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

Different authors have produced models for the physical properties of meteoroids based on the shape of a meteor's light curve, typically from short observing campaigns. We here analyze the height profiles and light curves of approx.200 double-station meteors from the Leonids and Perseids using data from the Virtual Meteor Observatory, to demonstrate that with this web-based meteor database it is possible to analyze very large datasets from different authors in a consistent way. We compute the average heights for begin point, maximum luminosity, and end heights for Perseids and Leonids. We also compute the skew of the light curve, usually called the F-parameter. The results compare well with other author's data. We display the average light curve in a novel way to assess the light curve shape in addition to using the F-parameter. While the Perseids show a peaked light curve, the average Leonid light curve has a more flat peak. This indicates that the particle distribution of Leonid meteors can be described by a Gaussian distribution; the Perseids can be described with a power law. The skew for Leonids is smaller than for Perseids, indicating that the Leonids are more fragile than the Perseids.

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Constraining the Physical Properties of Meteor Stream Particles by Light Curve Shapes Using the Virtual Meteor Observatory

These Data Release Notes provide information specific to the current reprocessing and re-export of the Q0-Q17 data. The data products included in this data release include target pixel files, light curve files, FFIs,CBVs, ARP, Background, and Collateral files. This release marks the final processing of the Kepler Mission Data. See Tables 1 and 2 for a list of the reprocessed Kepler cadence data. See Table 3 for a list of the available FFIs. The Long Cadence Data, Short Cadence Data, and FFI data are documented in these data release notes. The ancillary files (i.e., cotrending basis vectors, artifact removal pixels, background, and collateral data) are described in the Archive Manual (Thompson et al., 2016).

Kepler Data Release 25 Notes (Q0-Q17)

A Transiting Hot Jupiter Candidate toward the Galactic Center Identified in the Kepler/K2 Campaign 9 Microlensing Survey

NASA's Kepler, K2 and TESS missions employ Simple Aperture Photometry (SAP) to derive time-series photometry, where an aperture is estimated for each star, and pixels containing each star are summed to create a single light curve. This method is simple, but in crowded fields the derived time-series can be highly contaminated. The alternate method of fitting a Point Spread Function (PSF) to the data is able to account for crowding, but is computationally expensive. In this paper, we present a new approach to extracting photometry from these time-series missions, which fits the PSF directly, but makes simplifying assumptions in order to greatly reduce the computation expense. Our method fixes the scene of the field in each image, estimates the PSF shape of the instrument with a linear model, and allows only source flux and position to vary. We demonstrate that our method is able to separate the photometry from blended targets in the Kepler dataset that are separated by less than a pixel. Our method is fast to compute, and fully accounts for uncertainties from degeneracies due to crowded fields. We name the method described in this work Linearized Field Deblending (LFD). We demonstrate our method on the false positive Kepler target \koi. We are able to separate the photometry of the two sources in the data, and demonstrate the contaminating transiting signal is consistent with a small, sub-stellar companion with a radius of $2.67R_{jup}$ ($0.27R_{sol}$). Our method is equally applicable to extracting photometry from NASA's TESS mission.

Linearized Field Deblending: PSF Photometry for Impatient Astronomers

Space observatories have provided unprecedented depictions of the many variability behaviors typical of low-mass, young stars However, those studies have so far largely omitted more massive objects ($\sim$2 $M_\odot$ to 4-5 $M_\odot$), and were limited by the absence of simultaneous, multi-wavelength information We present a new study of young star variability in the $\sim$1-2 Myr-old, massive Lagoon Nebula region Our sample encompasses 278 young, late-B to K-type stars, monitored with Kepler/K2 Auxiliary $u,g,r,i,H\alpha$ time series photometry, simultaneous with K2, was acquired at the Paranal Observatory We employed this comprehensive dataset and archival infrared photometry to determine individual stellar parameters, assess the presence of circumstellar disks, and tie the variability behaviors to inner disk dynamics We found significant mass-dependent trends in variability properties, with B/A stars displaying substantially reduced levels of variability compared to G/K stars for any light curve morphology These properties suggest different magnetic field structures at the surface of early-type and later-type stars We also detected a dearth of some disk-driven variability behaviors, particularly dippers, among stars earlier than G This indicates that their higher surface temperatures and more chaotic magnetic fields prevent the formation and survival of inner disk dust structures co-rotating with the star Finally, we examined the characteristic variability timescales within each light curve, and determined that the day-to-week timescales are predominant over the K2 time series These reflect distinct processes and locations in the inner disk environment, from intense accretion triggered by instabilities in the innermost disk regions, to variable accretion efficiency in the outer magnetosphere

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Geert Barentsen

Papers

Constraining the Physical Properties of Meteor Stream Particles by Light Curve Shapes Using the Virtual Meteor Observatory

Kepler Data Release 25 Notes (Q0-Q17)

A Transiting Hot Jupiter Candidate toward the Galactic Center Identified in the Kepler/K2 Campaign 9 Microlensing Survey

Linearized Field Deblending: PSF Photometry for Impatient Astronomers

Multicolor Variability of Young Stars in the Lagoon Nebula: Driving Causes and Intrinsic Timescales