46 research outputs found

    The Beam Dynamics And Beam Related Uncertainties In Fermilab Muon G-2 Experiment

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    The anomaly of the muon magnetic moment, aμ ≡ (g-2)/2, has played an important role in constraining physics beyond the Standard Model for many years. Currently, the Standard Model prediction for aμ is accurate to 0.42 parts per million (ppm). The most recent muon g-2 experiment was done at Brookhaven National Laboratory (BNL) and determined aμ to 0.54 ppm, with a central value that differs from the Standard Model prediction by 3.3-3.6 standard deviations and provides a strong hint of new physics. The Fermilab Muon g-2 Experiment has a goal to measure aμ to unprecedented precision: 0.14 ppm, which could provide an unambiguous answer to the question whether there are new particles and forces that exist in nature. To achieve this goal, several items have been identified to lower the systematic uncertainties. In this work, we focus on the beam dynamics and beam associated uncertainties, which are important and must be better understood. We will discuss the electrostatic quadrupole system, particularly the hardware-related quad plate alignment and the quad extension and readout system. We will review the beam dynamics in the muon storage ring, present discussions on the beam related systematic errors, simulate the 3D electric fields of the electrostatic quadrupoles and examine the beam resonances. We will use a fast rotation analysis to study the muon radial momentum distribution, which provides the key input for evaluating the electric field correction to the measured aμ

    Semi-leptonic Decay of Lambda-b in the Standard Model and with New Physics

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    Heavy quark decays provide a very advantageous investigation to test the Standard Model (SM). Recently, promising experiments with \textit{b} quark, as well as the analysis of the huge data sets produced at the B factories, have led to an increasing study and sensitive measurements of relative \textit{b} quark decays. In this thesis, I calculate various observables in the semi-leptonic decay process ΛbΛcτνˉτ\Lambda_{b}\to \Lambda_{c}\tau\bar{\nu}_{\tau} both in the SM and in the presence of New Physics (NP) operators with different Lorentz structures. The results are relevant for the coming measurement of this semi-leptonic decay at LHC \textit{b} experiment in CERN, and also provide theoretical predictions to refine the physics beyond the SM.Comment: 38 page, 7 figures. arXiv admin note: text overlap with arXiv:1502.0723

    CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation

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    Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data. To better solve the above problems, we propose CGoDial, new challenging and comprehensive Chinese benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions and 574,949 dialog turns totally, covering three datasets with different knowledge sources: 1) a slot-based dialog (SBD) dataset with table-formed knowledge, 2) a flow-based dialog (FBD) dataset with tree-formed knowledge, and a retrieval-based dialog (RBD) dataset with candidate-formed knowledge. To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing. The proposed experimental settings include the combinations of training with either the entire training set or a few-shot training set, and testing with either the standard test set or a hard test subset, which can assess model capabilities in terms of general prediction, fast adaptability and reliable robustness.Comment: EMNLP 202

    Extracting low energy signals from raw LArTPC waveforms using deep learning techniques -- A proof of concept

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    We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs

    A deep-learning based waveform region-of-interest finder for the liquid argon time projection chamber

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    Parallel Flash Talk at the "XIX International Workshop on Neutrino Telescopes" on line - 18-26 February, 2021On behalf of the ArgoNeuT Collaboration Fermilab-Slides-21-007-ND-SCD-