185 research outputs found
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The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.
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 pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies
Design and construction of the MicroBooNE Cosmic Ray Tagger system
The MicroBooNE detector utilizes a liquid argon time projection chamber
(LArTPC) with an 85 t active mass to study neutrino interactions along the
Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground
level, the detector records many cosmic muon tracks in each beam-related
detector trigger that can be misidentified as signals of interest. To reduce
these cosmogenic backgrounds, we have designed and constructed a TPC-external
Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for
High Energy Physics (LHEP), Albert Einstein center for fundamental physics,
University of Bern. The system utilizes plastic scintillation modules to
provide precise time and position information for TPC-traversing particles.
Successful matching of TPC tracks and CRT data will allow us to reduce
cosmogenic background and better characterize the light collection system and
LArTPC data using cosmic muons. In this paper we describe the design and
installation of the MicroBooNE CRT system and provide an overview of a series
of tests done to verify the proper operation of the system and its components
during installation, commissioning, and physics data-taking
Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE
The single-phase liquid argon time projection chamber (LArTPC) provides a
large amount of detailed information in the form of fine-grained drifted
ionization charge from particle traces. To fully utilize this information, the
deposited charge must be accurately extracted from the raw digitized waveforms
via a robust signal processing chain. Enabled by the ultra-low noise levels
associated with cryogenic electronics in the MicroBooNE detector, the precise
extraction of ionization charge from the induction wire planes in a
single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event
display images, and quantitatively demonstrated via waveform-level and
track-level metrics. Improved performance of induction plane calorimetry is
demonstrated through the agreement of extracted ionization charge measurements
across different wire planes for various event topologies. In addition to the
comprehensive waveform-level comparison of data and simulation, a calibration
of the cryogenic electronics response is presented and solutions to various
MicroBooNE-specific TPC issues are discussed. This work presents an important
improvement in LArTPC signal processing, the foundation of reconstruction and
therefore physics analyses in MicroBooNE.Comment: 54 pages, 36 figures; the first part of this work can be found at
arXiv:1802.0870
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Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons
We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration
The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
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 pattern-recognition performance are
presented for simulated MicroBooNE events, using a selection of final-state
event topologies.Comment: Preprint to be submitted to The European Physical Journal
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Reconstruction and measurement of (100) MeV energy electromagnetic activity from Ï0 arrow γγ decays in the MicroBooNE LArTPC
We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current ΜΌ interactions with final state Ï0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant Ï0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of ΜΌ + Ar â ÎŒ + Ï0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of Ï0 kinematics
A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
We have developed a convolutional neural network (CNN) that can make a
pixel-level prediction of objects in image data recorded by a liquid argon time
projection chamber (LArTPC) for the first time. We describe the network design,
training techniques, and software tools developed to train this network. The
goal of this work is to develop a complete deep neural network based data
reconstruction chain for the MicroBooNE detector. We show the first
demonstration of a network's validity on real LArTPC data using MicroBooNE
collection plane images. The demonstration is performed for stopping muon and a
charged current neutral pion data samples
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