event reconstruction and selection not only in space, but also in time, so-called 4D event building and selection. This is a task of the First-Level Event Selection (FLES). The FLES reconstruction and selection package consists of several modules: track finding, track fitting, short-lived particles finding, event building and event selection. The Cellular Automaton (CA) track finder algorithm was adapted towards time-based reconstruction. In this article, we describe in detail the modification done to the algorithm, as well as the performance of the developed time-based CA approach
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