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Geert Barentsen

Bio: Geert Barentsen is an academic researcher from Ames Research Center. The author has contributed to research in topics: Exoplanet & Planet. The author has an hindex of 30, co-authored 141 publications receiving 8696 citations. Previous affiliations of Geert Barentsen include Max Planck Society & University of Hertfordshire.


Papers
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Journal ArticleDOI
Adrian M. Price-Whelan1, B. M. Sipőcz1, Hans Moritz Günther1, P. L. Lim1, Steven M. Crawford1, S. Conseil1, D. L. Shupe1, M. W. Craig1, N. Dencheva1, Adam Ginsburg1, Jacob T VanderPlas1, Larry Bradley1, David Pérez-Suárez1, M. de Val-Borro1, T. L. Aldcroft1, Kelle L. Cruz1, Thomas P. Robitaille1, E. J. Tollerud1, C. Ardelean1, Tomáš Babej1, Y. P. Bach1, Matteo Bachetti1, A. V. Bakanov1, Steven P. Bamford1, Geert Barentsen1, Pauline Barmby1, Andreas Baumbach1, Katherine Berry1, F. Biscani1, Médéric Boquien1, K. A. Bostroem1, L. G. Bouma1, G. B. Brammer1, E. M. Bray1, H. Breytenbach1, H. Buddelmeijer1, D. J. Burke1, G. Calderone1, J. L. Cano Rodríguez1, Mihai Cara1, José Vinícius de Miranda Cardoso1, S. Cheedella1, Y. Copin1, Lia Corrales1, Devin Crichton1, D. DÁvella1, Christoph Deil1, É. Depagne1, J. P. Dietrich1, Axel Donath1, M. Droettboom1, Nicholas Earl1, T. Erben1, Sebastien Fabbro1, Leonardo Ferreira1, T. Finethy1, R. T. Fox1, Lehman H. Garrison1, S. L. J. Gibbons1, Daniel A. Goldstein1, Ralf Gommers1, Johnny P. Greco1, P. Greenfield1, A. M. Groener1, Frédéric Grollier1, A. Hagen1, P. Hirst1, Derek Homeier1, Anthony Horton1, Griffin Hosseinzadeh1, L. Hu1, J. S. Hunkeler1, Ž. Ivezić1, A. Jain1, T. Jenness1, G. Kanarek1, Sarah Kendrew1, Nicholas S. Kern1, Wolfgang Kerzendorf1, A. Khvalko1, J. King1, D. Kirkby1, A. M. Kulkarni1, Ashok Kumar1, Antony Lee1, D. Lenz1, S. P. Littlefair1, Zhiyuan Ma1, D. M. Macleod1, M. Mastropietro1, C. McCully1, S. Montagnac1, Brett M. Morris1, M. Mueller1, Stuart Mumford1, D. Muna1, Nicholas A. Murphy1, Stefan Nelson1, G. H. Nguyen1, Joe Philip Ninan1, M. Nöthe1, S. Ogaz1, Seog Oh1, J. K. Parejko1, N. R. Parley1, Sergio Pascual1, R. Patil1, A. A. Patil1, A. L. Plunkett1, Jason X. Prochaska1, T. Rastogi1, V. Reddy Janga1, J. Sabater1, Parikshit Sakurikar1, Michael Seifert1, L. E. Sherbert1, H. Sherwood-Taylor1, A. Y. Shih1, J. Sick1, M. T. Silbiger1, Sudheesh Singanamalla1, Leo Singer1, P. H. Sladen1, K. A. Sooley1, S. Sornarajah1, Ole Streicher1, P. Teuben1, Scott Thomas1, Grant R. Tremblay1, J. Turner1, V. Terrón1, M. H. van Kerkwijk1, A. de la Vega1, Laura L. Watkins1, B. A. Weaver1, J. Whitmore1, Julien Woillez1, Victor Zabalza1, Astropy Contributors1 
TL;DR: The Astropy project as discussed by the authors is a Python project supporting the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community, including the core package astropy.
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.

