138 research outputs found
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 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
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