Video performance of “Parade”

Video performance of “Parade”by Alysia Harri

The Non-Aspectual Meaning of African American English 'Aspect' Markers

This dissertation contributes to the existing treatments of African-American English (AAE) by providing a new formal semantic account of the meanings of the AAE particles 'preverbal' done and 'invariant' be. The work presented here sheds much-needed light on both the semantic and distributional patterns of AAE sentences containing preverbal done and invariant be with respect to their unmarked counterparts. Preverbal done and invariant be are often understood as elements within a class of uninflected preverbal material unique to AAE. This class also includes perfect been, focused remote perfect BIN, and irrealis be done. Though they differ in the meanings assigned to each member, previous studies—from the early observations of Labov (1972) and Rickford (1975) to more recent investigations by Green (1993, 1998, 2000), Edwards (1991, 2001) Dayton (1996) and Terry (2004, 2006, 2010)—treat all members of this class as contributing the highly-specified aspectual information that form the complex set of overt aspectual distinctions that are seen as the dialect's distinguishing feature par excellence. This dissertation pivots from traditional views to cast the behaviors and contributions of two of these elements— done and be— in a new light. Contra existing analyses, I present evidence that preverbal done and invariant be are not primarily aspectual markers at all. I argue that these two elements are discourse-oriented particles whose semantic contributions index speaker's evidence, expectations, and evaluations concerning the propositions in which they appear. While perfective –ed and progressive –ing carry the majority of the aspectual load, done's and be's non-aspectual meanings provide evidence in favor of the broader cross-linguistic hypothesis that discourse properties— like relevancy, speaker-expectation and evidential strength— can affect the temporal and aspectual interpretations of sentences. Because I make a departure from purely-aspectual analyses, Chapter 2 is designed to present those familiar with the behavior of AAE done and be with the theoretical concepts of evidentiality (Section 2.2), mirativity and noncongruence (Section 2.3), and Kratzerian modality (Section 2.4) that I rely on to specific done and be's specific discourse functions. Chapter 3 puts forth a non-aspectual treatment of done, which carries a specialized version of the perfect's relevance presupposition— current diametric relevance— but none of the perfect's aspectual features. Done is shown to be a particle used to express the speaker's sentiment that a proposition does not align with the speaker's expectations, desires, or plans— a property I am calling ‘noncongruence'. Chapter 4 presents evidence for the status of be as an epistemic-evidential marker against the traditional understandings of be as directly indexing habitual aspect. Chapter 4 shows that be carries an evidential presupposition which requires the speaker to have some partial perceptual evidence on which to base their generalizations about normal states of affairs. This chapter provides semantic analyses of be, be +V–ing, and the constructions with which they alternate: predicate instantiation with the null copula, Present Progressive and the Simple Present. With the minimally distinct semantic entries for each formal alternative from Chapter 4, we can easily capture the significant overlap in the distributions of be +V–ing, the Present Progressive, and the Simple Present. Chapter 5 explains how these formal variants divide up the pragmatic labor of expressing propositions that are true of the present interval. Within a Neo-Gricean framework and building off Deo's work on the progressive-imperfective contrast (2015), I sketch the formal competition between be and non-be forms in both the verbal and nonverbal domains, accounting for be's habitual (Green 2000) and emphatic readings (Labov 1998, Alim 2004) as conversational implicatures. In Chapter 6, I refine the observations of Dayton (1996) and connect the meanings of done and be through their indexation of inference, and explore the broader implications for how evidential and epistemic notions can affect the interpretation of aspectual temporal reference. Lastly, I put forth a proposal that conceptualizes AAE's class of preverbal particles as advanced grammaticizations of highly-specified pragmatically-conditioned uses of the progressive and the perfect found in both Mainstream and nonstandard varieties of American English

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6 $\times$ 6 $\times$ 6 m$^3$ liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6x6x6m3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties