Calibrating CIV-based black hole mass estimators

We present the single-epoch black hole mass estimators based on the CIV (1549 A) broad emission line, using the updated sample of the reverberation-mapped AGNs and high-quality UV spectra. By performing multi-component spectral fitting analysis, we measure the CIV line widths (FWHM_CIV) and line dispersion (sigma_CIV) and the continuum luminosity at 1350 A (L_1350) to calibrate the CIV-based mass estimators. By comparing with the Hbeta reverberation-based masses, we provide new mass estimators with the best-fit relationships, i.e., M_BH \propto L_1350 ^ (0.50+-0.07) sigma_CIV ^2 and M_BH \propto L_1350 ^ (0.52+-0.09) FWHM_CIV ^ (0.56+-0.48). The new CIV-based mass estimators show significant mass-dependent systematic difference compared to the estimators commonly used in the literature. Using the published Sloan Digital Sky Survey QSO catalog, we show that the black hole mass of high-redshift QSOs decreases on average by ~0.25 dex if our recipe is adopted.Comment: 12 pages, 7 figures, ApJ in press, figure revise

Automated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test

This study proposes an automated brittle fracture rate (BFR) estimator using deep learning. As the demand for line-pipes increases in various industries, the need for BFR estimation through dropweight tear test (DWTT) increases to evaluate steel's property. Conventional BFR or ductile fracture rate (DFR) estimation methods require an expensive 3D scanner. Alternatively, a rule-based approach is used with a single charge-coupled device (CCD) camera. However, it is sensitive to the hyper-parameter. To solve these problems, we propose an approach based on deep learning that has recently been successful in the fields of computer vision and image processing. The method proposed in this study is the first to use deep learning approach for BFR estimation. The proposed method consists of a VGG-based U-Net (VU-Net) which is inspired by U-Net and fully convolutional network (FCN). VU-Net includes a deep encoder and a decoder. The encoder is adopted from VGG19 and transferred with a pre-trained model with ImageNet. In addition, the structure of the decoder is the same as that of the encoder, and the decoder uses the feature maps of the encoder through concatenation operation to compensate for the reduced spatial information. To analyze the proposed VU-Net, we experimented with different depths of networks and various transfer learning approaches. In terms of accuracy used in real industrial application, we compared the proposed VU-Net with U-Net and FCN to evaluate the performance. The experiments showed that VU-Net was the accuracy of approximately 94.9 %, and was better than the other two, which had the accuracies of about 91.8 % and 93.7 %, respectively.11Ysciescopu

Spin relaxation in mesoscopic superconducting Al wires

We studied the diffusion and the relaxation of the polarized quasiparticle spins in superconductors. To that end, quasiparticles of polarized spins were injected through an interface of a mesoscopic superconducting Al wire in proximity contact with an overlaid ferromagnetic Co wire in the single-domain state. The superconductivity was observed to be suppressed near the spin-injecting interface, as evidenced by the occurrence of a finite voltage for a bias current below the onset of the superconducting transition. The spin diffusion length, estimated from finite voltages over a certain length of Al wire near the interface, was almost temperature independent in the temperature range sufficiently below the superconducting transition but grew as the transition temperature was approached. This temperature dependence suggests that the relaxation of the spin polarization in the superconducting state is governed by the condensation of quasiparticles to the paired state. The spin relaxation in the superconducting state turned out to be more effective than in the normal state.Comment: 9 pages, 8 figure