26 research outputs found

    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

    Optical microcavities as platforms for entangled photon spectroscopy

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    Optical microcavities are often proposed as platforms for spectroscopy in the single- and few-photon regime due to strong light-matter coupling. For classical-light spectroscopies, an empty microcavity simply acts as an optical filter. However, we find that in the single- or few-photon regime treating the empty microcavity as an optical filter does not capture the full effect on the quantum state of the transmitted photons. Focusing on the case of entangled photon-pair spectroscopy, we consider how the propagation of one photon through an optical microcavity changes the joint spectrum of a frequency-entangled photon pair. Using the input-output treatment of a Dicke model, we find that propagation through a strongly coupled microcavity above a certain coupling threshold enhances the entanglement entropy between the signal and idler photons. These results show that optical microcavities are not neutral platforms for quantum-light spectroscopies and their effects must be carefully considered when using change in entanglement entropy as an observable

    Assessment of aflatoxin M1 enrichment factor in cheese produced with naturally contaminated milk

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    Aflatoxin M1 (AFM1) is a well-known carcinogenic compound that may contaminate milk and dairy products. Thus, with the regulation 1881/2006, the European Union established a concentration limit for AFM1 in milk and insisted on the importance of defining enrichment factors (EFs) for cheese. In 2019, the Italian Ministry of Health proposed four different EFs based on cheese’s moisture content on a fat-free basis (MMFB) for bovine dairy products. This study aimed to define the EFs of cheese with different MFFB. The milk used for cheesemaking was naturally contaminated with different AFM1 concentrations. Results showed that all the EF average values from this study were lower than those of the Italian Ministry of Health. Hence, the current EFs might need to be reconsidered for a better categorization of AFM1 risk in cheese

    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-

    The risk associated with hyperoncotic colloids in patients with shock

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