| 1. |
- van Leeuwen, F., et al.
(författare)
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Gaia Data Release 1 : Open cluster astrometry: Performance, limitations, and future prospects
- 2017
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 601
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Tidskriftsartikel (refereegranskat)abstract
- Context. The first Gaia Data Release contains the Tycho-Gaia Astrometric Solution (TGAS). This is a subset of about 2 million stars for which, besides the position and photometry, the proper motion and parallax are calculated using Hipparcos and Tycho-2 positions in 1991.25 as prior information. Aims. We investigate the scientific potential and limitations of the TGAS component by means of the astrometric data for open clusters. Methods. Mean cluster parallax and proper motion values are derived taking into account the error correlations within the astrometric solutions for individual stars, an estimate of the internal velocity dispersion in the cluster, and, where relevant, the effects of the depth of the cluster along the line of sight. Internal consistency of the TGAS data is assessed. Results. Values given for standard uncertainties are still inaccurate and may lead to unrealistic unit-weight standard deviations of least squares solutions for cluster parameters. Reconstructed mean cluster parallax and proper motion values are generally in very good agreement with earlier Hipparcos-based determination, although the Gaia mean parallax for the Pleiades is a significant exception. We have no current explanation for that discrepancy. Most clusters are observed to extend to nearly 15 pc from the cluster centre, and it will be up to future Gaia releases to establish whether those potential cluster-member stars are still dynamically bound to the clusters. Conclusions. The Gaia DR1 provides the means to examine open clusters far beyond their more easily visible cores, and can provide membership assessments based on proper motions and parallaxes. A combined HR diagram shows the same features as observed before using the Hipparcos data, with clearly increased luminosities for older A and F dwarfs.
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| 2. |
- Gaze, William H, et al.
(författare)
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Influence of humans on evolution and mobilization of environmental antibiotic resistome.
- 2013
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Ingår i: Emerging infectious diseases. - : Centers for Disease Control and Prevention (CDC). - 1080-6059 .- 1080-6040. ; 19:7
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Forskningsöversikt (refereegranskat)abstract
- The clinical failure of antimicrobial drugs that were previously effective in controlling infectious disease is a tragedy of increasing magnitude that gravely affects human health. This resistance by pathogens is often the endpoint of an evolutionary process that began billions of years ago in non-disease-causing microorganisms. This environmental resistome, its mobilization, and the conditions that facilitate its entry into human pathogens are at the heart of the current public health crisis in antibiotic resistance. Understanding the origins, evolution, and mechanisms of transfer of resistance elements is vital to our ability to adequately address this public health issue.
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| 3. |
- de Souza, R. S., et al.
(författare)
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A graph-based spectral classification of Type II supernovae
- 2023
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Ingår i: Astronomy and computing. - 2213-1337. ; 44
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Tidskriftsartikel (refereegranskat)abstract
- Given the ever-increasing number of time-domain astronomical surveys, employing robust, interpretative, and automated data-driven classification schemes is pivotal. Based on graph theory, we present new data-driven classification heuristics for spectral data. A spectral classification scheme of Type II supernovae (SNe II) is proposed based on the phase relative to the maximum light in the V band and the end of the plateau phase. We utilize a compiled optical data set that comprises 145 SNe and 1595 optical spectra in 4000-9000 angstrom. Our classification method naturally identifies outliers and arranges the different SNe in terms of their major spectral features. We compare our approach to the off-the-shelf UMAP manifold learning and show that both strategies are consistent with a continuous variation of spectral types rather than discrete families. The automated classification naturally reflects the fast evolution of Type II SNe around the maximum light while showcasing their homogeneity close to the end of the plateau phase. The scheme we develop could be more widely applicable to unsupervised time series classification or characterization of other functional data.
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