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- Vallenari, A., et al.
(författare)
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Gaia Data Release 3: Summary of the content and survey properties
- 2023
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Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 674
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Tidskriftsartikel (refereegranskat)abstract
- Context. We present the third data release of the European Space Agency's Gaia mission, Gaia DR3. This release includes a large variety of new data products, notably a much expanded radial velocity survey and a very extensive astrophysical characterisation of Gaia sources.Aims. We outline the content and the properties of Gaia DR3, providing an overview of the main improvements in the data processing in comparison with previous data releases (where applicable) and a brief discussion of the limitations of the data in this release.Methods. The Gaia DR3 catalogue is the outcome of the processing of raw data collected with the Gaia instruments during the first 34 months of the mission by the Gaia Data Processing and Analysis Consortium.Results. The Gaia DR3 catalogue contains the same source list, celestial positions, proper motions, parallaxes, and broad band photometry in the G, GBP, and G(RP) pass-bands already present in the Early Third Data Release, Gaia EDR3. Gaia DR3 introduces an impressive wealth of new data products. More than 33 million objects in the ranges G(RVS) < 14 and 3100 < T-eff < 14 500, have new determinations of their mean radial velocities based on data collected by Gaia. We provide GRVS magnitudes for most sources with radial velocities, and a line broadening parameter is listed for a subset of these. Mean Gaia spectra are made available to the community. The Gaia DR3 catalogue includes about 1 million mean spectra from the radial velocity spectrometer, and about 220 million low-resolution blue and red prism photometer BP /RP mean spectra. The results of the analysis of epoch photometry are provided for some 10 million sources across 24 variability types. Gaia DR3 includes astrophysical parameters and source class probabilities for about 470 million and 1500 million sources, respectively, including stars, galaxies, and quasars. Orbital elements and trend parameters are provided for some 800 000 astrometric, spectroscopic and eclipsing binaries. More than 150 000 Solar System objects, including new discoveries, with preliminary orbital solutions and individual epoch observations are part of this release. Reflectance spectra derived from the epoch BP /RP spectral data are published for about 60 000 asteroids. Finally, an additional data set is provided, namely the Gaia Andromeda Photometric Survey, consisting of the photometric time series for all sources located in a 5:5 degree radius field centred on the Andromeda galaxy.Conclusions. This data release represents a major advance with respect to Gaia DR2 and Gaia EDR3 because of the unprecedented quantity, quality, and variety of source astrophysical data. To date this is the largest collection of all-sky spectrophotometry, radial velocities, variables, and astrophysical parameters derived from both low- and high-resolution spectra and includes a spectrophotometric and dynamical survey of SSOs of the highest accuracy. The non-single star content surpasses the existing data by orders of magnitude. The quasar host and galaxy light profile collection is the first such survey that is all sky and space based. The astrophysical information provided in Gaia DR3 will unleash the full potential of Gaia's exquisite astrometric, photometric, and radial velocity surveys.
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| 5. |
- deSouza, Nandita M., et al.
(författare)
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Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)
- 2019
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Ingår i: Insights into Imaging. - : SPRINGEROPEN. - 1869-4101. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.
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