Overall quality optimization for DQM stage in High Energy Physics experiments
Metadatos
Mostrar el registro completo del ítemEditorial
IOP PUBLISHING LTD
Fecha
2020Referencia bibliográfica
N Benekos et al 2020 J. Phys.: Conf. Ser. 1525 012063 [doi: 10.1088/1742-6596/1525/1/012063]
Patrocinador
(MINECO - Gov. of Spain) P12-TIC-2958 TIN2016-81113-RResumen
Data Acquisition (DAQ) and Data Quality Monitoring (DQM) are key parts in
the HEP data chain, where the data are processed and analyzed to obtain accurate monitoring
quality indicators. Such stages are complex, including an intense processing work-flow and
requiring a high degree of interoperability between software and hardware facilities. Data
recorded by DAQ sensors and devices are sampled to perform live (and offline) DQM of the
status of the detector during data collection providing to the system and scientists the ability
to identify problems with extremely low latency, minimizing the amount of data that would
otherwise be unsuitable for physical analysis. DQM stage performs a large set of operations
(Fast Fourier Transform (FFT), clustering, classification algorithms, Region of Interest, particles
tracking, etc.) involving the use of computing resources and time, depending on the number of
events of the experiment, sampling data, complexity of the tasks or the quality performance. The
objective of our work is to show a proposal with aim of developing a general optimization of the
DQM stage considering all these elements. Techniques based on computational intelligence like
EA can help improve the performance and therefore achieve an optimization of task scheduling
in DQM.