Brazing process provides high-strength bond between aluminum and stainless steel

Brazing process uses vapor-deposited titanium and an aluminum-zirconium-silicon alloy to prevent formation of brittle intermetallic compounds in stainless steel and aluminum bonding. Joints formed by this process maintain their high strength, corrosion resistance, and hermetic sealing properties

Method of joining aluminum to stainless steel Patent

Joining aluminum to stainless steel by bonding aluminum coatings onto titanium coated stainless steel and brazing aluminum to aluminum/titanium coated stee

Study of space environment fabrication and repair techniques Final technical summary report

Joining techniques for fabrication and repair of structures in extraterrestrial environmen

The L_X--M relation of Clusters of Galaxies

We present a new measurement of the scaling relation between X-ray luminosity and total mass for 17,000 galaxy clusters in the maxBCG cluster sample. Stacking sub-samples within fixed ranges of optical richness, N_200, we measure the mean 0.1-2.4 keV X-ray luminosity, , from the ROSAT All-Sky Survey. The mean mass, , is measured from weak gravitational lensing of SDSS background galaxies (Johnston et al. 2007). For 9 <= N_200 < 200, the data are well fit by a power-law, /10^42 h^-2 erg/s = (12.6+1.4-1.3 (stat) +/- 1.6 (sys)) (/10^14 h^-1 M_sun)^1.65+/-0.13. The slope agrees to within 10% with previous estimates based on X-ray selected catalogs, implying that the covariance in L_X and N_200 at fixed halo mass is not large. The luminosity intercent is 30%, or 2\sigma, lower than determined from the X-ray flux-limited sample of Reiprich & Bohringer (2002), assuming hydrostatic equilibrium. This difference could arise from a combination of Malmquist bias and/or systematic error in hydrostatic mass estimates, both of which are expected. The intercept agrees with that derived by Stanek et al. (2006) using a model for the statistical correspondence between clusters and halos in a WMAP3 cosmology with power spectrum normalization sigma_8 = 0.85. Similar exercises applied to future data sets will allow constraints on the covariance among optical and hot gas properties of clusters at fixed mass.Comment: 5 pages, 1 figure, MNRAS accepte

An Evolving Entropy Floor in the Intracluster Gas?

Non-gravitational processes, such as feedback from galaxies and their active nuclei, are believed to have injected excess entropy into the intracluster gas, and therefore to have modified the density profiles in galaxy clusters during their formation. Here we study a simple model for this so-called preheating scenario, and ask (i) whether it can simultaneously explain both global X-ray scaling relations and number counts of galaxy clusters, and (ii) whether the amount of entropy required evolves with redshift. We adopt a baseline entropy profile that fits recent hydrodynamic simulations, modify the hydrostatic equilibrium condition for the gas by including approx. 20% non-thermal pressure support, and add an entropy floor K_0 that is allowed to vary with redshift. We find that the observed luminosity-temperature (L-T) relations of low-redshift (z=0.05) HIFLUGCS clusters and high-redshift (z=0.8) WARPS clusters are best simultaneously reproduced with an evolving entropy floor of K_0(z)=341(1+z)^{-0.83}h^{-1/3} keV cm^2. If we restrict our analysis to the subset of bright (kT > 3 keV) clusters, we find that the evolving entropy floor can mimic a self-similar evolution in the L-T scaling relation. This degeneracy with self-similar evolution is, however, broken when (0.5 < kT < 3 keV) clusters are also included. The approx. 60% entropy increase we find from z=0.8 to z=0.05 is roughly consistent with that expected if the heating is provided by the evolving global quasar population. Using the cosmological parameters from the WMAP 3-year data with sigma_8=0.76, our best-fit model underpredicts the number counts of the X-ray galaxy clusters compared to those derived from the 158 deg^2 ROSAT PSPC survey. Treating sigma_8 as a free parameter, we find a best-fit value of sigma_8=0.80+/- 0.02.Comment: 14 emulateapj pages with 9 figures, submitted to Ap

Exploiting Cross Correlations and Joint Analyses

In this report, we present a wide variety of ways in which information from multiple probes of dark energy may be combined to obtain additional information not accessible when they are considered separately. Fundamentally, because all major probes are affected by the underlying distribution of matter in the regions studied, there exist covariances between them that can provide information on cosmology. Combining multiple probes allows for more accurate (less contaminated by systematics) and more precise (since there is cosmological information encoded in cross-correlation statistics) measurements of dark energy. The potential of cross-correlation methods is only beginning to be realized. By bringing in information from other wavelengths, the capabilities of the existing probes of dark energy can be enhanced and systematic effects can be mitigated further. We present a mixture of work in progress and suggestions for future scientific efforts. Given the scope of future dark energy experiments, the greatest gains may only be realized with more coordination and cooperation between multiple project teams; we recommend that this interchange should begin sooner, rather than later, to maximize scientific gains.Comment: Report from the "Dark Energy and CMB" working group for the American Physical Society's Division of Particles and Fields long-term planning exercise ("Snowmass"

Generalized Fisher matrices

16.04.15 KB Ok to add accepted version to spira

A machine learning approach to the detection of ghosting and scattered light artifacts in dark energy survey images

Astronomical images are often plagued by unwanted artifacts that arise from a number of sources including imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and artificial satellites. Spurious reflections (known as “ghosts”) and the scattering of light off the surfaces of a camera and/or telescope are particularly difficult to avoid. Detecting ghosts and scattered light efficiently in large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts and scattered light using convolutional neural networks. The model architecture and training procedure are discussed in detail, and the performance on the training and validation set is presented. Testing is performed on data and results are compared with those from a ray-tracing algorithm. As a proof of principle, we have shown that our method is promising for the Rubin Observatory and beyond