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Diagnostic Analytics for Pixelated Particle Detectors: A Case Study

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Werner Florian Samayoa
Bruno Valinoti
Romina Molina
Luis G. García
Maria Liz Crespo
Sergio Carrato
Andres Cicuttin
Stefano Levorato
Published in
Applications in Electronics Pervading Industry, Environment and Society
Publisher
Springer Nature Switzerland

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Abstract

The chapter delves into the crucial role of diagnostic analytics in enhancing the performance of pixelated particle detectors. It uses the ECAL-2 calorimeter from the COMPASS experiment as a case study to illustrate how mathematical modeling and data analysis can be employed to monitor and diagnose detector performance. By fitting pulses with a second-order semi-Gaussian analog filter model, the authors extract parameters that provide insights into the physical aspects of the detector. This method reveals inhomogeneities and anomalies that are not detectable through traditional energy calibration methods, offering a more comprehensive diagnostic tool. The chapter highlights the importance of these advanced analytics in maintaining and optimizing the health and calibration of complex particle detectors, making it a valuable resource for professionals in the field.
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previous chapter A Reconfigurable 2D-Convolution Accelerator for DNNs Quantized with Mixed-Precision
next chapter Developing a Toolchain for Synthetic Driving Scenario Datasets
Title
Diagnostic Analytics for Pixelated Particle Detectors: A Case Study
Authors
Werner Florian Samayoa
Bruno Valinoti
Romina Molina
Luis G. García
Maria Liz Crespo
Sergio Carrato
Andres Cicuttin
Stefano Levorato
Copyright Year
2023
Book
Applications in Electronics Pervading Industry, Environment and Society

Print ISBN: 978-3-031-30332-6

Electronic ISBN: 978-3-031-30333-3

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-30333-3_28
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