Laser microscopy of tunneling magnetoresistance in manganite grain-boundary junctions

Using low-temperature scanning laser microscopy we directly image electric transport in a magnetoresistive element, a manganite thin film intersected by a grain boundary (GB). Imaging at variable temperature allows reconstruction and comparison of the local resistance vs temperature for both, the manganite film and the GB. Imaging at low temperature also shows that the GB switches between different resistive states due to the formation and growth of magnetic domains along the GB. We observe different types of domain wall growth; in most cases a domain wall nucleates at one edge of the bridge and then proceeds towards the other edge.Comment: 5 pages, 4 figures; submitted to Phys. Rev. Let

Sensitivity to the KARMEN Timing Anomaly at MiniBooNE

We present sensitivities for the MiniBooNE experiment to a rare exotic pion decay producing a massive particle, Q^0. This type of decay represents one possible explanation for the timing anomaly reported by the KARMEN collaboration. MiniBooNE will be able to explore an area of the KARMEN signal that has not yet been investigated

β\beta-Decay Spectrum, Response Function and Statistical Model for Neutrino Mass Measurements with the KATRIN Experiment

The objective of the Karlsruhe Tritium Neutrino (KATRIN) experiment is to determine the effective electron neutrino mass $m(\nu_\text{e})$ with an unprecedented sensitivity of $0.2\,\text{eV}$ (90\% C.L.) by precision electron spectroscopy close to the endpoint of the $\beta$ decay of tritium. We present a consistent theoretical description of the $\beta$ electron energy spectrum in the endpoint region, an accurate model of the apparatus response function, and the statistical approaches suited to interpret and analyze tritium $\beta$ decay data observed with KATRIN with the envisaged precision. In addition to providing detailed analytical expressions for all formulae used in the presented model framework with the necessary detail of derivation, we discuss and quantify the impact of theoretical and experimental corrections on the measured $m(\nu_\text{e})$. Finally, we outline the statistical methods for parameter inference and the construction of confidence intervals that are appropriate for a neutrino mass measurement with KATRIN. In this context, we briefly discuss the choice of the $\beta$ energy analysis interval and the distribution of measuring time within that range.Comment: 27 pages, 22 figures, 2 table

The Anderson-Mott transition induced by hole-doping in Nd1-xTiO3

The insulator/metal transition induced by hole-doping due to neodymium vacancies of the Mott- Hubbard antiferromagnetic insulator, Nd1-xTiO3, is studied over the composition range 0.010(6) < x < 0.243(10). Insulating p-types conduction is found for x < 0.071(10). Anderson localization in the presence of a Mott-Hubbard gap, is the dominant localization mechanism for the range of 0.074(10) < x < 0.089(1) samples. For x < 0.089(1), n-type conduction is observed and the activation energy extrapolates to zero by x < 0.1. The 0.095(8) < x < 0.203(10) samples are Fermi-liquid metals and the effects of strong electronic correlations are evident near the metal-to-insulator boundaries in features such as large Fermi liquid T2 coefficients. For 0.074(9) < x < 0.112(4), a weak negative magnetoresistance is found below ~ 15 K and it is attributed to the interaction of conduction electrons with Nd3+ magnetic moments. Combining information from our companion study of the magnetic properties of Nd1-xTiO3 solid solution, a phase diagram is proposed. The main conclusions are that long range antiferromagnetic order disappears before the onset of metallic behavior and that the Anderson-Mott transition occurs over a finite range of doping levels. Our results differ from conclusions drawn from a similar study on the hole doped Nd1-xCaxTiO3 system which found the co-existence of antiferromagnetic order and metallic behavior and that the Mott transition occurs at a discrete doping level

First-principles study of (BiScO3){1-x}-(PbTiO3){x} piezoelectric alloys

We report a first-principles study of a class of (BiScO3)_{1-x}-(PbTiO3)_x (BS-PT) alloys recently proposed by Eitel et al. as promising materials for piezoelectric actuator applications. We show that (i) BS-PT displays very large structural distortions and polarizations at the morphotropic phase boundary (MPB) (we obtain a c/a of ~1.05-1.08 and P_tet of ~1.1 C/m^2); (ii) the ferroelectric and piezoelectric properties of BS-PT are dominated by the onset of hybridization between Bi/Pb-6p and O-2p orbitals, a mechanism that is enhanced upon substitution of Pb by Bi; and (iii) the piezoelectric responses of BS-PT and Pb(Zr_{1-x}Ti_x)O3 (PZT) at the MPB are comparable, at least as far as the computed values of the piezoelectric coefficient d_15 are concerned. While our results are generally consistent with experiment, they also suggest that certain intrinsic properties of BS-PT may be even better than has been indicated by experiments to date. We also discuss results for PZT that demonstrate the prominent role played by Pb displacements in its piezoelectric properties.Comment: 6 pages, with 3 postscript figures embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/ji_bi/index.htm

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Observables sensitive to absolute neutrino masses: A reappraisal after WMAP-3y and first MINOS results

In the light of recent neutrino oscillation and non-oscillation data, we revisit the phenomenological constraints applicable to three observables sensitive to absolute neutrino masses: The effective neutrino mass in single beta decay (m_beta); the effective Majorana neutrino mass in neutrinoless double beta decay (m_2beta); and the sum of neutrino masses in cosmology (Sigma). In particular, we include the constraints coming from the first Main Injector Neutrino Oscillation Search (MINOS) data and from the Wilkinson Microwave Anisotropy Probe (WMAP) three-year (3y) data, as well as other relevant cosmological data and priors. We find that the largest neutrino squared mass difference is determined with a 15% accuracy (at 2-sigma) after adding MINOS to world data. We also find upper bounds on the sum of neutrino masses Sigma ranging from ~2 eV (WMAP-3y data only) to ~0.2 eV (all cosmological data) at 2-sigma, in agreement with previous studies. In addition, we discuss the connection of such bounds with those placed on the matter power spectrum normalization parameter sigma_8. We show how the partial degeneracy between Sigma and sigma_8 in WMAP-3y data is broken by adding further cosmological data, and how the overall preference of such data for relatively high values of sigma_8 pushes the upper bound of Sigma in the sub-eV range. Finally, for various combination of data sets, we revisit the (in)compatibility between current Sigma and m_2beta constraints (and claims), and derive quantitative predictions for future single and double beta decay experiments.Comment: 18 pages, including 7 figure

Statistical Analysis of Different Muon-antineutrino->Electron-antineutrino Searches

A combined statistical analysis of the experimental results of the LSND and KARMEN \numubnueb oscillation search is presented. LSND has evidence for neutrino oscillations that is not confirmed by the KARMEN experiment. This joint analysis is based on the final likelihood results for both data sets. A frequentist approach is applied to deduce confidence regions. At a combined confidence level of 36%, there is no area of oscillation parameters compatible with both experiments. For the complementary confidence of 1-0.36=64%, there are two well defined regions of oscillation parameters (sin^2(2th),Dm^2) compatible with both experiments.Comment: 25 pages, including 10 figures, submitted to Phys. Rev.