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typos in usecases.tex
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doc/usecases.tex

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The main purpose of MANGO is to add an higher description level to the tabular data of query responses.
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MANGO is not designed to replace the meta-data already present in query responses, but on the contrary,
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to provide a model-aware layer with structured classes to interpret them and exploit them in client applications.
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The main purpose of MANGO is to add a higher description level to the tabular data of query responses.
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MANGO is not designed to replace the metadata already present in query responses, but on the contrary,
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to provide a model-aware layer with structured classes to interpret and to exploit them in client applications.
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Uses-cases have been collected since 2019 from representatives of various astronomical
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missions, archive designers and tools developers.
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missions, archive designers, and tools developers.
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The call for contribution was totally open. This gave a good picture of the needs but we do not pretend
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that everything will be supported by this first version.
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%All the use-cases summarized below are detailed in appendix.
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\subsubsection{GAIA}
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The GAIA mission is producing the largest and most precise 3D map of our galaxy.
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The GAIA Astrometric Core Solution is able to provide the astrometry of more than 1
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\subsubsection{Gaia}
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The Gaia mission is producing the largest and most precise 3D map of our galaxy.
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The Gaia Astrometric Core Solution is able to provide the astrometry of more than 1
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billion sources by complex models and algorithms \citep{2012A&A...538A..78L}.
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Using a minimization problem approach, different detections identified on
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different scans can be associated to the appropriate astronomical source. Some of the
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properties would be direct measurements on single scans (e.g. positions or
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magnitudes). Other properties like radial velocity (measured in redshift
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units) are also obtained at integration time of the scans.
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units) are obtained at integration time of the scans.
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A non-exhaustive list of properties required for GAIA use cases would be composed
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A non-exhaustive list of properties required for Gaia use cases would be composed
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of:
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\begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt]
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\subsubsection{Euclid}
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The Euclid telescope has been designed to unveil some of the questions about the
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dark Universe, including dark matter and dark energy, what would include, for instance,
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dark Universe, including dark matter and dark energy, which would include, for instance,
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quite accurate measurements of the expansion of the Universe.
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Euclid will mainly observe extragalactic objects providing, for instance, information
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about the shapes of galaxies, gravitational lensing, baryon acoustic oscillations
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Euclid mainly observes extragalactic objects providing, for instance, information
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about the shapes of galaxies, gravitational lensing, baryon acoustic oscillations,
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and distances to galaxies using spectroscopic data.
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For this mission, and apart from the common metadata provided for extra galactic
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sources into astronomical catalogues, a good support for object taxonomy and
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shapes of objects will be required. As known due to general relativity effects,
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shapes of objects is required. As known due to general relativity effects,
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shapes of far galaxies could be deformed due to gravitational lensing effects,
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producing convergence (visual displacements on the position) and rear (deformation
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of the shape) effects. All these metadata should be ready for annotations and,
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\item identifier
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\item sky position
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\item correlation with other catalogues
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\item photometry (ground + satellite )
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\item photometry (ground + satellite)
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\item morphology class
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\item footprint
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\item redshift
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\item photometric redshift
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\end{itemize}
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\subsubsection{Exoplanets}
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Annotation of (exo-)planetary records in catalogues requires some
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Annotation of exoplanetary records in catalogues requires some
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specific metadata or model.
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The use cases identified requires the following metadata:
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\begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt]
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\item the degree of confidence in the detection: exoplanets candidates
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with respect to confirmed ones, plus last update of the record content ;
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with respect to confirmed ones, plus last update of the record content;
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\item the method used in the discovery (since it affects the available
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stellar system description parameters);
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\item a set of stellar host characteristics (besides sky coordinates):
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activity, mass, type, metallicity, age, some systemic values,
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like the global RV (radial velocity) of the system, and so on;
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\item (exo-)planet parameters, like mass, orbital period, orbit's
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eccentricity, RV semi-amplitude, time at periastron (for RV detections)
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or central transit time (for transit method), longitude of periastron,
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and so on.
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\item exoplanet parameters, like mass, orbital period, orbital
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eccentricity, RV semi-amplitude, time at periastron (for RV detections)
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or central transit time (for transit detections), longitude of periastron,
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and so on.
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\end{itemize}
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\subsubsection{Morphologically Complex Structures}
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The ViaLactea Knowledge Base (VLKB, see \cite{2016SPIE.9913E..0HM}) is a set of data
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resources and services built up to study the star formation regions and
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processes in the Milky Way. Besides 2-D images and 3-D radial velocity
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cubes, the VLKB exposes a bunch of source catalogues.
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cubes, the VLKB exposes a number of source catalogues.
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A model that supports description of such catalogues will need a
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way to describe sources with:
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\begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt]
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\subsubsection{X-ray Observatory Archives}
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The requirements for both Chandra
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and XMM-Newton \footnote{https://www.cosmos.esa.int/web/xmm-newton} science cases
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and XMM-Newton\footnote{https://www.cosmos.esa.int/web/xmm-newton} science cases
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are combined in this use case.
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These 2 X-ray observatories have many common features that could take advantage of sharing the same model:
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These two X-ray observatories have many common features and could take advantage of a shared model:
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\begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt]
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\item Both work as photon counters with a good time resolution.
