Measurement of low-energy antiproton detection efficiency in BESS below 1 GeV

An accelerator experiment was performed using a low-energy antiproton beam to measure antiproton detection efficiency of BESS, a balloon-borne spectrometer with a superconducting solenoid. Measured efficiencies showed good agreement with calculated ones derived from the BESS Monte Carlo simulation based on GEANT/GHEISHA. With detailed verification of the BESS simulation, the relative systematic error of detection efficiency derived from the BESS simulation has been determined to be $\pm$5%, compared with the previous estimation of $\pm$15% which was the dominant uncertainty for measurements of cosmic-ray antiproton flux.Comment: 13 pages, 7 figure

Measurements of atmospheric muon spectra at mountain altitude

We report new measurements of the atmospheric muons at mountain altitude. The measurement was carried out with the BESS detector at the top of Mt. Norikura, Japan. The altitude is 2,770 m above sea level. Comparing our results and predictions given by some interaction models, a further appropriate model has been investigated. These studies would improve accuracy of atmospheric neutrino calculations.Comment: Mean momentum in Table 1 was correcte

Measurements of Cosmic-ray Low-energy Antiproton and Proton Spectra in a Transient Period of the Solar Field Reversal

The energy spectra of cosmic-ray low-energy antiprotons and protons have been measured by BESS in 1999 and 2000, during a period covering the solar magnetic field reversal. Based on these measurements, a sudden increase of the antiproton to proton flux ratio following the solar magnetic field reversal was observed, and it generally agrees with a drift model of the solar modulation.Comment: 4 pages, 4 figures, revised version accepted for publication in Phys. Rev. Let

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Polarization-assisted surface engineering for low temperature degradation-proof in yttria-stabilized zirconia ceramics

This study demonstrates that electrical polarization inhibited low-temperature degradation (LTD) of yttria-stabilized zirconia (YSZ). Electrical polarizations, which were confirmed by the thermally stimulated depolarization current (TSDC) measurements, were induced by an applied dc voltage. The induced polarizations caused hydrophilic surfaces. The more hydrophilic the surfaces became, the more resistant they became to degradation. We propose a mechanism of LTD inhibition in which the electric repulsive force induced by the polarizations prevented the invasion of water into the inner part of the YSZ

Texture-Based Neural Network Model for Biometric Dental Applications

Background: The aim is to classify dentition using a novel texture-based automated convolutional neural network (CNN) for forensic and prosthetic applications. Methods: Natural human teeth (n = 600) were classified, cleaned, and inspected for exclusion criteria. The teeth were scanned with an intraoral scanner and identified using a texture-based CNN in three steps. First, through preprocessing, teeth images were segmented by extracting the front-facing region of the teeth. Then, texture features were extracted from the segmented teeth images using the discrete wavelet transform (DWT) method. Finally, deep learning-based enhanced CNN models were used to identify these images. Several experiments were conducted using five different CNN models with various batch sizes and epochs, with and without augmented data. Results: Based on experiments with five different CNN models, the highest accuracy achieved was 0.8 and the precision was 0.8 with a loss value of 0.9, a batch size of 32, and 250 epochs. A comparison of deep learning models with different parameters showed varied accuracy between the different classes of teeth. Conclusion: The accuracy of the point-based CNN method was promising. This texture-identification method will pave the way for many forensic and prosthodontic applications and will potentially help improve the precision of dental biometrics

Thermal Treatment of a Keggin‐Type Diplatinum(II)‐Coordinated Polyoxotungstate: Formation of Hydrophilic Colloidal Particles and Photocatalytic Hydrogen Production

autho