The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Hydroxyl radical generation by cactus-like copper oxide nanoporous carbon catalysts for microcystin-LR environmental remediation

Copper oxide supported on nanoporous activated carbon (CuO-NPAC) is reported for the aqueous phase catalytic degradation of cyanotoxin microcystin-LR (MC-LR). The loading and spatial distribution of CuO throughout the NPAC matrix strongly influence the catalytic efficiency. CuO-NPAC synthesis was optimized with respect to the copper loading and thermal processing, and the physicochemical properties of the resulting materials were characterized by XRD, BET, TEM, SEM, EPR, TGA, XPS and FT-IR spectroscopy. EPR spin trapping and fluorescence spectroscopy showed in situ. OH formation via H2O2 over CuO-NPAC as the catalytically relevant oxidant. The impact of reaction conditions, notably CuO-NPAC loading, H2O2 concentration and solution pH, is discussed in relation to the reaction kinetics for MC-LR remediation

Reconstruction of Three-Dimensional Aquifer Heterogeneity from Two-Dimensional Geophysical Data

Suitable training images (TIs) for multiple-point statistics (MPS) are difficult to identify in real-case three-dimensional applications, posing challenges for modelers trying to develop realistic subsurface models. This study demonstrates that two-dimensional geophysical images, when employed as training and conditioning data, can provide sufficient information for three-dimensional MPS simulations. The advantage of such a data-driven approach is that it does not rely on any external (possibly inappropriate) TI. The disadvantage is that three-dimensional MPS simulations must be carried out based on two-dimensional information. Three different approaches (two existing, one new) are tested to overcome this problem. The two existing approaches rely on three-dimensional reconstruction of incomplete datasets and on sequential two-dimensional simulations, respectively. The third approach is a newly proposed combination of the two former approaches. The three approaches are applied to model the three-dimensional facies structure of an alluvial aquifer based on high-resolution ground-penetrating radar cross-sections. The quality of each simulation outcome is evaluated based on the similarity of its multiple-point histogram (MPH) to reference MPHs derived from geophysical images. This evaluation reveals that the first approach (three-dimensional reconstruction) performs well close to conditioning data, but farther away from the data the simulation results deteriorate. Quite conversely, the second approach (sequential two-dimensional) performs well when only few conditioning data exist, but with increasing simulation sequence the quality decreases. The newly proposed third approach integrates the benefits of both approaches and is found to reproduce the reference MPHs significantly better than either of the two other approaches alone

Training Images from Process-Imitating Methods : An Application to the Lower Namoi Aquifer, Murray-Darling Basin, Australia

The lack of a suitable training image is one of the main limitations of the application of multiple-point statistics (MPS) for the characterization of heterogeneity in real case studies. Process-imitating facies modeling techniques can potentially provide training images. However, the parameterization of these process-imitating techniques is not straightforward. Moreover, reproducing the resulting heterogeneous patterns with standard MPS can be challenging. Here the statistical properties of the paleoclimatic data set are used to select the best parameter sets for the process-imitating methods. The data set is composed of 278 lithological logs drilled in the lower Namoi catchment, New South Wales, Australia. A good understanding of the hydrogeological connectivity of this aquifer is needed to tackle groundwater management issues. The spatial variability of the facies within the lithological logs and calculated models is measured using fractal dimension, transition probability, and vertical facies proportion. To accommodate the vertical proportions trend of the data set, four different training images are simulated. The grain size is simulated alongside the lithological codes and used as an auxiliary variable in the direct sampling implementation of MPS. In this way, one can obtain conditional MPS simulations that preserve the quality and the realism of the training images simulated with the process-imitating method. The main outcome of this study is the possibility of obtaining MPS simulations that respect the statistical properties observed in the real data set and honor the observed conditioning data, while preserving the complex heterogeneity generated by the process-imitating method. In addition, it is demonstrated that an equilibrium of good fit among all the statistical properties of the data set should be considered when selecting a suitable set of parameters for the process-imitating simulations