arXiv subjects
Publications and source records attributed to Byron P. Roe.
The densities passed through for neutrinos going from Fermilab to Sanford lab are obtained using two recent density tables, crustal [1] and Shen-Ritzwoller[2], as well as the values from an older table PEMC[3].
Recent results from MiniBooNE are described. These include neutrino oscillation results, low energy anomaly, and neutrino/antineutrino cross sections.
In this paper, we compare the performance, stability and robustness of Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT) using MiniBooNE Monte Carlo samples. These methods attempt to classify events given a number of identification variables. The BDT algorithm has been discussed by us in previous publications. Testing is done in this paper by smearing and shifting the input variables of testing samples. Based on these studies, BDT has better particle identification performance than ANN. The degradation of the classifications obtained by shifting or smearing variables of testing results is smaller for BDT than for ANN.
Boosted decision trees are applied to particle identification in the MiniBooNE experiment operated at Fermi National Accelerator Laboratory (Fermilab) for neutrino oscillations. Numerous attempts are made to tune the boosted decision trees, to compare performance of various boosting algorithms, and to select input variables for optimal performance.
The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics.
This note discusses the problem of choosing between hypotheses in a situation with many, correlated non-normal variables. A new method is introduced to shrink the many variables into a smaller subset of variables with zero mean, unit variance, and zero correlation coefficient between variables. These new variables are well suited to use in a neural net.
We propose using a Bayes procedure with uniform improper prior to determine credible belts for the mean of a Poisson distribution in the presence of background and for the continuous problem of measuring a non-negative quantity $θ$ with a normally distributed measurement error. Within the Bayesian framework, these belts are optimal. The credible limits are then examined from a frequentist point of view and found to have good frequentist and conditional frequentist properties.
A suggestion is made for improving the Feldman Cousins method of estimating signal counts in the presence of background. The method concentrates on finding essential information about the signal and ignoring extraneous information about background. An appropriate method is found which uses the condition that the number of background events obtained does not exceed the total number of events obtained. Several alternative approaches are explored.