The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
TL;DR: The Astropy project as discussed by the authors is an open-source and openly developed Python packages that provide commonly-needed functionality to the astronomical community, including the core package Astropy, which serves as the foundation for more specialized projects and packages.
Abstract: The Astropy project supports and fosters the development of open-source and openly-developed Python packages that provide commonly-needed functionality to the astronomical community. A key element of the Astropy project is the core package Astropy, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of inter-operable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy project.
Citations
More filters
University of Jyväskylä1, University of California, Los Angeles2, California Polytechnic State University3, Los Alamos National Laboratory4, National Research University – Higher School of Economics5, University of California, Berkeley6, University of Birmingham7, Australian Nuclear Science and Technology Organisation8, University of Washington9, University of Massachusetts Amherst10, University of West Bohemia11, Brigham Young University12, University of Texas at Austin13, Universidade Federal de Minas Gerais14, Google15
TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year.
Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
6,244 citations
Harvard University1, University of Chile2, Rice University3, Heidelberg University4, Pontifical Catholic University of Chile5, University of Nevada, Las Vegas6, Ludwig Maximilian University of Munich7, Tsinghua University8, University of Grenoble9, Wesleyan University10, California State University, Northridge11
TL;DR: The Disk Substructures at High Angular Resolution Project (DSHARP) as mentioned in this paper was the first large-scale project to find and characterize substructures in the spatial distributions of solid particles for a sample of 20 nearby protoplanetary disks, using very high resolution (similar to 0'' 035 or 5 au, FWHM) observations of their 240 GHz (1.25 mm) continuum emission.
Abstract: We introduce the Disk Substructures at High Angular Resolution Project (DSHARP), one of the initial Large Programs conducted with the Atacama Large Millimeter/submillimeter Array (ALMA). The primary goal of DSHARP is to find and characterize substructures in the spatial distributions of solid particles for a sample of 20 nearby protoplanetary disks, using very high resolution (similar to 0.'' 035, or 5 au, FWHM) observations of their 240 GHz (1.25 mm) continuum emission. These data provide a first homogeneous look at the small-scale features in disks that are directly relevant to the planet formation process, quantifying their prevalence, morphologies, spatial scales, spacings, symmetry, and amplitudes, for targets with a variety of disk and stellar host properties. We find that these substructures are ubiquitous in this sample of large, bright disks. They are most frequently manifested as concentric, narrow emission rings and depleted gaps, although large-scale spiral patterns and small arc-shaped azimuthal asymmetries are also present in some cases. These substructures are found at a wide range of disk radii (from a few astronomical units to more than 100 au), are usually compact (less than or similar to 10 au), and show a wide range of amplitudes (brightness contrasts). Here we discuss the motivation for the project, describe the survey design and the sample properties, detail the observations and data calibration, highlight some basic results, and provide a general overview of the key conclusions that are presented in more detail in a series of accompanying articles. The DSHARP data-including visibilities, images, calibration scripts, and more-are released for community use at https://almascience.org/alma-data/lp/DSHARP.
822 citations
TL;DR: Gaia DR2 as mentioned in this paper is the second Gaia data release, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21.
Abstract: We present the second Gaia data release, Gaia DR2, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21. In addition epoch astrometry and photometry are provided for a modest sample of minor planets in the solar system.
A summary of the contents of Gaia DR2 is presented, accompanied by a discussion on the differences with respect to Gaia DR1 and an overview of the main limitations which are still present in the survey. Recommendations are made on the responsible use of Gaia DR2 results.
Gaia DR2 contains celestial positions and the apparent brightness in G for approximately 1.7 billion sources. For 1.3 billion of those sources, parallaxes and proper motions are in addition available. The sample of sources for which variability information is provided is expanded to 0.5 million stars. This data release contains four new elements: broad-band colour information in the form of the apparent brightness in the $G_\mathrm{BP}$ (330--680 nm) and $G_\mathrm{RP}$ (630--1050 nm) bands is available for 1.4 billion sources; median radial velocities for some 7 million sources are presented; for between 77 and 161 million sources estimates are provided of the stellar effective temperature, extinction, reddening, and radius and luminosity; and for a pre-selected list of 14000 minor planets in the solar system epoch astrometry and photometry are presented. Finally, Gaia DR2 also represents a new materialisation of the celestial reference frame in the optical, the Gaia-CRF2, which is the first optical reference frame based solely on extragalactic sources. There are notable changes in the photometric system and the catalogue source list with respect to Gaia DR1, and we stress the need to consider the two data releases as independent.
761 citations
TL;DR: In this paper, the Event Horizon Telescope (EHT) 1.3 mm radio wavelength observations of the supermassive black hole candidate at the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017 April 5-11 observing campaign are presented.
Abstract: We present the calibration and reduction of Event Horizon Telescope (EHT) 1.3 mm radio wavelength observations of the supermassive black hole candidate at the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017 April 5–11 observing campaign. These global very long baseline interferometric observations include for the first time the highly sensitive Atacama Large Millimeter/submillimeter Array (ALMA); reaching an angular resolution of 25 μas, with characteristic sensitivity limits of ~1 mJy on baselines to ALMA and ~10 mJy on other baselines. The observations present challenges for existing data processing tools, arising from the rapid atmospheric phase fluctuations, wide recording bandwidth, and highly heterogeneous array. In response, we developed three independent pipelines for phase calibration and fringe detection, each tailored to the specific needs of the EHT. The final data products include calibrated total intensity amplitude and phase information. They are validated through a series of quality assurance tests that show consistency across pipelines and set limits on baseline systematic errors of 2% in amplitude and 1° in phase. The M87 data reveal the presence of two nulls in correlated flux density at ~3.4 and ~8.3 Gλ and temporal evolution in closure quantities, indicating intrinsic variability of compact structure on a timescale of days, or several light-crossing times for a few billion solar-mass black hole. These measurements provide the first opportunity to image horizon-scale structure in M87.
