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      Starburst to Quiescent from HST/ALMA: Stars and Dust Unveil Minor Mergers in Submillimeter Galaxies at z ∼ 4.5

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          Stellar population synthesis at the resolution of 2003

          We present a new model for computing the spectral evolution of stellar populations at ages between 100,000 yr and 20 Gyr at a resolution of 3 A across the whole wavelength range from 3200 to 9500 A for a wide range of metallicities. These predictions are based on a newly available library of observed stellar spectra. We also compute the spectral evolution across a larger wavelength range, from 91 A to 160 micron, at lower resolution. The model incorporates recent progress in stellar evolution theory and an observationally motivated prescription for thermally-pulsing stars on the asymptotic giant branch. The latter is supported by observations of surface brightness fluctuations in nearby stellar populations. We show that this model reproduces well the observed optical and near-infrared colour-magnitude diagrams of Galactic star clusters of various ages and metallicities. Stochastic fluctuations in the numbers of stars in different evolutionary phases can account for the full range of observed integrated colours of star clusters in the Magellanic Clouds. The model reproduces in detail typical galaxy spectra from the Early Data Release (EDR) of the Sloan Digital Sky Survey (SDSS). We exemplify how this type of spectral fit can constrain physical parameters such as the star formation history, metallicity and dust content of galaxies. Our model is the first to enable accurate studies of absorption-line strengths in galaxies containing stars over the full range of ages. Using the highest-quality spectra of the SDSS EDR, we show that this model can reproduce simultaneously the observed strengths of those Lick indices that do not depend strongly on element abundance ratios [abridged].
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            Is Open Access

            Astropy: A Community Python Package for Astronomy

            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.
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              Star Formation in Galaxies Along the Hubble Sequence

              Observations of star formation rates (SFRs) in galaxies provide vital clues to the physical nature of the Hubble sequence, and are key probes of the evolutionary properties of galaxies. The focus of this review is on the broad patterns in the star formation properties of galaxies along the Hubble sequence, and their implications for understanding galaxy evolution and the physical processes that drive the evolution. Star formation in the disks and nuclear regions of galaxies are reviewed separately, then discussed within a common interpretive framework. The diagnostic methods used to measure SFRs are also reviewed, and a self-consistent set of SFR calibrations is presented as an aid to workers in the field.
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                Author and article information

                Journal
                The Astrophysical Journal
                ApJ
                American Astronomical Society
                1538-4357
                April 01 2018
                March 30 2018
                : 856
                : 2
                : 121
                Article
                10.3847/1538-4357/aab206
                395e3c7e-4895-40e0-9480-574c0d26222c
                © 2018

                http://iopscience.iop.org/info/page/text-and-data-mining

                http://iopscience.iop.org/page/copyright

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