Introduction to MiniBooNE and Vu charged-current quasi-elastic results

"The MiniBooNE experiment is described together with the procedures used to obtain a result for Vm - Ve oscillations. (The oscillation results are described in the companion talk of M. Sorel.) Results are given here for the charged-current quasi-elastic (CCQE) cross section, Vmn - m[?]p. It is found that the simple relativistic Fermi gas nuclear model with Fermi momentum, PF = 220 MeV/c and binding energy EB = 34 MeV is insufficient to describe the reaction for any values of the axial vector mass MA. It was found necessary to add a new empirical Pauli blocking parameter, k. With this new term, the best values found were MA = 1.23 +- 0.20 GeV and k = 1.019 +- 0.011."http://deepblue.lib.umich.edu/bitstream/2027.42/64213/1/jpconf8_110_082018.pd

Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees

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.Comment: 23 pages, 13 figure

Studies of Boosted Decision Trees for MiniBooNE Particle Identification

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.Comment: 28 pages, 22 figures, submitted to Nucl. Inst & Meth.

Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification

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.Comment: 6 pages, 5 figures; Accepted for publication in Nucl. Inst. & Meth.

Improved Probability Method for Estimating Signal in the Presence of Background

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.Comment: Modified 12/21 for singlespace to save trees, 9 pages, 1 figure. Modified 8/11/99 to add small modifications made for the Phys. Rev. articl

A prompt neutrino measurement

A test has been made to explore the possibility of beam dump neutrino experiments with short target‐detector separations and modest detectors. Results have given a positive neutrino signal which is interpreted in the context of various charmed‐meson production models. A limit to the lifetime and mass of the axion is also a byproduct of this test.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87326/2/246_1.pd

Prompt Neutrino Results from Fermi Lab

Results from a Fermi lab experiment to study prompt neutrino production are presented. Assuming the prompt neutrinos come from the decay of charmed mesons we find a total DD production cross section of approx. 20 μb/nucleon, in good agreement with previous CERN results. We find a ν/ν ratio and a νe/νμ of approx. 1.0. The energy and pT spectra of the prompt neutrinos are consistent with those expected from DD production. Limits on the production of supersymmetric particles have also been obtained.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87356/2/262_1.pd

Results from a Fermilab neutrino beam dump experiment

The flux of prompt neutrinos from a beam dump has been measured in an experiment at the Fermi National Accelerator Laboratory (E613). Assuming that the charm production has a linear dependence on atomic number and varies as (1−‖×‖)5 e−2mT, a model dependent cross section of 27±5μb/nucleon can be derived. For neutrino energies greater than 20 GeV, the flux of electron neutrinos with respect to muon neutrinos is 0.78±0.19. For neutrinos with energy greater than 30 GeV and p⟂ greater than 0.2, the flux of ν̄u compared to νμ is 0.96±0.22.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87363/2/100_1.pd