Speaker
Jonathan Asaadi
(University of Texas Arlington)
Description
Future long baseline neutrino experiments such as the Deep Underground Neutrino Experiment (DUNE) call for the deployment of multiple multi-kiloton scale liquid argon time projection chambers (LArTPCs). To date, two detector readout technologies are being studied in large-scale prototype detectors: the single phase (SP) and dual phase (DP) detectors using projective charge readout wire based anode planes. These projective readout technologies come with a set of challenges in the construction of the anode planes, the continuous readout of the system required to accomplish the physics goals of proton decay searches and supernova neutrino sensitivity, and the 2D projective reconstruction of complex neutrino topologies.
The Q-Pix concept (arXiv: 1809.10213) is a continuously integrating low-power charge-sensitive amplifier (CSA) viewed by a Schmitt trigger. When the trigger threshold is met, the comparator initiates a ‘reset’ transition and returns the CSA circuitry to a stable baseline. This is the elementary Charge-Integrate / Reset (CIR) circuit. The instance of reset time is captured in a 32-bit clock value register, buffers the cycle and then begins again. What is exploited in this new architecture is the time difference between one clock capture and the next sequential capture, called the Reset Time Difference (RTD). The RTD measures the time to integrate a predefined integrated quantum of charge (Q). Waveforms are reconstructed without differentiation and an event is characterized by the sequence of RTDs. In quiescent mode the RTDs will be evenly spaced with time intervals of seconds between RTDs with an event signaled by the appearance of a sequence of varying $\mu$s RTDs. This technique easily distinguishes the background RTDs due to 39Ar decays (which also provide an automatic absolute charge calibration) and signal RTD sequences due to ionizing tracks. Q-Pix offers the ability to extract all track information providing very detailed track profiles and also utilizes a dynamically established network for DAQ for exceptional resilience against single point failures.
Primary author
Jonathan Asaadi
(University of Texas Arlington)