4,044 citations

Journal ArticleDOI
Adrian M. Price-Whelan, Brigitta Sipőcz, Hans Moritz Günther, P. L. Lim, Steven M. Crawford, Simon Conseil, David L. Shupe, Matt Craig, N. Dencheva, Adam Ginsburg, Jacob T VanderPlas, Larry Bradley, David Pérez-Suárez, M. de Val-Borro, T. L. Aldcroft, Kelle L. Cruz, Thomas P. Robitaille, Erik J. Tollerud, C. Ardelean, Tomáš Babej, Matteo Bachetti, A. V. Bakanov, Steven P. Bamford, Geert Barentsen, Pauline Barmby, Andreas Baumbach, Katherine Berry, F. Biscani, Médéric Boquien, K. A. Bostroem, L. G. Bouma, G. B. Brammer, Erik Bray, H. Breytenbach, H. Buddelmeijer, Douglas Burke, G. Calderone, J. L. Cano Rodríguez, Mihai Cara, José Vinícius de Miranda Cardoso, S. Cheedella, Y. Copin, Devin Crichton, D. DÁvella, Christoph Deil, Éric Depagne, J. P. Dietrich, Axel Donath, Michael Droettboom, Nicholas Earl, T. Erben, Sebastien Fabbro, Leonardo Ferreira, T. Finethy, R. T. Fox, Lehman H. Garrison, S. L. J. Gibbons, Daniel A. Goldstein, Ralf Gommers, Johnny P. Greco, Perry Greenfield, A. M. Groener, Frédéric Grollier, Alex Hagen, Paul Hirst, Derek Homeier, Anthony Horton, Griffin Hosseinzadeh, L. Hu, J. S. Hunkeler, Željko Ivezić, A. Jain, Tim Jenness, G. Kanarek, Sarah Kendrew, Nicholas S. Kern, Wolfgang Kerzendorf, A. Khvalko, J. King, D. Kirkby, A. M. Kulkarni, Ashok Kumar, Antony Lee, D. Lenz, S. P. Littlefair, Zhiyuan Ma, D. M. Macleod, M. Mastropietro, C. McCully, S. Montagnac, Brett M. Morris, Michael Mueller, Stuart Mumford, Demitri Muna, Nicholas A. Murphy, Stefan Nelson, G. H. Nguyen, Joe Philip Ninan, M. Nöthe, S. Ogaz, Seog Oh, John K. Parejko, N. R. Parley, Sergio Pascual, R. Patil, A. A. Patil, A. L. Plunkett, Jason X. Prochaska, T. Rastogi, V. Reddy Janga, Josep Sabater, Parikshit Sakurikar, Michael Seifert, L. E. Sherbert, H. Sherwood-Taylor, A. Y. Shih, J. Sick, M. T. Silbiger, Sudheesh Singanamalla, Leo Singer, P. H. Sladen, K. A. Sooley, S. Sornarajah, Ole Streicher, Peter Teuben, Scott Thomas, Grant R. Tremblay, J. Turner, V. Terrón, M. H. van Kerkwijk, A. de la Vega, Laura L. Watkins, B. A. Weaver, J. Whitmore, Julien Woillez, Victor Zabalza 
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.

