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Register a new userStatistical Issues in Neutrino Physics Analyses
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Authors
Louis Lyons
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Various statistical issues relevant to searches for new physics or to parameter determination in analyses of data in neutrino experiments are briefly discussed.
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Given the cost, both financial and even more importantly in terms of human effort, in building High Energy Physics accelerators and detectors and running them, it is important to use good statistical techniques in analysing data. Some of the statistical issues that arise in searches for New Physics are discussed briefly. They include topics such as: Should we insist on the 5 sigma criterion for discovery claims? The probability of A, given B, is not the same as the probability of B, given A. The meaning of p-values. What is Wilks Theorem and when does it not apply? How should we deal with the `Look Elsewhere Effect? Dealing with systematics such as background parametrisation. Coverage: What is it and does my method have the correct coverage? The use of p0 versus p1 plots.
A fast physics analysis framework has been developed based on SNiPER to process the increasingly large data sample collected by BESIII. In this framework, a reconstructed event data model with SmartRef is designed to improve the speed of Input/Output operations, and necessary physics analysis tools are migrated from BOSS to SNiPER. A real physics analysis $e^{+}e^{-} rightarrow pi^{+}pi^{-}J/psi$ is used to test the new framework, and achieves a factor of 10.3 improvement in Input/Output speed compared to BOSS. Further tests show that the improvement is mainly attributed to the new reconstructed event data model and the lazy-loading functionality provided by SmartRef.
Asymptotic formulae for likelihood-based tests of new physics presents a mathematical formalism for a new approximation for hypothesis testing in high energy physics. The approximations are designed to greatly reduce the computational burden for such problems. We seek to test the conditions under which the approximations described remain valid. To do so, we perform parallel calculations for a range of scenarios and compare the full calculation to the approximations to determine the limits and robustness of the approximation. We compare this approximation against values calculated with the Collie framework, which for our analysis we assume produces true values.
Machine learning has proven to be an indispensable tool in the selection of interesting events in high energy physics. Such technologies will become increasingly important as detector upgrades are introduced and data rates increase by orders of magnitude. We propose a toolkit to enable the creation of a drone classifier from any machine learning classifier, such that different classifiers may be standardised into a single form and executed in parallel. We demonstrate the capability of the drone neural network to learn the required properties of the input neural network without the use of any labels from the training data, only using appropriate questioning of the input neural network.
This paper presents a novel approach to estimate the Standard Model backgrounds based on modifying Monte Carlo predictions within their systematic uncertainties. The improved background model is obtained by altering the original predictions with successively more complex correction functions in signal-free control selections. Statistical tests indicate when sufficient compatibility with data is reached. In this way, systematic effects are absorbed into the new background model. The same correction is then applied on the Monte Carlo prediction in the signal region. Comparing this method to other background estimation techniques shows improvements with respect to statistical and systematical uncertainties. The proposed method can also be applied in other fields beyond high energy physics.
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