33 research outputs found

    25th International Conference on Computing in High Energy & Nuclear Physics

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    Modern experiments in high energy physics analyze millions of events recorded in particle detectors to select the events of interest and make measurements of physics parameters. These data can often be stored as tabular data in files with detector information and reconstructed quantities. Current techniques for event selection in these files lack the scalability needed for high performance computing environments. We describe our work to develop a high energy physics analysis framework suitable for high performance computing. This new framework utilizes modern tools for reading files and implicit data parallelism. Framework users analyze tabular data using standard, easy-to-use data analysis techniques in Python while the framework handles the file manipulations and parallelism without the user needing advanced experience in parallel programming. In future versions, we hope to provide a framework that can be utilized on a personal computer or a high performance computing cluster with little change to the user code

    Treatment of Systematic Uncertainties on the NOvA Experiment

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    Constraints on Neutrino Oscillation Parameters from Neutrinos and Antineutrinos with Machine Learning

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    Neutrino physics with deep learning. Techniques and applications on NOvA.

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    The NOvA experiment has made both νμ \nu_\mu disappearance and νe \nu_e appearance measurements in Fermilab's NuMI beam, and is working on cross section measurements using near detector data. At the core of NOvA's measurements is the use of deep learning algorithms for identification and reconstruction of the neutrino flavor and energy. These algorithms, used for the first time on NOvA in 2016, yielded large improvements in selection efficiency, and will be applied to our first anti-neutrino results to be released this year. Presented here is the extension of our deep learning efforts for identification of neutrino signal events, final state identification, single particle tagging, and reconstruction using instance segmentation techniques. We will describe the new implementations of modified Convolutional Neural Networks for anti-neutrino events, single particles and their performance for analysis final states selection, standard candle measurements, and reconstruction.</p

    NOvA Reconstruction using Deep Learning

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    Systematic Uncertainties and Cross-Checks for the NOvA Joint νμ+νe Analysis

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    <p>One of the key physics goals of NOvA is to constrain oscillation parameters such as the octant of θ<sub>23</sub>, δ<sub>cp</sub>, and the neutrino mass hierarchy via a joint ν<sub>μ</sub>+ν<sub>e</sub> oscillation analysis. We do this by propagating νμs from the world's most powerful neutrino beam at Fermilab, over a baseline of 810 km to northern Minnesota, USA, and measure the ν<sub>μ</sub> to νeoscillation probability. NOvA will be presenting its latest oscillation results, based on 9×10<sup>20</sup> (7×10<sup>20</sup>) protons on target neutrino (antineutrino) data. Strong constraints on these oscillation parameters require a rigorous treatment of systematic uncertainties and performing thorough cross-checks. In this poster, we present an overview of the treatment of systematic uncertainties, considering contributions from the beam flux, calibration, and uncertainties in our modeling of interactions, as well as cross checks using muon removed simulations and cosmic muon brem showers.</p

    A review on machine learning for neutrino experiments

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    Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds, elusive signals, and small statistics. The introduction of state-of-the-art machine learning tools to solve analysis tasks has made major impacts to these challenges in neutrino experiments across the board. Machine learning algorithms have become an integral tool of neutrino physics, and their development is of great importance to the capabilities of next generation experiments. An understanding of the roadblocks, both human and computational, and the challenges that still exist in the application of these techniques is critical to their proper and beneficial utilization for physics applications. This review presents the current status of machine learning applications for neutrino physics in terms of the challenges and opportunities that are at the intersection between these two fields. </jats:p

    PandAna: A Python Analysis Framework for Scalable High Performance Computing in High Energy Physics

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    Modern experiments in high energy physics analyze millions of events recorded in particle detectors to select the events of interest and make measurements of physics parameters. These data can often be stored as tabular data in files with detector information and reconstructed quantities. Most current techniques for event selection in these files lack the scalability needed for high performance computing environments. We describe our work to develop a high energy physics analysis framework suitable for high performance computing. This new framework utilizes modern tools for reading files and implicit data parallelism. Framework users analyze tabular data using standard, easy-to-use data analysis techniques in Python while the framework handles the file manipulations and parallelism without the user needing advanced experience in parallel programming. In future versions, we hope to provide a framework that can be utilized on a personal computer or a high performance computing cluster with little change to the user code
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