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Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments
Journal article   Open access

Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments

Maryon Ahrens, Christian Bohm, Kunal Deoskar, Chad Finley, Klas Hultqvist, Erin O'Sullivan, Christian Walck and Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, v 977, p164332
2020
DOI: https://doi.org/10.1016/j.nima.2020.164332
url
https://doi.org/10.1016/j.nima.2020.164332View
Accepted (AM)Maybe Open Access (Publisher Bronze),  Open

Abstract

Annan teknik Detector Engineering and Technology FVLV nu T Fysik KDE Maskinteknik Monte Carlo Naturvetenskap Neutrino Neutrino mass ordering Other Engineering and Technologies Smoothing Teknik och teknologier Data Analysis Mechanical Engineering Natural Sciences Physical Sciences Statistics
The current and upcoming generation of Very Large Volume Neutrino Telescopes - collecting unprecedented quantities of neutrino events - can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.

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Domestic collaboration
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Instruments & Instrumentation
Nuclear Science & Technology
Physics, Nuclear
Physics, Particles & Fields
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