Cosmic Ray Background Rejection with Wire-Cell LArTPC Event Reconstruction in the MicroBooNE Detector

Abstract

For a large liquid argon time projection chamber (LArTPC) operating on or near the Earth's surface to detect neutrino interactions, the rejection of cosmogenic background is a critical and challenging task because of the large cosmic ray flux and the long drift time of the TPC. We introduce a superior cosmic background rejection procedure based on the Wire-Cell three-dimensional (3D) event reconstruction for LArTPCs. From an initial 1:20,000 neutrino to cosmic-ray background ratio, we demonstrate these tools on data from the MicroBooNE experiment and create a high performance generic neutrino event selection with a cosmic contamination of 14.9% (9.7%) for a visible energy region greater than O(200) MeV. The neutrino interaction selection efficiency is 80.4% and 87.6% for inclusive νµ charged-current and νe charged-current interactions, respectively. This significantly improved performance compared to existing reconstruction algorithms, marks a major milestone toward reaching the scientific goals of LArTPC neutrino oscillation experiments operating near the Earth's surface.

Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

“…In Wire-Cell, a track trajectory is a list of trajectory points (3D points with charge information, about 6 mm away from each other) obtained from fitting; a track segment is a subset of a TPC cluster including a set of 3D space points and associated 2D (projection) pixels with deposited charge information. A key technique in determining vertices and track segments is the multi-track trajectory and 𝑑𝑄/𝑑𝑥 fitting, which is an expansion of the single-track trajectory and 𝑑𝑄/𝑑𝑥 fitting technique used in the generic neutrino selection [22]. Figure 5 illustrates the relationships between key concepts used in the multi-track trajectory and 𝑑𝑄/𝑑𝑥 fitting algorithm.…”

“…The cosmic-ray muons that stop inside the TPC active volume can be determined by identifying the direction of the muon track. A 3D trajectory and 𝑑𝑄/𝑑𝑥 (ionization charge loss per unit length) fitting procedure [22] was developed to identify Bragg peaks and reject those stopped muons. Note that the measurable 𝑑𝑄/𝑑𝑥 is directly related to the previously mentioned 𝑑𝐸/𝑑𝑥 (energy loss per unit length).…”

“…Both traditional algorithms (non-machine-learning algorithms) and machine-learning algorithms are used in this pattern recognition procedure. Some of the basic tools-the track trajectory and 𝑑𝑄/𝑑𝑥 fitting, for example-are improved versions of the techniques developed for generic neutrino detection [21,22]. This fitting algorithm is expanded to fit multiple tracks and multiple vertices rather than fitting a single track.…”

LEvEL: Low-Energy Neutrino Experiment at the LHC

“…Even if we assume a LArTPC detector like MicroBooNE (89 ton fiducial mass), whose readout is of order 2 ms due to the drift time of electrons, this would still translate into 0.5 CR muons per readout. MicroBooNE has shown that by considering the topological information on CR muon events and light collection, an overall CR muon rejection power of 7 × 10 −6 can be achieved [111]. As we will see later, the rate of events for the processes of interest in a hypothetical LArTPC detector are much higher than 10 −6 per dump.…”

Search for an anomalous excess of charged-current quasi-elastic $ν_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

“…Events are then run through the Wire-Cell charge-light matching algorithm [50,51], used jointly in this analysis and the Wire-Cell inclusive LEE search [16], to map charge clusters to flashes of scintillation light observed by the PMTs. Charge clusters mapped to flashes outside the beam spill are tagged as cosmic in origin.…”