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Register a new userThe Software Package for Astronomical Reductions with KMOS: SPARK
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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.
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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.
Over the past few decades, the measurement precision of some pulsar-timing experiments has advanced from ~10 us to ~10 ns, revealing many subtle phenomena. Such high precision demands both careful data handling and sophisticated timing models to avoid systematic error. To achieve these goals, we present PINT (PINT Is Not Tempo3), a high-precision Python pulsar timing data analysis package, which is hosted on GitHub and available on Python Package Index (PyPI) as pint-pulsar. PINT is well-tested, validated, object-oriented, and modular, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. It utilizes well-debugged public Python packages (e.g., the NumPy and Astropy libraries) and modern software development schemes (e.g., version control and efficient development with git and GitHub) and a continually expanding test suite for improved reliability, accuracy, and reproducibility. PINT is developed and implemented without referring to, copying, or transcribing the code from other traditional pulsar timing software packages (e.g., TEMPO and TEMPO2) and therefore provides a robust tool for cross-checking timing analyses and simulating pulse arrival times. In this paper, we describe the design, usage, and validation of PINT, and we compare timing results between it and TEMPO and TEMPO2.
astroquery is a collection of tools for requesting data from databases hosted on remote servers with interfaces exposed on the internet, including those with web pages but without formal application program interfaces (APIs). These tools are built on the Python requests package, which is used to make HTTP requests, and astropy, which provides most of the data parsing functionality. astroquery modules generally attempt to replicate the web page interface provided by a given service as closely as possible, making the transition from browser-based to command-line interaction easy. astroquery has received significant contributions from throughout the astronomical community, including several significant contributions from telescope archives. astroquery enables the creation of fully reproducible workflows from data acquisition through publication. This paper describes the philosophy, basic structure, and development model of the astroquery package. The complete documentation for astroquery can be found at http://astroquery.readthedocs.io/.
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 describe a general analysis package for supernova (SN) light curves, called SNANA, that contains a simulation, light curve fitter, and cosmology fitter. The software is designed with the primary goal of using SNe Ia as distance indicators for the determination of cosmological parameters, but it can also be used to study efficiencies for analyses of SN rates, estimate contamination from non-Ia SNe, and optimize future surveys. Several SN models are available within the same software architecture, allowing technical features such as K-corrections to be consistently used among multiple models, and thus making it easier to make detailed comparisons between models. New and improved light-curve models can be easily added. The software works with arbitrary surveys and telescopes and has already been used by several collaborations, leading to more robust and easy-to-use code. This software is not intended as a final product release, but rather it is designed to undergo continual improvements from the community as more is learned about SNe. Below we give an overview of the SNANA capabilities, as well as some of its limitations. Interested users can find software downloads and more detailed information from the manuals at http://www.sdss.org/supernova/SNANA.html .
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