This topic collects proposals for modifications of the Meas-1.0 specification in order to improve the next revision of the specification.

The RFC comments included extensions/refinements which would make the model more compatible with Catalog data. Since the support of catalogs is not in the scope of this version of the model, these are being added here for consideration on the next round, which is expected to include Source properties in catalogs.

- Confidence Level:
- [F-X Pineau] When we assume that errors are Gaussian (we probably need a way to mention it explicitly), we can convert covariance matrices into elliptical errors. But to convert symmetrical or ellipitical errors into covariance matrices we need an extra information which is the "confidence level" (or a number of sigma, ...) the symmetrical or elliptical error is associated to. For example, the error radius in ROSAT is associated to a 68% confidence interval. In the WGCAT or FIRST catalogues, it is a 90% confidence interval. A lot of catalogues in VizieR contain circular positional errors associated to confidence intervals different from 39% (value defined by a 2D covariance Matrix).

- Alternate Error representations
- [F-X Pineau] Positional error matrices in catalogues are provided by 3 columns. The 2 first ones are the standard deviations (I have not yet seen variances). The 3rd one may be: the covariance (I do not remember a catalogue using it), the correlation (see e.g. SDSS DR7 or Gaia) or the co-sigma (see "sigradec" in the AllWISE doc) The current model accounts only for the covariances and Markus suggestion is to use correlations. We should probably support both (plus co-sigma).
- [Markus D] I'll also note that the current model is insufficient to annotate the Gaia result catalog, because there, the errors of all five primary observables are correlated. Perhaps annotating Gaia DR2 in full depth is a lot to ask, but with a generic Correlation class you could at least sensibly annotate all the columns that we do have, which I'd count as an indication it may be preferable (not to mention it's simpler model-wise).

- Tight coupling between properties
- [F-X Pineau] Gaia case in mind. One can consider only the postions or the positions + proper motions (+ plx + Vr). Can we make a measurement which is the composition of positions + PMs (+ ...)? In that case, how to add the covariances (or correlation factors) between positional and PM parameters?

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Topic revision: r1 - 2020-06-24 - MarkCresitelloDittmar

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