| |
| author |
Mark Vakulenko
| | title |
Online and Offline Data Quality Monitoring for the Mu2e Calorimeter
| | abstract |
This thesis presents the design, implementation, and validation of a calorimeter Data Quality
Monitoring (DQM) toolchain for the Mu2e experiment at Fermilab. Mu2e searches for charged
lepton flavor violation via coherent muon-to-electron conversion in the field of an aluminum
nucleus, µ−+ Al → e−+ Al, a process whose observation would constitute clear evidence of
physics beyond the Standard Model. Achieving target sensitivity requires stringent control
of detector performance and data integrity during acquisition, as subtle issues in readout
configuration, data formatting, or electronics behavior can compromise reconstruction and
bias downstream analyzes. To address these challenges, this work develops a multi-layer
DQM approach spanning both raw data validation and reconstructed digi-level diagnostics.
At the low level, a fragment analysis component performs word- and bit-field decoding of
calorimeter readout blocks, enabling sanity checks of the expected structure and producing
detailed error and integrity statistics useful for commissioning and troubleshooting. At
the digi level, the CaloDigiDQM analyzer is implemented within the art framework and
transforms each CaloDigiCollection into a structured hierarchy of ROOT histograms
designed for fast drill-down diagnostics. The module generates coherent monitoring views
at global, disk, board, and channel granularity, including occupancy, waveform-derived
features (baseline, RMS, peak amplitude and position), and left-right sensor consistency
metrics. Detector-aware channel-to-electronics mapping is performed through the conditions
system (CaloDAQMap), ensuring that diagnostics remain aligned with hardware identifiers
used in operations. For end-to-end testing without reliance on live DAQ data, a synthetic
CaloDigi producer is developed to generate realistic waveforms with controlled noise and
pulse shapes. The resulting system supports both offline ROOT-file production and online
operation, including optional histogram streaming through otsdaq via ots::HistoSender.
This toolchain provides a practical and scalable foundation for calorimeter commissioning
and stable data collection, enabling early detection of anomalies and reducing operational
risk for Mu2e.
| | school |
The Caspersen School of Graduate Studies, Drew University
| | degree |
M.S. (2025)
|
| advisor |
Kamal Benslama
|
| committee |
Alexander Rudniy
Pavel Murat
|
| full text | MVakulenko.pdf - requires Drew uLogin |
| |