625 citations
TL;DR: Bilby as discussed by the authors is a Bayesian inference library for gravitational-wave astronomy, which provides expert-level parameter estimation infrastructure with straightforward syntax and tools that facilitate use by beginners, allowing users to perform accurate and reliable gravitationalwave parameter estimation on both real, freely available data from LIGO/Virgo and simulated data.
Abstract: Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. It is the method by which gravitational-wave data is used to infer the sources' astrophysical properties. We introduce a user-friendly Bayesian inference library for gravitational-wave astronomy, Bilby. This Python code provides expert-level parameter estimation infrastructure with straightforward syntax and tools that facilitate use by beginners. It allows users to perform accurate and reliable gravitational-wave parameter estimation on both real, freely available data from LIGO/Virgo and simulated data. We provide a suite of examples for the analysis of compact binary mergers and other types of signal models, including supernovae and the remnants of binary neutron star mergers. These examples illustrate how to change the signal model, implement new likelihood functions, and add new detectors. Bilby has additional functionality to do population studies using hierarchical Bayesian modeling. We provide an example in which we infer the shape of the black hole mass distribution from an ensemble of observations of binary black hole mergers.
442 citations
References
More filters
TL;DR: The multiband periodogram as mentioned in this paper is a general extension of the well-known Lomb-Scargle approach for detecting periodic signals in time-domain data in addition to advantages of the Lomb-scargle method such as treatment of non-uniform sampling and heteroscedastic errors, which significantly improves period finding for randomly sampled multiband light curves.
Abstract: This paper introduces the multiband periodogram, a general extension of the well-known Lomb-Scargle approach for detecting periodic signals in time-domain data In addition to advantages of the Lomb-Scargle method such as treatment of non-uniform sampling and heteroscedastic errors, the multiband periodogram significantly improves period finding for randomly sampled multiband light curves (eg, Pan-STARRS, DES and LSST) The light curves in each band are modeled as arbitrary truncated Fourier series, with the period and phase shared across all bands The key aspect is the use of Tikhonov regularization which drives most of the variability into the so-called base model common to all bands, while fits for individual bands describe residuals relative to the base model and typically require lower-order Fourier series This decrease in the effective model complexity is the main reason for improved performance We use simulated light curves and randomly subsampled SDSS Stripe 82 data to demonstrate the superiority of this method compared to other methods from the literature, and find that this method will be able to efficiently determine the correct period in the majority of LSST's bright RR Lyrae stars with as little as six months of LSST data A Python implementation of this method, along with code to fully reproduce the results reported here, is available on GitHub
193 citations
Posted Content•
TL;DR: IAU 2015 Resolution B3 adopts a set of nominal solar, terrestrial, and jovian conversion constants for stellar and (exo)planetary astronomy which are defined to be exact SI values as mentioned in this paper.
Abstract: Astronomers commonly quote the properties of celestial objects in units of parameters for the Sun, Jupiter, or the Earth. The resolution presented here was proposed by the IAU Inter-Division Working Group on Nominal Units for Stellar and Planetary Astronomy and passed by the XXIXth IAU General Assembly in Honolulu. IAU 2015 Resolution B3 adopts a set of nominal solar, terrestrial, and jovian conversion constants for stellar and (exo)planetary astronomy which are defined to be exact SI values. While the nominal constants are based on current best estimates (CBEs; which have uncertainties, are not secularly constant, and are updated regularly using new observations), they should be interpreted as standard values and not as CBEs. IAU 2015 Resolution B3 adopts five solar conversion constants (nominal solar radius, nominal total solar irradiance, nominal solar luminosity, nominal solar effective temperature, and nominal solar mass parameter) and six planetary conversion constants (nominal terrestrial equatorial radius, nominal terrestrial polar radius, nominal jovian equatorial radius, nominal jovian polar radius, nominal terrestrial mass parameter, and nominal jovian mass parameter).
49 citations
Posted Content•
[...]
TL;DR: This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software.
Abstract: The Astropy Project (http://astropy.org) is, in its own words, "a community
effort to develop a single core package for Astronomy in Python and foster
interoperability between Python astronomy packages." For five years this
project has been managed, written, and operated as a grassroots,
self-organized, almost entirely volunteer effort while the software is used by
the majority of the astronomical community. Despite this, the project has
always been and remains to this day effectively unfunded. Further, contributors
receive little or no formal recognition for creating and supporting what is now
critical software. This paper explores the problem in detail, outlines possible
solutions to correct this, and presents a few suggestions on how to address the
sustainability of general purpose astronomical software.
20 citations
Posted Content•
TL;DR: An overview of the Gammapy package and project is given and an analysis application example with simulated CTA data is shown, showing the simulation and analysis of observations from the Cherenkov Telescope Array.
Abstract: Gammapy is a Python package for high-level gamma-ray data analysis built on Numpy, Scipy and Astropy. It enables us to analyze gamma-ray data and to create sky images, spectra and lightcurves, from event lists and instrument response information, and to determine the position, morphology and spectra of gamma-ray sources. So far Gammapy has mostly been used to analyze data from H.E.S.S. and Fermi-LAT, and is now being used for the simulation and analysis of observations from the Cherenkov Telescope Array (CTA). We have proposed Gammapy as a prototype for the CTA science tools. This contribution gives an overview of the Gammapy package and project and shows an analysis application example with simulated CTA data.
8 citations