Confidence Intervals For An Effect Size When Variances Are Not Equal

Confidence intervals must be robust in having nominal and actual probability coverage in close agreement. This article examined two ways of computing an effect size in a two-group problem: (a) the classic approach which divides the mean difference by a single standard deviation and (b) a variant of a method which replaces least squares values with robust trimmed means and a Winsorized variance. Confidence intervals were determined with theoretical and bootstrap critical values. Only the method that used robust estimators and a bootstrap critical value provided generally accurate probability coverage under conditions of nonnormality and variance heterogeneity in balanced as well as unbalanced designs

Confidence Intervals for the Squared Multiple Semipartial Correlation Coefficient

The squared multiple semipartial correlation coefficient is the increase in the squared multiple correlation coefficient that occurs when two or more predictors are added to a multiple regression model. Coverage probability was investigated for two variations of each of three methods for setting confidence intervals for the population squared multiple semipartial correlation coefficient. Results indicated that the procedure that provides coverage probability in the [.925, .975] interval for a 95% confidence interval depends primarily on the number of added predictors. Guidelines for selecting a procedure are presented

High-fidelity state detection and tomography of a single ion Zeeman qubit

We demonstrate high-fidelity Zeeman qubit state detection in a single trapped 88 Sr+ ion. Qubit readout is performed by shelving one of the qubit states to a metastable level using a narrow linewidth diode laser at 674 nm followed by state-selective fluorescence detection. The average fidelity reached for the readout of the qubit state is 0.9989(1). We then measure the fidelity of state tomography, averaged over all possible single-qubit states, which is 0.9979(2). We also fully characterize the detection process using quantum process tomography. This readout fidelity is compatible with recent estimates of the detection error-threshold required for fault-tolerant computation, whereas high-fidelity state tomography opens the way for high-precision quantum process tomography

High order magnon bound states in the quasi-one-dimensional antiferromagnet α\alpha-NaMnO2_2

Here we report on the formation of two and three magnon bound states in the quasi-one-dimensional antiferromagnet $\alpha$-NaMnO$_2$, where the single-ion, uniaxial anisotropy inherent to the Mn$^{3+}$ ions in this material provides a binding mechanism capable of stabilizing higher order magnon bound states. While such states have long remained elusive in studies of antiferromagnetic chains, neutron scattering data presented here demonstrate that higher order $n>2$ composite magnons exist, and, specifically, that a weak three-magnon bound state is detected below the antiferromagnetic ordering transition of NaMnO$_2$. We corroborate our findings with exact numerical simulations of a one-dimensional Heisenberg chain with easy-axis anisotropy using matrix-product state techniques, finding a good quantitative agreement with the experiment. These results establish $\alpha$-NaMnO$_2$ as a unique platform for exploring the dynamics of composite magnon states inherent to a classical antiferromagnetic spin chain with Ising-like single ion anisotropy.Comment: 5 pages, 4 figure

Galaxy Satellites and the Weak Equivalence Principle

Numerical simulations of the effect of a long-range scalar interaction (LRSI) acting only on nonbaryonic dark matter, with strength comparable to gravity, show patterns of disruption of satellites that can agree with what is seen in the Milky Way. This includes the symmetric Sagittarius stellar stream. The exception presented here to the Kesden and Kamionkowski demonstration that an LRSI tends to produce distinctly asymmetric streams follows if the LRSI is strong enough to separate the stars from the dark matter before tidal disruption of the stellar component, and if stars dominate the mass in the luminous part of the satellite. It requires that the Sgr galaxy now contains little dark matter, which may be consistent with the Sgr stellar velocity dispersion, for in the simulation the dispersion at pericenter exceeds virial. We present other examples of simulations in which a strong LRSI produces satellites with large mass-to-light ratio, as in Draco, or free streams of stars, which might be compared to "orphan" streams.Comment: 14 pages, accepted for publication in PR

A multistage mathematical approach to automated clustering of high-dimensional noisy data

A critical problem faced in many scientific fields is the adequate separation of data derived from individual sources. Often, such datasets require analysis of multiple features in a highly multidimensional space, with overlap of features and sources. The datasets generated by simultaneous recording from hundreds of neurons emitting phasic action potentials have produced the challenge of separating the recorded signals into independent data subsets (clusters) corresponding to individual signal-generating neurons. Mathematical methods have been developed over the past three decades to achieve such spike clustering, but a complete solution with fully automated cluster identification has not been achieved. We propose here a fully automated mathematical approach that identifies clusters in multidimensional space through recursion, which combats the multidimensionality of the data. Recursion is paired with an approach to dimensional evaluation, in which each dimension of a dataset is examined for its informational importance for clustering. The dimensions offering greater informational importance are given added weight during recursive clustering. To combat strong background activity, our algorithm takes an iterative approach of data filtering according to a signal-to-noise ratio metric. The algorithm finds cluster cores, which are thereafter expanded to include complete clusters. This mathematical approach can be extended from its prototype context of spike sorting to other datasets that suffer from high dimensionality and background activity.National Institutes of Health (U.S.) (Grant R01 MH060379)United States. Defense Advanced Research Projects AgencyUnited States. Army Research Office (Grant W911NF-10-1-0059)Cure Huntington’s Disease Initiative, Inc. (Grant A-5552

A Novel, Robust Quantum Detection Scheme

Protocols used in quantum information and precision spectroscopy rely on efficient internal quantum state discrimination. With a single ion in a linear Paul trap, we implement a novel detection method which utilizes correlations between two detection events with an intermediate spin-flip. The technique is experimentally characterized as more robust against fluctuations in detection laser power compared to conventionally implemented methods. Furthermore, systematic detection errors which limit the Rabi oscillation contrast in conventional methods are overcome

The clustering of SDSS galaxy groups: mass and color dependence

We use a sample of galaxy groups selected from the SDSS DR 4 with an adaptive halo-based group finder to probe how the clustering strength of groups depends on their masses and colors. In particular, we determine the relative biases of groups of different masses, as well as that of groups with the same mass but with different colors. In agreement with previous studies, we find that more massive groups are more strongly clustered, and the inferred mass dependence of the halo bias is in good agreement with predictions for the $\Lambda$CDM cosmology. Regarding the color dependence, we find that groups with red centrals are more strongly clustered than groups of the same mass but with blue centrals. Similar results are obtained when the color of a group is defined to be the total color of its member galaxies. The color dependence is more prominent in less massive groups and becomes insignificant in groups with masses \gta 10^{14}\msunh. We construct a mock galaxy redshift survey constructed from the large Millenium simulation that is populated with galaxies according to the semi-analytical model of Croton et al. Applying our group finder to this mock survey, and analyzing the mock data in exactly the same way as the true data, we are able to accurately recover the intrinsic mass and color dependencies of the halo bias in the model. This suggests that our group finding algorithm and our method of assigning group masses do not induce spurious mass and/or color dependencies in the group-galaxy correlation function. The semi-analytical model reveals the same color dependence of the halo bias as we find in our group catalogue. In halos with M\sim 10^{12}\msunh, though, the strength of the color dependence is much stronger in the model than in the data.Comment: 16 pages, 14 figures, Accepted for publication in ApJ. In the new version, we add the bias of the shuffled galaxy sample. The errors are estimated according to the covariance matrix of the GGCCF, which is then diagonalize