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A particle trap learns to pick out the important stuff on the fly.
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The MicroBooNE detector is a large tank of liquid argon that catches particles flying through it. When a particle passes, it leaves a trail of charge and a flash of light. These trails combine into a three-dimensional picture of the event, as if the particle were drawing its path in a transparent cube.
But there are too many such events: hundreds of cosmic muons fly through the detector every second. Recording everything would mean drowning in data. Usually, the selection is done later, on computers, but that is slow and expensive. The new work shows how to teach the detector to select what matters right on the fly, like an experienced chef skimming the foam off a broth without turning off the stove.
The algorithm looks only at the charge left by the particle in the wire planes and decides in fractions of a second: is this an interesting electron from a stopping muon, or just noise?
Such online selection has been tested for the first time on real data from a liquid argon detector. It works fast and accurately, and it can be transferred to future experiments where the data flow will be even more powerful. It's like teaching a mail filter to tell important letters from spam before they hit your inbox.
Now the detector doesn't just see particles: it learns to hear the whisper of rare events amid the roar of cosmic rays.
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.
