No Arabic abstract
Astronomical software is now a fact of daily life for all hands-on members of our community. Purpose-built software for data reduction and modeling tasks becomes ever more critical as we handle larger amounts of data and simulations. However, the writing of astronomical software is unglamorous, the rewards are not always clear, and there are structural disincentives to releasing software publicly and to embedding it in the scientific literature, which can lead to significant duplication of effort and an incomplete scientific record. We identify some of these structural disincentives and suggest a variety of approaches to address them, with the goals of raising the quality of astronomical software, improving the lot of scientist-authors, and providing benefits to the entire community, analogous to the benefits provided by open access to large survey and simulation datasets. Our aim is to open a conversation on how to move forward. We advocate that: (1) the astronomical community consider software as an integral and fundable part of facility construction and science programs; (2) that software release be considered as integral to the open and reproducible scientific process as are publication and data release; (3) that we adopt technologies and repositories for releasing and collaboration on software that have worked for open-source software; (4) that we seek structural incentives to make the release of software and related publications easier for scientist-authors; (5) that we consider new ways of funding the development of grass-roots software; (6) and that we rethink our values to acknowledge that astronomical software development is not just a technical endeavor, but a fundamental part of our scientific practice.
We have created a new semantic tool called AstroConcepts, providing definitions of astronomical concepts present on Web pages. This tool is a Google Chrome plug-in that interrogates the Etymological Dictionary of Astronomy and Astrophysics, developed at Paris Observatory. Thanks to this tool, if one selects an astronomical concept on a web page, a pop-up window will display the definition of the available English or French terms. Another expected use of this facility could be its implementation in Virtual Observatory services.
KMOS is a multi-object near-infrared integral field spectrometer with 24 deployable cryogenic pick-off arms. Inevitably, data processing is a complex task that requires careful calibration and quality control. In this paper we describe all the steps involved in producing science-quality data products from the raw observations. In particular, we focus on the following issues: (i) the calibration scheme which produces maps of the spatial and spectral locations of all illuminated pixels on the detectors; (ii) our concept of minimising the number of interpolations, to the limiting case of a single reconstruction that simultaneously uses raw data from multiple exposures; (iii) a comparison of the various interpolation methods implemented, and an assessment of the performance of true 3D interpolation schemes; (iv) the way in which instrumental flexure is measured and compensated. We finish by presenting some examples of data processed using the pipeline.
In this paper we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions etc. -- a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world.
The U.S. Virtual Astronomical Observatory was a software infrastructure and development project designed both to begin the establishment of an operational Virtual Observatory (VO) and to provide the U.S. coordination with the international VO effort. The concept of the VO is to provide the means by which an astronomer is able to discover, access, and process data seamlessly, regardless of its physical location. This paper describes the origins of the VAO, including the predecessor efforts within the U.S. National Virtual Observatory, and summarizes its main accomplishments. These accomplishments include the development of both scripting toolkits that allow scientists to incorporate VO data directly into their reduction and analysis environments and high-level science applications for data discovery, integration, analysis, and catalog cross-comparison. Working with the international community, and based on the experience from the software development, the VAO was a major contributor to international standards within the International Virtual Observatory Alliance. The VAO also demonstrated how an operational virtual observatory could be deployed, providing a robust operational environment in which VO services worldwide were routinely checked for aliveness and compliance with international standards. Finally, the VAO engaged in community outreach, developing a comprehensive web site with on-line tutorials, announcements, links to both U.S. and internationally developed tools and services, and exhibits and hands-on training .... All digital products of the VAO Project, including software, documentation, and tutorials, are stored in a repository for community access. The enduring legacy of the VAO is an increasing expectation that new telescopes and facilities incorporate VO capabilities during the design of their data management systems.
System assurance is confronted by significant challenges. Some of these are new, for example, autonomous systems with major functions driven by machine learning and AI, and ultra-rapid system development, while others are the familiar, persistent issues of the need for efficient, effective and timely assurance. Traditional assurance is seen as a brake on innovation and often costly and time consuming. We therefore propose a modernized framework, Assurance 2.0, as an enabler that supports innovation and continuous incremental assurance. Perhaps unexpectedly, it does so by making assurance more rigorous, with increased focus on the reasoning and evidence employed, and explicit identification of defeaters and counterevidence.