scholarly journals Accelerating Calculation of Confidence Intervals for NOvA's Neutrino Oscillation Parameter Estimation with Supercomputers

2020 ◽  
Author(s):  
Steven Calvez ◽  
◽  
Tarak Thakore ◽  
Keyword(s):  
Parameter Estimation ◽  
Confidence Intervals ◽  
Neutrino Oscillation ◽  
Oscillation Parameter ◽  
Neutrino Oscillation Parameter

scholarly journals Neutrino oscillation parameter sampling with MonteCUBES

2010 ◽  
Vol 181 (1) ◽  
pp. 227-231 ◽  
Author(s):  
Mattias Blennow ◽  
Enrique Fernandez-Martinez
Keyword(s):  
Neutrino Oscillation ◽  
Oscillation Parameter ◽  
Neutrino Oscillation Parameter

scholarly journals Efficient Neutrino Oscillation Parameter Inference with Gaussian Process

2019 ◽  
Vol 33 ◽  
pp. 9967-9968
Author(s):  
Lingge Li ◽  
Nitish Nayak ◽  
Jianming Bian ◽  
Pierre Baldi
Keyword(s):  
Gaussian Process ◽  
Neutrino Oscillation ◽  
Oscillation Parameter ◽  
Unified Approach ◽  
Parameter Inference ◽  
Computation Cost ◽  
Computationally Expensive ◽  
Set Up ◽  
The Universe ◽  
Neutrino Oscillation Parameter

Many experiments have been set-up to measure the parameters governing the neutrino oscillation probabilities accurately, with implications for the fundamental structure of the universe. Very often, this involves inferences from tiny samples of data which have complicated dependencies on multiple oscillation parameters simultaneously. This is typically carried out using the unified approach of Feldman and Cousins which is very computationally expensive, on the order of tens of millions of CPU hours. In this work, we propose an iterative method using Gaussian Process to efficiently find a confidence contour for the oscillation parameters and show that it produces the same results at a fraction of the computation cost.

CONSTRAINTS ON NEUTRINO OSCILLATION PARAMETERS FROM TIME/ENERGY DEPENDENCE OF SOLAR 8B NEUTRINO FLUX

2001 ◽  
Vol 16 (supp01b) ◽  
pp. 718-720
Author(s):  
◽  
MICHAEL B SMY
Keyword(s):  
Energy Dependence ◽  
Neutrino Oscillation ◽  
Parameter Space ◽  
Time Variation ◽  
Solar Neutrino ◽  
Neutrino Flux ◽  
Oscillation Parameter ◽  
Solar Neutrino Flux ◽  
Time Variations ◽  
Neutrino Oscillation Parameter

A search for neutrino oscillation is presented using time variations and energy dependence of the observed reduction of the solar neutrino flux. No significant time variation or energy dependence has been found in 1117 days of solar neutrino data taken with the Super-Kamiokande experiment. This constrains the two-neutrino oscillation parameter space independently of the model dependence of the solar neutrino flux. The combination of Super-Kamiokande's data of the day-night variation, energy dependence and flux results in two allowed regions at 95% C.L.

scholarly journals MLE-Based Parameter Estimation for Four-Parameter Exponential Gamma Distribution and Asymptotic Variance of Its Quantiles

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2092
Author(s):  
Songbai Song ◽  
Yan Kang ◽  
Xiaoyan Song ◽  
Vijay P. Singh
Keyword(s):  
Parameter Estimation ◽  
Confidence Intervals ◽  
Asymptotic Variance ◽  
Information Matrix ◽  
Estimation Method ◽  
Flood Frequency Analysis ◽  
Annual Precipitation ◽  
Precipitation Data ◽  
Marquardt Algorithm ◽  
Design Values

The choice of a probability distribution function and confidence interval of estimated design values have long been of interest in flood frequency analysis. Although the four-parameter exponential gamma (FPEG) distribution has been developed for application in hydrology, its maximum likelihood estimation (MLE)-based parameter estimation method and asymptotic variance of its quantiles have not been well documented. In this study, the MLE method was used to estimate the parameters and confidence intervals of quantiles of the FPEG distribution. This method entails parameter estimation and asymptotic variances of quantile estimators. The parameter estimation consisted of a set of four equations which, after algebraic simplification, were solved using a three dimensional Levenberg-Marquardt algorithm. Based on sample information matrix and Fisher’s expected information matrix, derivatives of the design quantile with respect to the parameters were derived. The method of estimation was applied to annual precipitation data from the Weihe watershed, China and confidence intervals for quantiles were determined. Results showed that the FPEG was a good candidate to model annual precipitation data and can provide guidance for estimating design values

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