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

TL;DR

This paper demonstrates a real-time data selection algorithm for liquid argon TPCs, specifically identifying electrons from cosmic rays in MicroBooNE, crucial for future large-scale neutrino experiments like DUNE.

Contribution

It introduces the first online data selection method using charge information in a LArTPC with real data, enabling efficient data reduction for large-scale neutrino detectors.

Findings

Successfully identified electrons from cosmic rays in real-time

Achieved high accuracy in data selection with low latency

Proven applicability for future large-scale experiments like DUNE

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.