Speaker
Description
Triggerless data acquisition (DAQ) systems are increasingly adopted in dark matter experiments to cope with high detector granularity and continuous data streams while avoiding biases in event selection. In this approach, independent front-end readout electronics operate on each channel in continuous free-running mode, using individual digitization thresholds (self-triggers) rather than global trigger decisions. Event building is implemented in software, where timestamps and temporal coincidences between front-end signals are used to define events. Such architectures require a control and monitoring system capable of tracking, in real time, data transfers, timing signals, status flags, and error conditions without interfering with the normal data flow.
We present a general purpose FPGA-based monitoring system designed for triggerless DAQ architectures and optimized for the specific requirements of dark matter detectors. The system is implemented in hardware using auxiliary boards hosting FPGA devices that interface with front-end digitizers and data concentrators. These boards process monitoring signals such as buffer occupancy, busy and flow-control flags, timing and synchronization signals, and error conditions during both science data taking and calibration runs, enabling continuous checks of DAQ performance and stability.
The proposed solution provides a flexible and scalable design suitable for next-generation dark matter detectors employing triggerless readout. The main features of the implementation are described together with the test bench and the DAQ environment used for system validation. A specialized version of this system has been installed in the Neutron Veto detector of the XENONnT experiment at the Laboratori Nazionali del Gran Sasso (LNGS).
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