MANGO version 1.0

Overview

There is a paradox in the VO world: source data which represent the basic building blocks of astronomers’ work, is not modelled. This paradox can be explained by the fact that the observation of source objects is multifaceted.
In a general way, the way features for source data are described and organised depends on the targeted science case. Principal investigators and archive designers set up the data profile and structure it according to this goal which varies from one project to another. Therefore this diversity cannot be served by a single static data model describing a source item for all possible cases.
Having a global source model would lead to a very complex solution not usable in practice.
This standard proposes to overcome this paradox and presents a template model gathering independent components from VO existing models embedded on demand in a container. MANGO is not designed to describe what a source is but to help clients to discover and to understand the quantities available for a particular source instance.

VOtable data mapped on MANGO with Mivot annotations can be consumed quantity par quantity instead of column per column. The way complex quantities are built is described by MANGO but no longer by the clients.

The main purpose of MANGO is to add an upper description level to the tabular data of query responses. MANGO is not designed to replace the meta-data already present in query responses,
Uses-cases have been collected since 2019 from representatives of various astronomical missions, archive designers and tools developers. The contribution was totally open.
This gave a good picture of the needs but we do not pretend that everything will be supported by this first version.

Participants

Laurent Michel, François Bonnarel, Gilles Landais,Jesus Salgado, Mireille Louys and Marco Molinaro and all the people having tested the model or reviewed related code.

Uses cases

The main purpose of MANGO is to add an upper description level to the tabular data of query responses. MANGO is not designed to replace the meta-data already present in query responses.
Uses-cases have been collected since 2019 from representatives of various astronomical missions, archive designers and tools developers. The contribution was totally open.

• Gaia, Euclid, exoplanets, Morphologically Complex Structures, Chandra (or XMM), Vizier, client developers, XMatch tools

This gave a good picture of the needs but we do not pretend that everything will be supported by this first version.

Requirements

From the list of use-cases, we have identified 4 domains for which the model should provide added value:

1. support of the most popular quantities
2. data description enhancement
3. description of quantities consisting of several columns
4. connected quantities.

Documentation and Implementation

The working draft PDF is regularly udpdated on GitHub.

Epoch Propagation

Mango has been developed along with MIVOT with a particular focus on the EPOC propagation.

• Vizier deployed a cone search service that returns VOtable partially mapped on MANGO: https://cdsarc.cds.unistra.fr/beta/viz-bin/mivotconesearch/<TABLE ID>
  Just try on the /I/239/hip_main table.
  This dataset can be processed by PyVO (development branch to be released with 1.6)
• A Gaia VOTable completly mapped on the epoch propagation class of MANGO (mango:EpochPosition) can bee seen on
  GitHub

HE data and Photometry

A TAP service serving XMM catalogue (https://xcatdb.unistra.fr/xtapdb/) data (table mergedentry) has been deployed.

This service demonstrates the ability of MANGO to properly describe the following quantities

• Coordinate systems (space, time imported from Coords DM)
• Position at a given epoch
• Photometric filters and calibration (imported from Phot DM)
• Fluxes and hardness ratio
• Quality flags

If you want to connect it from another client, you lust specify FORMAT=application/mango to get annotated data

Discussion Topics

Git Issues