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This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported.

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We use distorted wave electron scattering calculations to extract the weak charge form factor F W (q), the weak charge radius R W , and the point neutron radius R n of 208 Pb from the Lead Radius Experiment (PREX) parity-violating asymmetry measurement. The form factor is the Fourier transform of the weak charge density at the average momentum transferq = 0.475 fm −1 . We find F W (q) = 0.204 ± 0.028 (exp) ± 0.001 (model). We use the Helm model to infer the weak radius from F W (q). We find R W = 5.826 ± 0.181 (exp) ± 0.027 (model) fm. Here the experimental error includes PREX statistical and systematic errors, while the model error describes the uncertainty in R W from uncertainties in the surface thickness σ of the weak charge density. The weak radius is larger than the charge radius, implying a "weak charge skin" where the surface region is relatively enriched in weak charges compared to (electromagnetic) charges. We extract the point neutron radius R n = 5.751 ± 0.175 (exp) ± 0.026 (model) ± 0.005 (strange) fm from R W . Here there is only a very small error (strange) from possible strange quark contributions. We find R n to be slightly smaller than R W because of the nucleon's size. Finally, we find a neutron skin thickness of R n − R p = 0.302 ± 0.175 (exp) ± 0.026 (model) ± 0.005 (strange) fm, where R p is the point proton radius.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current patternrecognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.