The WFPC2 Archival Parallels Project

We describe the methods and procedures developed to obtain a near-automatic combination of WFPC2 images obtained as part of the WFPC2 Archival Pure Parallels program. Several techniques have been developed or refined to ensure proper alignment, registration, and combination of overlapping images that can be obtained at different times and with different orientations. We quantify the success rate and the accuracy of the registration of images of different types, and we develop techniques suitable to equalize the sky background without unduly affecting extended emission. About 600 combined images of the 1,500 eventually planned have already been publicly released through the STScI Archive. The images released to date are especially suited to study star formation in the Magellanic Clouds, the stellar population in the halo of nearby galaxies, and the properties of star-forming galaxies at $z \sim 3$.Comment: 12 pages, 7 figures, to appear in the PAS

Don't Repeat Yourself: Seamless Execution and Analysis of Extensive Network Experiments

This paper presents MACI, the first bespoke framework for the management, the scalable execution, and the interactive analysis of a large number of network experiments. Driven by the desire to avoid repetitive implementation of just a few scripts for the execution and analysis of experiments, MACI emerged as a generic framework for network experiments that significantly increases efficiency and ensures reproducibility. To this end, MACI incorporates and integrates established simulators and analysis tools to foster rapid but systematic network experiments. We found MACI indispensable in all phases of the research and development process of various communication systems, such as i) an extensive DASH video streaming study, ii) the systematic development and improvement of Multipath TCP schedulers, and iii) research on a distributed topology graph pattern matching algorithm. With this work, we make MACI publicly available to the research community to advance efficient and reproducible network experiments