The ALICE data quality monitoring system

Haller, B. von; Telesca, A.; Chapeland, S.; Carena, F.; Carena, W.; Barroso, V. Chibante; Costa, F.; Dénes, E.; Divià, R.; Fuchs, U.; Simonetti, G.; Soós, C.; Vyvre, P. Vande

doi:10.1088/1742-6596/331/2/022030

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…The data acquisition software was based on DATE, the Data Acquisition and Test Environment from the ALICE collaboration [19]. The event reconstruction software, containing among others the clustering algorithms, has been further developed from the data quality monitoring framework of ALICE, AMORE [20]. Since the SRS/APV system does not allow self-triggering, the 1 The measurements using this setup were performed at Helsinki Institute of Physics.…”

Section: Setupmentioning

confidence: 99%

Hole Misalignment and Gain Performance of Gaseous Electron Multipliers

Brücken,

Heino,

Hildén

et al. 2021

Preprint

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It is well known and has been shown that the gain performance of Gaseous Electron Multipliers (GEM) depends on the size of the holes. With an optical scanner it is possible to measure the dimensions of the holes, and to predict the performance of GEMs. However, the gain prediction of GEMs that are manufactured with a double mask etching technique is not straightforward. With the hole size information alone, it is not possible to make precise prediction of the gain. We show that the alignment of the photo-masks between the two sides of the GEM foils plays a crucial role. A misalignment of a few microns can lower the gain substantially. The study is performed by using the Helsinki high definition optical scanner for quality control of GEM foils, and this will show its true potential.

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Section: Setupmentioning

confidence: 99%

Hole Misalignment and Gain Performance of Gaseous Electron Multipliers

Brücken,

Heino,

Hildén

et al. 2021

Preprint

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“…This opens a panel that is shown on the right. In this example, the user selects a threshold (which is multiplied by a trivial scaling factor for display purposes) in (2), which specifies the maximum counts threshold for the reprocessing. The text (3) displays the channels found above this threshold, with the reprocessed histogram displayed below.…”

Section: Time Slices and Reprocessingmentioning

confidence: 99%

“…Given the high particle-multiplicity physics environment, large data rates, and complexity of the detectors, effective data-quality monitoring is an integral part of recording useful physics data. For the ALICE collaboration, this important task has been successfully addressed during LHC Run 1 and 2 by the data-quality monitoring (DQM) system known as AMORE [2].…”

Section: Introductionmentioning

confidence: 99%

“…First, what novel data-quality monitoring capabilities could be implemented during Run 2 by taking advantage of this additional processing power? Second, looking further forward, ALICE is planning major upgrades for Run 3, which include a substantial software project known as O 2 [5] -since the current HLT operates in a similar manner as O 2 , what experiences related to operating a quality assurance (QA) system in such an environment could be developed while still in Run 2?…”

Section: Introductionmentioning

confidence: 99%

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Alice Overwatch: Online Monitoring and Data Quality Assurance Using Hlt Data

Ehlers

Mulligan

2019

EPJ Web Conf.

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ALICE Overwatch is a project started in late 2015 to provide augmented online monitoring and data quality assurance utilizing time-stamped QA histograms produced by the ALICE High Level Trigger. The system receives the data via ZeroMQ, stores it for later review, enriches it with detector specific functionality, and visualizes it via a web application. These provided capabilities are complementary to the existing Data Quality Monitoring system. In addition to basic visualization, additional processing options are accessible to the user, including requests for data within a particular time range or reprocessing of a particular run with different processing parameters. For example, the first ten minutes of a particular run could be investigated for a transient hot trigger channel. Due to similarities between the Overwatch architecture and that which will be used for Quality Control (QC) in LHC Run 3 and beyond, Overwatch will also be utilized to develop and test various QC components during LHC Run 2. Some of the areas of QC development include a new trending and alarm framework. We report on the project's design, development, and status.

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“…A DQM software system, called AMORE (Automatic MOnitoRing Environment) [2][3], was developed for the ALICE experiment. It is based on the data analysis framework ROOT [4] and uses the DATE monitoring library [5].…”

Section: Amore Design and Architecturementioning

confidence: 99%

The ALICE Data Quality Monitoring: qualitative and quantitative review of three years of operations

Haller

Bellini²,

Telesca

et al. 2014

J. Phys.: Conf. Ser.

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Abstract. ALICE (A Large Ion Collider Experiment) is a detector designed to study the physics of strongly interacting matter produced in heavy-ion collisions at the CERN Large Hadron Collider (LHC). Due to the complexity of ALICE in terms of number of detectors and performance requirements, Data Quality Monitoring (DQM) plays an essential role in providing online feedback on the data being recorded. It intends to provide shifters with precise and complete information to quickly identify problems, and as a consequence to ensure acquisition of high quality data. This paper presents a review of the ALICE DQM system during the first three years of LHC operations from a quantitative and qualitative point of view. We start by presenting the DQM software and tools before moving on to the various analyses carried out. An overview of the produced monitoring quantities is given, presenting the diversity of usage and flexibility of the DQM. Well-prepared shifters and experts, in addition to a precise organisation, were required to ensure smooth and successful operations. The description of the measures taken to ensure both aspects and an account of the DQM shifters' job are followed by a summary of the evolution of the system. We then give a quantitative review of the final setup of the system used during the whole year 2012. We conclude the paper with use cases where the DQM proved to be very valuable, scalable and efficient and with the plans for the coming years.

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The ALICE data quality monitoring system

Cited by 4 publications

References 2 publications

Hole Misalignment and Gain Performance of Gaseous Electron Multipliers

Hole Misalignment and Gain Performance of Gaseous Electron Multipliers

Alice Overwatch: Online Monitoring and Data Quality Assurance Using Hlt Data

The ALICE Data Quality Monitoring: qualitative and quantitative review of three years of operations

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