@@ -21,7 +21,7 @@ During the data acquisition process, the online monitoring software presents rea
As explained in XX.YY, the optical data obtained from the detector operation are stored in ROOT files and moved to a high performance storage environment. The offline data quality control procedures start with a first analysis of these files which is performed daily. It mainly focuses on but is not restricted to the summary data stored in the ROOT files. The summary data contain information related to the performance of the data acquisition procedures for each optical module in the detector. As a result of this first analysis, a set of key-value pairs is produced where each key corresponds to a parameter that represents a given dimension of data quality and the value represents the evaluation of this parameter for the livetime of the analysed data. The results are tagged with a unique identifier corresponding to the analysed data set and uploaded to the database. In the present implementation the analysis is performed for each available file where each file corresponds to a data taking run, although this may change in the future as the data volume generated per run will increase with the detector size.
A further analysis of the results stored in the database includes the comparison of the values of the different parameters to some reference values, allowing for a classification of data periods according to their quality. The reference values are typically set according to the accuracy with which the current detector simulations include the different quality parameters. In addition, the evolution of the different quality parameters can be monitored and made available to the full collaboration as reports. Currently this is done every week by the shifters, and the reports are posted on an electronic log book (ELOG). Figure {FIG}, shows an example of the time evolution for two quality parameters during the period corresponding to the data sample that are provided together with this report.
A further analysis of the results stored in the database includes the comparison of the values of the different parameters to some reference values, allowing for a classification of data periods according to their quality. The reference values are typically set according to the accuracy with which the current detector simulations include the different quality parameters. In addition, the evolution of the different quality parameters can be monitored and made available to the full collaboration as reports. Currently this is done every week by the shifters, and the reports are posted on an electronic log book (ELOG). Figure {FIG}, shows an example of the time evolution for two quality parameters during the period corresponding to the data sample that are provided together with this report. The selected runs correspond to a period of stable rates and during which the different quality parameters were within the allowed tolerance.
##Calibration
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@@ -29,6 +29,8 @@ The first step in the data processing chain is to determine the detector calibra
##Simulations and Event reconstruction
Once the calibration constnats have been determined, the data processing chain continues with the event reconstruction, and with the simulation and reconstruction of an equivalent set of events where the information in the summary data is used to simulate the data taking conditions. The simulation of particle interactions and propagation is done by dedicated software, while the detector simulation and event reconstruction is done by Jpp. As a result of the simulation chain, a ROOT file is obtained which has the same format as the ROOT file produced by the data acquisition system. This contains events obtained after the simulation of the detector trigger. The resulting file and the corresponding data taking file, are identically processed by the reconstruction software, which produces ROOT formatted files with the result of reconstructing the real data events and the simulated events respectively. The comparison between data and simulations is an important parameter to measure the quality of the data and it can be done at trigger level, and at reconstruction level. In both cases, the comparison follows the same strategy: the root files are used to produce histograms of different observables and these histograms are saved into new ROOT files. A set of libraries and applications devoted to histogram comparisons have been developped in Jpp. These implement multiple statistical tests that can be used to determine if two histograms are compatible, as well as the degree of incompatibility between them. Additionally, tools have been developed that allow to summarise the reuslts into a number per file, which represents the average result after comparing all the observables. For the example provided here, the discrepancy between data and montecarlo is measured through the calculation of the reduced chi2 for each observable, and the summary is given as the average reduced chi2 of all the compared observables for each file.
Once the calibration constnats have been determined, the data processing chain continues with the event reconstruction, and with the simulation and reconstruction of an equivalent set of events where the information in the summary data is used to simulate the data taking conditions. The simulation of particle interactions and propagation is done by dedicated software, while the detector simulation and event reconstruction is done by Jpp. As a result of the simulation chain, a ROOT file is obtained which has the same format as the ROOT file produced by the data acquisition system. This contains events obtained after the simulation of the detector trigger. The resulting file and the corresponding data taking file, are identically processed by the reconstruction software, which produces ROOT formatted files with the result of reconstructing the real data events and the simulated events respectively. The comparison between data and simulations is an important parameter to measure the quality of the data and it can be done at trigger level, and at reconstruction level. In both cases, the comparison follows the same strategy: the root files are used to produce histograms of different observables and these histograms are saved into new ROOT files. A set of libraries and applications devoted to histogram comparisons have been developped in Jpp. These implement multiple statistical tests that can be used to determine if two histograms are compatible, as well as the degree of incompatibility between them. Additionally, tools have been developed that allow to summarise the reuslts into a number per file, which represents the average result after comparing all the observables. For the example provided here, the discrepancy between data and montecarlo is measured through the calculation of the reduced chi2 for each observable, and the summary is given as the average reduced chi2 of all the compared observables for each file. Figures {X} and {Y} show the value of this parameter as a function of the run number.
The contents in these files are the main ingredients for the physics analyses based on reconstructed event data, though these analyses are typically done by dedicated software frameworks which require a special formatting of the data. The data format used for physics analyses is called 'aanet' format, which is also based in ROOT. Control mechanisms are thus needed to ensure consistency between the files produced by Jpp, and the aanet formatted files. There exist mechanisms that can be used to verify the consistency between the content in both files. done by verifying that the distributions of multiple parameters related to the events, as obtained from each of these files, are identical. For this, software tools have been developed to generate the necessary distributions from each file.
These tools would also allow for the calculation of other data quality metrics by comparing the data with figures of merit for different observables. For this, the development of plans and strategies mentioned in the introduction of this section is necessary.
The contents in the files produced by the reconstruction routines are the main ingredients for the physics analyses, though these analyses are typically done with dedicated software frameworks which require a special formatting of the data. The data format used for physics analyses is called 'aanet' format, which is also based in ROOT. Control mechanisms are thus needed to ensure consistency between the files produced by Jpp, and the aanet formatted files. Consistency between Jpp and aanet files can be verified up to a certain level by producing distributions of the different parameters and by verifying that these distributions are identical.
