[Heig] validation of UCD proposal by the semantics WG : results of the semantics meeting for UCD in Strasbourg 6-7 of May

[Mireille Louys]

Hi everyone,

The discussion about the UCD terms proposed in the note is summarized on 
the request for modification page for UCD:
https://wiki.ivoa.net/twiki/bin/view/IVOA/UCDList_1-7_RFM

The terms are described in the VEP-UCD description files available from 
their specific link from the page above.
All the VEP-UCD files are also available at 
https://voparis-gitlab.obspm.fr/vespa/ivoa-standards/semantics/vep-ucd

We need to update the UCD section following the decisions taken .

There is another topic with semantics : the analysis products vocabulary .

I attach here a draft version of a VEP for analysis data product type :
What is needed are

- a revision of the definitions in order to encompass various kinds of 
HE experiments
- file examples for the Used-in section
- clarification of the #region data product

Thanks for helping for this.

Mireille

-- 
--
Mireille Louys, MCF (Assistant Professor)
Centre de données Astronomiques (CDS)       Equipe Images, ICube
Observatoire de Strasbourg                  Telecom Physique Strasbourg
11, rue de l' Université                    300, Bd Sebastien Brandt CS 10413
F-67000 Strasbourg                          F-67412  Illkirch Cedex
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Vocabulary: http://www.ivoa.net/rdf/analysis-product-type
Author: Bruno Khelifi <khelifi at in2p3.fr> ,<mireille.louys at unistra.fr>
Date: 2026-02-04/ updated 2026-04-22

New Term: draws

Action: Addition

Label: draws

Description: Probabilistic dataset containing a collection of samples (draws)
	generated from a probability distribution.

Relationships: none

Used-in: % todo : provide a link to an example dataset 
?? example corner plot gammapy ?? 
by high energy photon and neutrino experiments, and by cosmological observatories

Rationale: 

High-energy photon and neutrino experiments must employ statistical methods to derive 
final products  like #spectrum, #sed, #light-curve or #image in physical units. The 
underlying reason is that instrument responses are inherently non-invertible. By 
computation of probabilities for random variables associated with spectral, spatial, 
and/or temporal models, these final products can be derived.

In a frequentist approach, the best parameter estimates correspond to the maximum 
likelihood probability among all possible realizations of the random variables.
When priors are applied, the estimate is derived from the maximum of the posterior
probability. In Bayesian inference, the best estimate is associated with the 50th 
percentile (median) of the posterior draws.

This dataset maps the likelihood or probability landscapes across a space phase of 
possible values of the random variables. The collection of probabilities enables the
computation of quantiles, confidence intervals, confidence limits, and thus uncertainties,
upper limits, and lower limits. This collection is particularly critical in cases of 
non-Gaussian degeneracies or when dealing with a large number of parameters.

Discussion : 
++ The term is highly generic and applicable to any statistical framework, whether frequentist or Bayesian. It is worth noting that "draws" is a term typically associated with Bayesian statistics, whereas "samples" is more generic.
Note that 'samples', initially considered, can also be used for moon rocks samples, or other laboratory physical samples which would be outside of the HEIG scope here and misleading. 



=======================================

Vocabulary: http://www.ivoa.net/rdf/product-type
Author: Bruno Khelifi <khelifi at in2p3.fr>
Date: 2026-02-04

New Term: pdf

Action: Addition

Label: Probability Density Function of a quantity

Description: Probability density function of a quantity, for example the Bayesian
	marginal probability density function associated to the spectral index of
	a spectrum

Relationships: none

Used-in: --> please provide an example 
by high energy photon and neutrino experiments, and by cosmological observatories

Rationale:
When statistical analyses are in used to derive final products like #spectrum, #sed, 
#light-curve or #image in physical units, the probability density function (PDF) associated
to a random variable can be derived. This PSF can be the probability, the posterior or even
the prior of a random variable.

This is very useful when the distribution is highly asymmetrical or multi-modal. If the 
variable is for exemple the size of an object, the knowledge of asymmetry of this PDF is 
obviously more useful than symmetric errors.

Note that this PDF can be "differential" (e.g. a probability at a given value), "integral" or
"average" (when bins are used for the random variable). The serialization of this data production
should then contain accurate metadata information.

When statistical analyses are employed to derive final products such as #spectrum, #sed, #light-curve
or #image in physical units, the probability density function (PDF) associated with a random variable
can be derived. This PDF may represent the probability, posterior, or prior distribution of the random variable.

Discussion:
This approach is particularly valuable when the distribution is highly asymmetric or multimodal. For example, if the variable represents the size of an object, knowledge of the asymmetry in the PDF is significantly more informative than symmetric error estimates.

It is important to note that the PDF can be "differential" (e.g., the probability at a specific value), "integral",
or "averaged" (when bins are used for the random variable). Consequently, the serialisation of this data product
must include precise metadata to ensure clarity and reproducibility.

M.L: --> parameters to describe for PDF to be retrieved : 
probability_type = differential/integral/averaged

=======================================

Vocabulary: http://www.ivoa.net/rdf/product-type
Author: Bruno Khelifi <khelifi at in2p3.fr>
Date: 2026-02-04, update 2026-04-22

New Term: region

Action: Addition

Label: Region

Description: dataset that encodes (one or more) regions of parameter space, for example 
a spatial region or a region of phase space covered by a dataset. The set of dimensions
represented by the region can be arbitrary

Relationships: none

Used-in: %todo: provide a real example 
Rationale: 
%todo: clarify the role and dimensionality of this dataset kind
It seems that the spatial coverage of the observation is given as an extra data product (like an excess_map, or an error_map ?) in Chandra. 

Discussion:
Not clear how universal this can be in the High Energy domain. 
Some data collections like XMM, SVOM, etc. may store this information in a FITS file extension , or a S_MOC extension.
If the dataset is multidimensional, it does not fit into the tree proposed in "http://www.ivoa.net/rdf/product-type", which is based on the number and kind of data axes