Demonstration and performance of an online data selection algorithm for liquid argon time projection chambers using MicroBooNE

TL;DR

The paper demonstrates, for the first time in a LArTPC, an online data-selection framework that uses collection-plane ionization information to identify Michel electrons from stopping cosmic-ray muons. Built on an emulated online SN data stream, the workflow compresses and analyzes TPC signals through three stages—Trigger Primitive generation, Trigger Candidate generation, and a High-Level Trigger—to selectively store ROI data while meeting strict latency constraints. Validation with MC ensembles (stopping, crossing, and CORSIKA muons) and real SN data shows the topology-based approach can achieve meaningful Michel-electron selection with tolerable efficiency loss and robust performance within the detector’s data-rate demands. This work provides a concrete proof-of-principle for real-time TPC-based data selection in MicroBooNE and establishes a scalable path for applying similar online triggers to SBND and DUNE, including integration with AI/ML techniques for even more adaptive, low-latency operation.

Abstract

The MicroBooNE detector is a liquid argon time projection chamber (LArTPC) that produces three-dimensional images of particle interactions using ionization charge collected by anode wire plane arrays and scintillation light collected by a light detection system. In addition to testing long-standing experimental neutrino anomalies and performing measurements of neutrino interactions with argon nuclei using the Fermilab Booster Neutrino Beam, MicroBooNE aims to develop methodologies for rare beyond the Standard Model and off-beam physics searches. Looking ahead to the upcoming Deep Underground Neutrino Experiment (DUNE), with MicroBooNE serving as a valuable testbed, achieving high sensitivity and livetime for off-beam physics while satisfying data processing and storage constraints will require data-driven, intelligent, and online or real-time data selection techniques. These techniques are essential for reducing data rates and preserving rare signals with high accuracy. In this paper, we describe a fast data selection algorithm suitable for online execution to identify electrons from stopping cosmic ray muons in the MicroBooNE detector utilizing ionization charge information, and present its performance. This represents the first demonstration of online data selection in a LArTPC using real data and charge information exclusively and provides an important proof-of-principle for applying such techniques to other LArTPC experiments such as the Short-Baseline Near Detector and DUNE.

Figures (14)

• Figure 1: A schematic illustrating the MicroBooNE liquid argon time projection chamber. A neutrino interaction in the detector medium produces ionization electrons that drift toward the anode wire planes under a uniform electric field.
• Figure 2: A schematic illustrating MicroBooNE readout data flow. The blue-labeled path is part of the NU stream. The green-labeled path is part of the SN stream. See text for more details.
• Figure 3: Example of a MicroBooNE ROI in the continuous SN data stream from one of the collection plane channels, showing samples saved as part of the ROI waveform (blue circles), once the threshold criterion is met. Baseline and threshold ADC values for this channel are shown in red and dashed green lines, respectively.
• Figure 4: A schematic showing the online TPC-based data selection framework developed and exercised on the MicroBooNE "emulated online" SN data stream. The online emulation refers to the ROI data input to Process 1 being read directly from memory, once already transferred over the network.
• Figure 5: Baseline and nominal threshold values for collection plane channels. Some of the noisier channels have higher baseline settings.