scispace - formally typeset
Search or ask a question
Author

Tom Aldcroft

Bio: Tom Aldcroft is an academic researcher from Harvard University. The author has contributed to research in topics: Redshift & Active galactic nucleus. The author has an hindex of 25, co-authored 48 publications receiving 10966 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: Astropy as discussed by the authors is a Python package for astronomy-related functionality, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, 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.
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

9,720 citations

Journal ArticleDOI
TL;DR: Astropy as mentioned in this paper 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.
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.

1,944 citations

Journal ArticleDOI
TL;DR: The Chandra COSMOS Survey (C-COSMS) is a large, 1.8Ms, Chandra program that has imaged the central 0.5 deg^2 area with an effective exposure of ~160 ks as discussed by the authors.
Abstract: The Chandra COSMOS Survey (C-COSMOS) is a large, 1.8 Ms, Chandra program that has imaged the central 0.5 deg^2 of the COSMOS field (centered at 10 ^h , +02 ^o ) with an effective exposure of ~160 ks, and an outer 0.4 deg^2 area with an effective exposure of ~80 ks. The limiting source detection depths are 1.9 × 10^(–16) erg cm^(–2) s^(–1) in the soft (0.5-2 keV) band, 7.3 × 10^(–16) erg cm^(–2) s^(–1) in the hard (2-10 keV) band, and 5.7 × 10^(–16) erg cm^(–2) s^(–1) in the full (0.5-10 keV) band. Here we describe the strategy, design, and execution of the C-COSMOS survey, and present the catalog of 1761 point sources detected at a probability of being spurious of <2 × 10^(–5) (1655 in the full, 1340 in the soft, and 1017 in the hard bands). By using a grid of 36 heavily (~50%) overlapping pointing positions with the ACIS-I imager, a remarkably uniform (±12%) exposure across the inner 0.5 deg^2 field was obtained, leading to a sharply defined lower flux limit. The widely different point-spread functions obtained in each exposure at each point in the field required a novel source detection method, because of the overlapping tiling strategy, which is described in a companion paper. This method produced reliable sources down to a 7-12 counts, as verified by the resulting logN-logS curve, with subarcsecond positions, enabling optical and infrared identifications of virtually all sources, as reported in a second companion paper. The full catalog is described here in detail and is available online.

508 citations

Journal ArticleDOI
TL;DR: The COSMOS-Legacy survey as discussed by the authors is a 4.6Ms Chandra program that has imaged 2.2 deg2 of the COS-MOS field with an effective exposure of ≃ 160 ks over the central 1.5 deg^2 and ≃ 80 ks in the remaining area.
Abstract: The COSMOS-Legacy survey is a 4.6 Ms Chandra program that has imaged 2.2 deg2 of the COSMOS field with an effective exposure of ≃ 160 ks over the central 1.5 deg^2 and of ≃ 80 ks in the remaining area. The survey is the combination of 56 new observations obtained as an X-ray Visionary Project with the previous C-COSMOS survey. We describe the reduction and analysis of the new observations and the properties of 2273 point sources detected above a spurious probability of 2 × 10^(−5). We also present the updated properties of the C-COSMOS sources detected in the new data. The whole survey includes 4016 point sources (3814, 2920 and 2440 in the full, soft, and hard band). The limiting depths are 2.2 × 10^(−16), 1.5 × 10^(−15), and 8.9 × 10^(−16) erg cm^(-2)s^(-1) in the 0.5–2, 2–10, and 0.5–10 keV bands, respectively. The observed fraction of obscured active galactic nuclei with a column density >10^(22) cm^(−2) from the hardness ratio (HR) is 50_(-16)^(+17)%. Given the large sample we compute source number counts in the hard and soft bands, significantly reducing the uncertainties of 5%–10%. For the first time we compute number counts for obscured (HR > −0.2) and unobscured (HR < −0.2) sources and find significant differences between the two populations in the soft band. Due to the unprecedent large exposure, COSMOS-Legacy area is three times larger than surveys at similar depths and its depth is three times fainter than surveys covering similar areas. The area-flux region occupied by COSMOS-Legacy is likely to remain unsurpassed for years to come.

424 citations

Journal ArticleDOI
TL;DR: In this article, the authors report the final optical identifications of the medium-depth (~60 ks), contiguous (2 deg^2) XMM-Newton survey of the COSMOS field.
Abstract: We report the final optical identifications of the medium-depth (~60 ks), contiguous (2 deg^2) XMM-Newton survey of the COSMOS field. XMM-Newton has detected ~1800 X-ray sources down to limiting fluxes of ~5 × 10^(–16), ~3 × 10^(–15), and ~7 × 10^(–15) erg cm^(–2) s^(–1) in the 0.5-2 keV, 2-10 keV, and 5-10 keV bands, respectively (~1 × 10^(–15), ~6 × 10^(–15), and ~1 × 10^(–14) erg cm^(–2) s^(–1), in the three bands, respectively, over 50% of the area). The work is complemented by an extensive collection of multiwavelength data from 24 μm to UV, available from the COSMOS survey, for each of the X-ray sources, including spectroscopic redshifts for ≳50% of the sample, and high-quality photometric redshifts for the rest. The XMM and multiwavelength flux limits are well matched: 1760 (98%) of the X-ray sources have optical counterparts, 1711 (~95%) have IRAC counterparts, and 1394 (~78%) have MIPS 24 μm detections. Thanks to the redshift completeness (almost 100%) we were able to constrain the high-luminosity tail of the X-ray luminosity function confirming that the peak of the number density of log L_X > 44.5 active galactic nuclei (AGNs) is at z ~ 2. Spectroscopically identified obscured and unobscured AGNs, as well as normal and star-forming galaxies, present well-defined optical and infrared properties. We devised a robust method to identify a sample of ~150 high-redshift (z > 1), obscured AGN candidates for which optical spectroscopy is not available. We were able to determine that the fraction of the obscured AGN population at the highest (L_X > 10^(44) erg s^(–1)) X-ray luminosity is ~15%-30% when selection effects are taken into account, providing an important observational constraint for X-ray background synthesis. We studied in detail the optical spectrum and the overall spectral energy distribution of a prototypical Type 2 QSO, caught in a stage transitioning from being starburst dominated to AGN dominated, which was possible to isolate only thanks to the combination of X-ray and infrared observations.

380 citations


Cited by
More filters
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: How a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data is reviewed.
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.

4,342 citations

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
TL;DR: 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.
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.

2,738 citations