Observation data model comments

Alberto Micol Alberto.Micol at eso.org
Fri May 7 15:56:52 PDT 2004


I like the table you put together, it shows all the various aspects at once.

Few comments:

In the Characterisation class you mention:
> e.g. spatial coverage in objects/deg^2

I would say that objects/deg^2 does not belong to "spatial coverage", it's
instead a way to characterise the observation. 

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In the explanations of the Processing class there is the following question:
>If one set of observations can have different processing parameters (e.g.
>different weightings), possibly established on-the-fly, giving ObsData with
>different Characterisation, is this a separate Observation? 

They way we handle such case for HST enhanced products (eg wfpc2 associations)
is to create different versions of the same observation (using a generation date
to distinguish among them, and having a flag telling us which one is the current
version). Each version was generated with different input parameters or
algorithms, hence each version has different characterisation/coverage.

So, the observation is the same, it's the version that changes.
(Hence, the requirement is that a VO identifier must be able to handle
versioning.)  
 
---

You seem to separate Processing from Provenance. I would have tought that
Processing is part of Provenance. Suppose that you want to describe a product
which was generated by combining other products coming even from different
instruments; in such case I would say that Processing and Provenance are very
much intertwined.

---

Maybe a twiki problem ...
> The gray boxes show classes which exchange information with other models,
I can't see gray boxes, they are all white on my netscape/linux.
I will try with macosx/safari ...

---

In the last table:

Bounds: for Flux more than the noise rms I would use the limiting flux
for the lower limit, and I would add the saturation level for the upper limit.


Support: for Flux I would use the min and max fluxes in the data.

Sensitivity: for Temporal I would say "exposure map"

Sample precision: for Spatial -> pixel scale, for Flux -> readout noise for CCD
data.


I love the fact that now the Radio domain is represented! 
(That's a challenge, though, since I need to understand/think radio now ...) 

Thanks!

Alberto



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