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

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam

A Short-Baseline Neutrino (SBN) physics program of three LAr-TPC detectors located along the Booster Neutrino Beam (BNB) at Fermilab is presented. This new SBN Program will deliver a rich and compelling physics opportunity, including the ability to resolve a class of experimental anomalies in neutrino physics and to perform the most sensitive search to date for sterile neutrinos at the eV mass-scale through both appearance and disappearance oscillation channels. Using data sets of 6.6e20 protons on target (P.O.T.) in the LAr1-ND and ICARUS T600 detectors plus 13.2e20 P.O.T. in the MicroBooNE detector, we estimate that a search for muon neutrino to electron neutrino appearance can be performed with ~5 sigma sensitivity for the LSND allowed (99% C.L.) parameter region. In this proposal for the SBN Program, we describe the physics analysis, the conceptual design of the LAr1-ND detector, the design and refurbishment of the T600 detector, the necessary infrastructure required to execute the program, and a possible reconfiguration of the BNB target and horn system to improve its performance for oscillation searches.Comment: 209 pages, 129 figure

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

Determination of muon momentum in the MicroBooNE LArTPC using an improved model of multiple Coulomb scattering

We discuss a technique for measuring a charged particle's momentum by means of multiple Coulomb scattering (MCS) in the MicroBooNE liquid argon time projection chamber (LArTPC). This method does not require the full particle ionization track to be contained inside of the detector volume as other track momentum reconstruction methods do (range-based momentum reconstruction and calorimetric momentum reconstruction). We motivate use of this technique, describe a tuning of the underlying phenomenological formula, quantify its performance on fully contained beam-neutrino-induced muon tracks both in simulation and in data, and quantify its performance on exiting muon tracks in simulation. Using simulation, we have shown that the standard Highland formula should be re-tuned specifically for scattering in liquid argon, which significantly improves the bias and resolution of the momentum measurement. With the tuned formula, we find agreement between data and simulation for contained tracks, with a small bias in the momentum reconstruction and with resolutions that vary as a function of track length, improving from about 10% for the shortest (one meter long) tracks to 5% for longer (several meter) tracks. For simulated exiting muons with at least one meter of track contained, we find a similarly small bias, and a resolution which is less than 15% for muons with momentum below 2 GeV/c. Above 2 GeV/c, results are given as a first estimate of the MCS momentum measurement capabilities of MicroBooNE for high momentum exiting tracks

Evidence for t\bar{t}\gamma Production and Measurement of \sigma_t\bar{t}\gamma / \sigma_t\bar{t}

Using data corresponding to 6.0/fb of ppbar collisions at sqrt(s) = 1.96 TeV collected by the CDF II detector, we present a cross section measurement of top-quark pair production with an additional radiated photon. The events are selected by looking for a lepton, a photon, significant transverse momentum imbalance, large total transverse energy, and three or more jets, with at least one identified as containing a b quark. The ttbar+photon sample requires the photon to have 10 GeV or more of transverse energy, and to be in the central region. Using an event selection optimized for the ttbar+photon candidate sample we measure the production cross section of, and the ratio of cross sections of the two samples. Control samples in the dilepton+photon and lepton+photon+\met, channels are constructed to aid in decay product identification and background measurements. We observe 30 ttbar+photon candidate events compared to the standard model expectation of 26.9 +/- 3.4 events. We measure the ttbar+photon cross section to be 0.18+0.08 pb, and the ratio of the cross section of ttbar+photon to ttbar to be 0.024 +/- 0.009. Assuming no ttbar+photon production, we observe a probability of 0.0015 of the background events alone producing 30 events or more, corresponding to 3.0 standard deviations.Comment: 9 pages, 3 figure

A search for resonant production of ttˉt\bar{t} pairs in $4.8\ \rm{fb}^{-1}ofintegratedluminosityof of integrated luminosity of p\bar{p}collisionsat collisions at \sqrt{s}=1.96\ \rm{TeV}$

We search for resonant production of tt pairs in 4.8 fb^{-1} integrated luminosity of ppbar collision data at sqrt{s}=1.96 TeV in the lepton+jets decay channel, where one top quark decays leptonically and the other hadronically. A matrix element reconstruction technique is used; for each event a probability density function (pdf) of the ttbar candidate invariant mass is sampled. These pdfs are used to construct a likelihood function, whereby the cross section for resonant ttbar production is estimated, given a hypothetical resonance mass and width. The data indicate no evidence of resonant production of ttbar pairs. A benchmark model of leptophobic Z \rightarrow ttbar is excluded with m_{Z'} < 900 GeV at 95% confidence level.Comment: accepted for publication in Physical Review D Sep 21, 201