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The result is that physical quantities remain tied to the instrument response.
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Therefore, the metadata must refer to instrumental parameters that are needed to
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understand the data well.
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\item Both observatories work in pointed mode and provide the community with sets of products per observation.
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\item Observation-level data are periodically merged into catalog of detections,
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\item Observation-level data are periodically merged into a catalogue of detections,
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which is a very important scientific product,
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but individual observations are equally important and are used directly for analysis.
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\item Detection catalogs are merged into source catalogs, and it is important to be able to
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\item Detection catalogues are merged into source catalogues, and it is important to be able to
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associate sources with their detections.
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\item Equally important, given the more than 2 decades that both spacecraft are flying,
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is the ability to correlate catalog data with time.
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\item Equally important, given the more than two decades of operation for both spacecraft,
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is the ability to correlate catalogue data with time.
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\item X-ray data reveal quantities that are usually not well supported by the VO:
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\begin{itemize}
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\item energy bands
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\item hardness ratio
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\item Flags that are very important for understanding the source detections.
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\item Complex errors (asymmetric, ellipse)
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\item model-based data (flux, spectra)
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\item energy bands;
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\item hardness ratio;
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\item flags that are very important for understanding the source detections;
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\item complex errors (asymmetric, ellipse);
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\item model-based data (flux, spectra).
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\end{itemize}
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\item X-ray data are often analyzed in conjunction with data from other domains,
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This is made easier if they all have the same way of describing the quantities of interest.
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% ============================================
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\subsubsection{VizieR catalog archive}
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VizieR provides science ready catalogs coming from space agencies or articles from the astronomical journals, covering number of different science cases.
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\subsubsection{VizieR catalogue archive}
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VizieR provides science ready catalogues coming from space agencies or articles from the astronomical journals, covering number of different science cases.
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Published data encompass a very large set of measures (position, photometry, redshift, source type, etc.)
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depending on their origin.
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They can result from observations, simulations, models or catalog compilations.
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They can result from observations, simulations, models, or catalogue compilations.
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Individual VizieR tables can contain data all related to one source (e.g. time series of positions or magnitudes) or to a set of sources (one row per source) or a mix of both.
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The MANGO model must be able to provide a standard representation of most of the metadata contained
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to make those understandable/interoperable for a wide panel of VO-compliant clients.
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\subsubsection{Client Use-cases}
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Right now, the meta-data provided within the VOTable allow client software such as Aladin or Topcat to run most
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Right now, the metadata provided within the VOTable allow client software such as Aladin or Topcat to run most
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of the functionalities expected by the user, either for data analysis or plotting.
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This information is often inferred from UCDs, UTypes or column names. It can also be given by the user.
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This information is often inferred from UCDs, UTypes, or column names. It can also be provided by the user.
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Client applications do not require the use of full model instances, but in some cases,
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models can make explicit the relationships between quantities in an input table.
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models can make the relationships explicit between quantities in an input table.
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Most cases are oriented towards interpretation of columns for visualization, e.g.:
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\begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt]
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\item what is the sky position for this row
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(what columns contain latitude and longitude, and what sky system are they in)
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(what columns contain latitude and longitude, and what sky system are they in);
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\item what +/-ERR error bars should I plot for these points
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(what column is a simple error for column A)
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(what column is a simple error for column A);
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\item what error ellipses should I plot for these sky positions
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(what columns provide ra\_error, dec\_error, ra\_dec\_corr,
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or how can I derive those from columns that do exist)
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or how can I derive those from columns that do exist);
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\item where do I get the grid information for a column containing
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a vector of samples so I can label the X axis of a spectrogram
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(what column or parameter contains an axis vector matching
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the sample vectors)
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the sample vectors);
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\item does this table contain sky positions, or HEALPix tiles, or both?
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What's the best way to represent it on the sky?
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\end{itemize}
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But there are some other cases like:
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\begin{itemize}[noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt]
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\item how do I propagate this sky position to a future epoch
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(what columns contain pmra, pmdec, and maybe all the
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associated errors and correlation coefficients)
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associated errors and correlation coefficients);
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\item what is the error ellipse/oid to use for a sky/Cartesian crossmatch
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(which columns provide the relevant errors and, if available,
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correlations)
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correlations);
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\end{itemize}
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This usage shows that MANGO must be designed in a way that individual measurements or quantities
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This usage shows that MANGO must be designed in a way that individual measurements and quantities
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can easily be identified as such and manipulated independently of the whole instance.
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\subsubsection{Xmatch tool }
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More generally, a cross-match is the association of sources from different tables given their
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proximity in an astrometric (but also possibly photometric, statistical, ...) parameter
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space \citep{2017A&A...597A..89P} .
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space \citep{2017A&A...597A..89P}.
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If proper motions (plus parallax and radial velocities) are available, the cross-match tool
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may propagate the positions of each table to a common epoch.
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UCDs may help in identifying the errors associated to positional columns,
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%as shown in table
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but this is not sufficient for tables with more complex cases based on multi-parameter cases.
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but this is not sufficient for tables with more complex cases based on multi-parameter cases.

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