2,286 citations

Journal ArticleDOI
Susan E. Thompson1, Susan E. Thompson2, Susan E. Thompson3, Jeffrey L. Coughlin1, Jeffrey L. Coughlin3, K. Hoffman1, Fergal Mullally3, Fergal Mullally1, Jessie L. Christiansen4, Christopher J. Burke1, Christopher J. Burke5, Christopher J. Burke3, Steve Bryson3, Natalie M. Batalha3, Michael R. Haas3, Joseph Catanzarite3, Joseph Catanzarite1, Jason F. Rowe6, Geert Barentsen, Douglas A. Caldwell1, Douglas A. Caldwell3, Bruce D. Clarke1, Bruce D. Clarke3, Jon M. Jenkins3, Jie Li1, David W. Latham7, Jack J. Lissauer3, Savita Mathur8, Robert L. Morris1, Robert L. Morris3, Shawn Seader, Jeffrey C. Smith1, Jeffrey C. Smith3, Todd C. Klaus3, Joseph D. Twicken3, Joseph D. Twicken1, Jeffrey Van Cleve1, B. Wohler1, B. Wohler3, Rachel Akeson4, David R. Ciardi4, William D. Cochran9, Christopher E. Henze3, Steve B. Howell3, Daniel Huber, Andrej Prsa10, Solange V. Ramirez4, Timothy D. Morton11, Thomas Barclay12, Jennifer R. Campbell3, William J. Chaplin13, William J. Chaplin14, David Charbonneau7, Jørgen Christensen-Dalsgaard13, Jessie L. Dotson3, Laurance R. Doyle1, Laurance R. Doyle15, Edward W. Dunham16, Andrea K. Dupree7, Eric B. Ford, John C. Geary7, Forrest R. Girouard3, Howard Isaacson17, Hans Kjeldsen13, Elisa V. Quintana12, Darin Ragozzine18, Megan Shabram3, Avi Shporer4, Victor Silva Aguirre13, Jason H. Steffen19, Martin Still, Peter Tenenbaum1, Peter Tenenbaum3, William F. Welsh20, Angie Wolfgang21, Khadeejah A. Zamudio3, David G. Koch3, William J. Borucki3 
TL;DR: The Robovetter and the metrics it uses to decide which TCEs are called planet candidates in the DR25 KOI catalog are discussed and a value called the disposition score is discussed which provides an easy way to select a more reliable, albeit less complete, sample of candidates.
Abstract: We present the Kepler Object of Interest (KOI) catalog of transiting exoplanets based on searching 4 yr of Kepler time series photometry (Data Release 25, Q1–Q17). The catalog contains 8054 KOIs, of which 4034 are planet candidates with periods between 0.25 and 632 days. Of these candidates, 219 are new, including two in multiplanet systems (KOI-82.06 and KOI-2926.05) and 10 high-reliability, terrestrial-size, habitable zone candidates. This catalog was created using a tool called the Robovetter, which automatically vets the DR25 threshold crossing events (TCEs). The Robovetter also vetted simulated data sets and measured how well it was able to separate TCEs caused by noise from those caused by low signal-to-noise transits. We discuss the Robovetter and the metrics it uses to sort TCEs. For orbital periods less than 100 days the Robovetter completeness (the fraction of simulated transits that are determined to be planet candidates) across all observed stars is greater than 85%. For the same period range, the catalog reliability (the fraction of candidates that are not due to instrumental or stellar noise) is greater than 98%. However, for low signal-to-noise candidates between 200 and 500 days around FGK-dwarf stars, the Robovetter is 76.7% complete and the catalog is 50.5% reliable. The KOI catalog, the transit fits, and all of the simulated data used to characterize this catalog are available at the NASA Exoplanet Archive.

356 citations

Journal ArticleDOI
TL;DR: The Ecliptic Plane Input Catalog (EPIC) as mentioned in this paper provides coordinates, photometry and kinematics based on a federation of all-sky catalogs to support target selection and target management for the K2 mission.
Abstract: The K2 Mission uses the Kepler spacecraft to obtain high-precision photometry over ~80 day campaigns in the ecliptic plane. The Ecliptic Plane Input Catalog (EPIC) provides coordinates, photometry and kinematics based on a federation of all-sky catalogs to support target selection and target management for the K2 mission. We describe the construction of the EPIC, as well as modifications and shortcomings of the catalog. Kepler magnitudes (Kp) are shown to be accurate to ~0.1 mag for the Kepler field, and the EPIC is typically complete to Kp~17 (Kp~19 for campaigns covered by SDSS). We furthermore classify 138,600 targets in Campaigns 1-8 (~88% of the full target sample) using colors, proper motions, spectroscopy, parallaxes, and galactic population synthesis models, with typical uncertainties for G-type stars of ~3% in Teff, ~0.3 dex in log(g), ~40% in radius, ~10% in mass, and ~40% in distance. Our results show that stars targeted by K2 are dominated by K-M dwarfs (~41% of all selected targets), F-G dwarfs (~36%) and K giants (~21%), consistent with key K2 science programs to search for transiting exoplanets and galactic archeology studies using oscillating red giants. However, we find a significant variation of the fraction of cool dwarfs with galactic latitude, indicating a target selection bias due to interstellar reddening and the increased contamination by giant stars near the galactic plane. We discuss possible systematic errors in the derived stellar properties, and differences to published classifications for K2 exoplanet host stars. The EPIC is hosted at the Mikulski Archive for Space Telescopes (MAST): this http URL.

321 citations


Cited by
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Journal ArticleDOI
TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which 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.
Abstract: 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.

12,774 citations

Journal ArticleDOI
TL;DR: The second Gaia data release, Gaia DR2 as mentioned in this paper, is a major advance with respect to Gaia DR1 in terms of completeness, performance, and richness of the data products.
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.

8,308 citations

Journal ArticleDOI
16 Sep 2020-Nature
TL;DR: In this paper, the authors review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data, and their evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
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.

7,624 citations

Journal ArticleDOI
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

15 Mar 1979
TL;DR: In this article, the experimental estimation of parameters for models can be solved through use of the likelihood ratio test, with particular attention to photon counting experiments, and procedures presented solve a greater range of problems than those currently in use, yet are no more difficult to apply.
Abstract: 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.

1,748 citations