K. Azalee Bostroem

Bio: K. Azalee Bostroem is an academic researcher from Space Telescope Science Institute. The author has contributed to research in topics: Physics & Supernova. The author has an hindex of 5, co-authored 17 publications receiving 7352 citations.

Astropy: A community Python package for astronomy

Abstract: We present the first public version (v02) of the open-source and community-developed Python package, Astropy This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions Significant functionality is under activedevelopment, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions

Astropy: A Community Python Package for Astronomy

Abstract: We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as Flexible Image Transport System (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions. Significant functionality is under active development, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools. The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions.

Type Ia Supernova Rate Measurements to Redshift 2.5 from CANDELS : Searching for Prompt Explosions in the Early Universe

Abstract: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) was a multi-cycle treasury program on the Hubble Space Telescope (HST) that surveyed a total area of ~0.25 deg^2 with ~900 HST orbits spread across 5 fields over 3 years. Within these survey images we discovered 65 supernovae (SN) of all types, out to z~2.5. We classify ~24 of these as Type Ia SN (SN Ia) based on host-galaxy redshifts and SN photometry (supplemented by grism spectroscopy of 6 SN). Here we present a measurement of the volumetric SN Ia rate as a function of redshift, reaching for the first time beyond z=2 and putting new constraints on SN Ia progenitor models. Our highest redshift bin includes detections of SN that exploded when the universe was only ~3 Gyr old and near the peak of the cosmic star-formation history. This gives the CANDELS high-redshift sample unique leverage for evaluating the fraction of SN Ia that explode promptly after formation ( 40 Myr. However, a mild tension is apparent between ground-based low-z surveys and space-based high-z surveys. In both CANDELS and the sister HST program CLASH, we find a low rate of SN Ia at z>1. This could be a hint that prompt progenitors are in fact relatively rare, accounting for only ~20% of all SN Ia explosions -- though further analysis and larger samples will be needed to examine that suggestion.

The Type II-P Supernova 2017eaw: from explosion to the nebular phase

Abstract: The nearby SN 2017eaw is a Type II-P (``plateau') supernova showing early-time, moderate CSM interaction. We present a comprehensive study of this SN including the analysis of high-quality optical photometry and spectroscopy covering the very early epochs up to the nebular phase, as well as near-UV and near-infrared spectra, and early-time X-ray and radio data. The combined data of SNe 2017eaw and 2004et allow us to get an improved distance to the host galaxy, NGC 6946, as $D \sim 6.85$ $\pm 0.63$ Mpc; this fits in recent independent results on the distance of the host and disfavors the previously derived (30% shorter) distances based on SN 2004et. From modeling the nebular spectra and the quasi-bolometric light curve, we estimate the progenitor mass and some basic physical parameters for the explosion and the ejecta. Our results agree well with previous reports on a RSG progenitor star with a mass of $\sim15-16$ M$_\odot$. Our estimation on the pre-explosion mass-loss rate ($\dot{M} \sim3 \times 10^{-7} -$ $1\times 10^{-6} M_{\odot}$ yr$^{-1}$) agrees well with previous results based on the opacity of the dust shell enshrouding the progenitor, but it is orders of magnitude lower than previous estimates based on general light-curve modeling of Type II-P SNe. Combining late-time optical and mid-infrared data, a clear excess at 4.5 $\mu$m can be seen, supporting the previous statements on the (moderate) dust formation in the vicinity of SN 2017eaw.

The slow demise of the long-lived SN 2005ip

Abstract: The Type IIn supernova (SN) 2005ip is one of the most well-studied and long-lasting examples of a SN interacting with its circumstellar environment. The optical light curve plateaued at a nearly constant level for more than five years, suggesting ongoing shock interaction with an extended and clumpy circumstellar medium (CSM). Here we present continued observations of the SN from $\sim 1000-5000$ days post-explosion at all wavelengths, including X-ray, ultraviolet, near-infrared, and mid-infrared. The UV spectra probe the pre-explosion mass loss and show evidence for CNO processing. From the bolometric light curve, we find that the total radiated energy is in excess of $10^{50}$ erg, the progenitor star's pre-explosion mass-loss rate was $\gtrsim 1 \times 10^{-2}\,{\rm M_{\odot}~ yr}^{-1}$, and the total mass lost shortly before explosion was $\gtrsim 1\,{\rm M_\odot}$, though the mass lost could have been considerably larger depending on the efficiency for the conversion of kinetic energy to radiation. The ultraviolet through near-infrared spectrum is characterised by two high density components, one with narrow high-ionisation lines, and one with broader low-ionisation H I, He I, [O I], Mg II, and Fe II lines. The rich Fe II spectrum is strongly affected by Ly$\alpha$ fluorescence, consistent with spectral modeling. Both the Balmer and He I lines indicate a decreasing CSM density during the late interaction period. We find similarities to SN 1988Z, which shows a comparable change in spectrum at around the same time during its very slow decline. These results suggest that, at long last, the shock interaction in SN 2005ip may finally be on the decline.

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

Abstract: 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.

Array programming with NumPy

Abstract: 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.

Array Programming with NumPy

Abstract: Array programming provides a powerful, compact, 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 plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material science, engineering, finance, and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves and the first imaging of a black hole. Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Because of its central position in the ecosystem, NumPy increasingly plays the role of an interoperability layer between these new array computation libraries.

The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package

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 interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project.

LSST: from Science Drivers to Reference Design and Anticipated Data Products

Abstract: (Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pachon in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.