Fitting theory to data in the presence of background uncertainties

When fitting theory to data in the presence of background uncertainties, the question of whether the spectral shape of the background happens to be similar to that of the theoretical model of physical interest has not generally been considered previously. These correlations in shape are considered in the present note and found to make important corrections to the calculations. The discussion is phrased in terms of $\chi^2$ fits, but the general considerations apply to any fits. Including these new correlations provides a more powerful test for confidence regions. Fake data studies, as used at present, may not be optimum.Comment: Example added; some conclusions change

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

The structure of an orthorhombic crystal form of a 'forced reduced' thiol peroxidase reveals lattice formation aided by the presence of the affinity tag

Thiol peroxidase (Tpx) is an atypical 2-Cys peroxiredoxin, which has been suggested to be important for cell survival and virulence in Gram-negative pathogens. The structure of a catalytically inactive version of this protein in an orthorhombic crystal form has been determined by molecular replacement. Structural alignments revealed that Tpx is conserved. Analysis of the crystal packing shows that the linker region of the affinity tag is important for formation of the crystal lattice

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 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