Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects

Bayesian neural network (BNN) approach is employed to improve the nuclear mass predictions of various models. It is found that the noise error in the likelihood function plays an important role in the predictive performance of the BNN approach. By including a distribution for the noise error, an appropriate value can be found automatically in the sampling process, which optimizes the nuclear mass predictions. Furthermore, two quantities related to nuclear pairing and shell effects are added to the input layer in addition to the proton and mass numbers. As a result, the theoretical accuracies are significantly improved not only for nuclear masses but also for single-nucleon separation energies. Due to the inclusion of the shell effect, in the unknown region, the BNN approach predicts a similar shell-correction structure to that in the known region, e.g., the predictions of underestimation of nuclear mass around the magic numbers in the relativistic mean-field model. This manifests that better predictive performance can be achieved if more physical features are included in the BNN approach.Comment: 15 pages, 4 figures, and 3 table

An X-ray and radio study of the cluster A 2717

We present an X-ray, radio and optical study of the cluster A 2717. The central D galaxy is associated with aWide- Angled-Tailed (WAT) radio source. A Rosat PSPC observation of the cluster shows that the cluster has a well constrained temperature of 1.9+0.3 −0.2 × 107 K. The pressure of the intracluster medium was found to be comparable to the mininum pressure of the radio source suggesting that the tails may in fact be in equipartition with the surrounding hot gas

An X-ray and Radio study of the Cluster A2717

We present an X-ray, radio and optical study of the cluster A2717. The central D galaxy is associated with a Wide-Angled-Tailed (WAT) radio source. A Rosat PSPC observation of the cluster shows that the cluster has a well constrained temperature of 2x10^7 K. The pressure of the intracluster medium was found to be comparable to the mininum pressure of the radio source suggesting that the tails may in fact be in equipartition with the surrounding hot gas.Comment: 7 pages, 6 Postscript figures, to appear in Astronomy and Astrophysics 199

High resolution fourier domain optical coherence tomography at 2 microns for painted objects

Optical Coherence Tomography has been successfully applied to the non-invasive imaging of subsurface microstructure of a variety of materials from biological tissues to painted objects of art. One of the limitations of the technique is the low depth of penetration due to the strong scattering and absorption in the material. Previous studies found that for paint materials, the optimum window for large depth of penetration is around 2.2 microns. This is also true for many other materials with low water content. We have previously demonstrated OCT systems in this wavelength regime for imaging with improved depth of penetration. In this paper, we present an improved 2 micron high resolution Fourier domain OCT system using a broadband supercontinuum source. The system achieved a depth resolution of 9 microns in air (or 6 microns in paint or any polymer)

Long wavelength optical coherence tomography for painted objects

Optical Coherence Tomography has been successfully applied to the imaging of painted objects in recent years. However, a significant limitation is the low penetration depth of OCT in paint because of the high opacity of paint due to either scattering or absorption. It has been shown that the optimum spectral window for OCT imaging of paint layers is around 2.2μm in wavelength. In this paper, we demonstrate a 1950nm OCT for imaging painted objects using a superfluorescent fiber source at low power