SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Context. 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. Aims: 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. Methods: The raw data collected with the Gaia instruments during the first 22 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into this second data release, which represents a major advance with respect to Gaia DR1 in terms of completeness, performance, and richness of the data products. 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 GBP (330-680 nm) and GRP (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 14 000 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. Conclusions: Gaia DR2 represents a major achievement for the Gaia mission, delivering on the long standing promise to provide parallaxes and proper motions for over 1 billion stars, and representing a first step in the availability of complementary radial velocity and source astrophysical information for a sample of stars in the Gaia survey which covers a very substantial fraction of the volume of our galaxy.

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Gaia Data Release 2. Summary of the contents and survey properties

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.

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Array programming with NumPy

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.

/pdf/scipy-1-0-fundamental-algorithms-for-scientific-computing-in-45othhotbu.pdf

SciPy 1.0: fundamental algorithms for scientific computing in Python.

Many problems in the experimental estimation of parameters for models can be solved through use of the likelihood ratio test. Applications of the likelihood ratio, with particular attention to photon counting experiments, are discussed. The procedures presented solve a greater range of problems than those currently in use, yet are no more difficult to apply. The procedures are proved analytically, and examples from current problems in astronomy are discussed.

Parameter estimation in astronomy through application of the likelihood ratio. [satellite data analysis techniques

$K2$ greatly extended $Kepler$'s ability to find new planets, but it was typically limited to identifying transiting planets with orbital periods below 40 days. While analyzing $K2$ data through the Exoplanet Explorers project, citizen scientists helped discover one super-Earth and four sub-Neptune sized planets in the relatively bright ($V=12.21$, $K=10.3$) K2-138 system, all which orbit near 3:2 mean motion resonances. The $K2$ light curve showed two additional transit events consistent with a sixth planet. Using $Spitzer$ photometry, we validate the sixth planet's orbital period of $41.966\pm0.006$ days and measure a radius of $3.44^{+0.32}_{-0.31}\,R_{\oplus}$, solidifying K2-138 as the $K2$ system with the most currently known planets. There is a sizeable gap between the outer two planets, since the fifth planet in the system, K2-138 f, orbits at 12.76 days. We explore the possibility of additional non-transiting planets in the gap between f and g. Due to the relative brightness of the K2-138 host star, and the near resonance of the inner planets, K2-138 could be a key benchmark system for both radial velocity and transit timing variation mass measurements, and indeed radial velocity masses for the inner four planets have already been obtained. With its five sub-Neptunes and one super-Earth, the K2-138 system provides a unique test bed for comparative atmospheric studies of warm to temperate planets of similar size, dynamical studies of near resonant planets, and models of planet formation and migration.

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K2-138 g: Spitzer Spots a Sixth Planet for the Citizen Science System

The INT Galactic Plane Survey (IGAPS) is the merger of the optical photometric surveys, IPHAS and UVEX, based on data from the Isaac Newton Telescope (INT) obtained between 2003 and 2018. These capture the entire northern Galactic plane within the Galactic coordinate range, -5<b<+5 deg. and 30<l<215 deg. From the beginning, the incorporation of narrowband H-alpha imaging has been a unique and distinctive feature of this effort. Alongside a focused discussion of the nature and application of the H-alpha data, we present the IGAPS world-accessible database of images for all 5 survey filters, i, r, g, U-RGO and narrowband H-alpha, observed on a pixel scale of 0.33 arcsec and at an effective (median) angular resolution of 1.1 to 1.3 arcsec. The background, noise, and sensitivity characteristics of the narrowband H-alpha filter images are outlined. Typical noise levels in this band correspond to a surface brightness at full one-arcsec resolution of around 2e-16 erg/cm2/s/arcsec2. Illustrative applications of the H-alpha data to planetary nebulae and Herbig-Haro objects are outlined and, as part of a discussion of mosaicking technique, we present a very large background-subtracted narrowband mosaic of the supernova remnant, Simeis 147. Finally we lay out a method that exploits the database via an automated selection of bright ionized diffuse interstellar emission targets for the coming generation of wide-field massive-multiplex spectrographs. Two examples of the diffuse H-alpha maps output from this selection process are presented and compared with previously published data.

High resolution H-alpha imaging of the Northern Galactic Plane, and the IGAPS images database

Kepler, K2, and Transiting Exoplanet Survey Satellite (TESS) have demonstrated the power of high-precision uninterrupted photometry. Combining data sets from these two observatories can extend the baseline of high precision photometry on a target. In addition, for some investigations, the differing passbands of the two telescopes may provide additional information about the causes of the observed variability. In order to facilitate studies combining these unique data sets, we present a list of all the 55,641 targets observed by K2 which fell within the TESS field of view during its first three years of observations.

Geert Barentsen

Papers

K2-138 g: Spitzer Spots a Sixth Planet for the Citizen Science System

High resolution H-alpha imaging of the Northern Galactic Plane, and the IGAPS images database

K2 Targets Observed in TESS Cycles 1-3