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<p>Hi Ian , thank you for your inputs. <br>
</p>
<p>here are my comments ( included) , before I include most of it
in the VEP update. </p>
<p>The updated version of this VEP is uploaded on
ivoa/HighEnergyObscoreExtension in a new pull request so that we
can review it internally. <br>
<br>
</p>
<p>thanks , Mireille.</p>
<div class="moz-cite-prefix">Le 20/05/2026 à 10:19 PM, Dr. Ian N.
Evans a écrit :<br>
</div>
<blockquote type="cite"
cite="mid:059C5E57-3EAD-4BAD-9FCC-3FF0D243DB7E@cfa.harvard.edu">
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
Hi Mireille,
<div><br>
</div>
<div>Here is some feedback on what is currently written for
VEP-analysis-products-MLouys-2026-04-22.txt.</div>
<div><br>
</div>
<div>————</div>
<div><br>
</div>
<div>
<blockquote type="cite">
<div>New Term: draws</div>
<div><br>
</div>
<div>Action: Addition</div>
<div><br>
</div>
<div>Label: draws</div>
<div><br>
</div>
<div>Description: Probabilistic dataset containing a
collection of samples (draws)</div>
<div><span class="Apple-tab-span" style="white-space:pre"> </span>generated
from a probability distribution.</div>
</blockquote>
<div><br>
</div>
Description: A dataset that records statistical draws computed
from a probability distribution or a sample population, for
example Markov chain Monte Carlo (MCMC) draws used when
computing the Bayesian marginal probability density function for
a random variable. The draws</div>
<p class="p1"
style="margin: 0px; font-width: normal; line-height: normal; font-size-adjust: none; font-kerning: auto; font-variant-alternates: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; font-feature-settings: normal; font-optical-sizing: auto; font-variation-settings: normal;">can
be interpreted to provide a robust estimation of the probability
distribution of variable, and correlations between the draws
provide information about how well the draws converge to the
parent probability distribution.</p>
<div><br>
</div>
<div><br>
<blockquote type="cite">
<div><br>
</div>
<div>Relationships: none</div>
</blockquote>
<div><br>
</div>
Relationships: parent #measurements</div>
<div><br>
</div>
</blockquote>
Measurements is not recommended anymore in the use of ObsCore as I
explained some time ago. <br>
The term #measurements is not implemented , and too ambiguous. <br>
<p>And this hierarchy does not help to figure out the content of
these data products. <br>
There is no reasonning involved on the VEP labels . </p>
<blockquote type="cite"
cite="mid:059C5E57-3EAD-4BAD-9FCC-3FF0D243DB7E@cfa.harvard.edu">
<div><br>
<blockquote type="cite">
<div><br>
</div>
<div>Used-in: % todo : provide a link to an example dataset </div>
<div>?? example corner plot gammapy ?? </div>
<div>by high energy photon and neutrino experiments, and by
cosmological observatories</div>
</blockquote>
<div><br>
</div>
Used-in: Example: detection position uncertainty draws data
products (Chandra Source Catalog data product), e.g., <a
href="https://cda.cfa.harvard.edu/csccli/retrieveFile?filename=acisf03498_000N030_r2102s_draws3.fits&filetype=draws&version=rel2.1"
moz-do-not-send="true">https://cda.cfa.harvard.edu/csccli/retrieveFile?filename=acisf03498_000N030_r2102s_draws3.fits&filetype=draws&version=rel2.1</a>;
there are also aperture photometry draws data products (draws
for various flux distributions) that will be released in October
2026.</div>
</blockquote>
Thanks for this example
<blockquote type="cite"
cite="mid:059C5E57-3EAD-4BAD-9FCC-3FF0D243DB7E@cfa.harvard.edu">
<div><br>
<blockquote type="cite">
<div><br>
</div>
<div>Rationale: </div>
<div><br>
</div>
<div>High-energy photon and neutrino experiments must employ
statistical methods to derive </div>
<div>final products like #spectrum, #sed, #light-curve or
#image in physical units. The </div>
<div>underlying reason is that instrument responses are
inherently non-invertible. By </div>
<div>computation of probabilities for random variables
associated with spectral, spatial, </div>
<div>and/or temporal models, these final products can be
derived.</div>
<div><br>
</div>
<div>In a frequentist approach, the best parameter estimates
correspond to the maximum </div>
<div>likelihood probability among all possible realizations of
the random variables.</div>
<div>When priors are applied, the estimate is derived from the
maximum of the posterior</div>
<div>probability. In Bayesian inference, the best estimate is
associated with the 50th </div>
<div>percentile (median) of the posterior draws.</div>
<div><br>
</div>
<div>This dataset maps the likelihood or probability
landscapes across a space phase of </div>
<div>possible values of the random variables. The collection
of probabilities enables the</div>
<div>computation of quantiles, confidence intervals,
confidence limits, and thus uncertainties,</div>
<div>upper limits, and lower limits. This collection is
particularly critical in cases of </div>
<div>non-Gaussian degeneracies or when dealing with a large
number of parameters.</div>
</blockquote>
<div><br>
</div>
Rationale:</div>
<div><br>
</div>
<div>Many analysis methods across all wavebands use statistical
methods to establish optimal parameter estimates for measured or
derived properties. In particular, high-energy astrophysics
analyses must employ statistical methods to derive products such
as #spectrum, #sed, #light-curve, #image etc. in physical units
since the instrument responses are usually non-invertible.</div>
<div><br>
</div>
<div>The term draws is equally applicable to Bayesian inference or
frequentist analysis. In the frequentist approach, the best
parameter estimates correspond to the maximum likelihood
probability among all realizations of the random variables. In
Bayesian inference, The best parameter estimates are typically
derived from the mode of the posterior probability distribution.</div>
<div><br>
</div>
<div>A draws dataset maps the probability (or equivalently,
likelihood) of the desired parameters across a phase space of
possible values of selected random variables. The set of draws
enables the computation of the distributions of the probability
density functions of desired parameters as a function of the
random variables, enabling determination of optimal parameter
estimates, confidence intervals, quantiles, confidence limits,
and thus uncertainties, upper limits, and lower limits. The
draws provide information as to the actual statistical
distribution of parameter uncertainties, with is particularly
critical in cases of non-Gaussian degeneracies, small number
statistics (inherently non-Gaussian), or when dealing with large
numbers of parameters. Additionally, a key benefit of draws is
that the dataset inherently provides information on the
robustness of the statistical sampling approach and how well the
draws converge to the parent probability distribution, which is
not available from other parameter estimation data products such
as probability density functions.</div>
<div><br>
</div>
<div><br>
<blockquote type="cite">
<div><br>
</div>
<div>Discussion : </div>
<div>++ 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.</div>
<div>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. </div>
</blockquote>
<blockquote type="cite">
<div><br>
</div>
</blockquote>
<div><br>
</div>
Discussion:</div>
<div><br>
</div>
<div>The term “draws” is highly generic and is applicable to any
statistical framework, whether frequentist analysis or Bayesian
inference. The term “samples” was also considered initially,
but is very general and widely used in astronomy for a variety
of different purposes (for example, moon rocks samples, or other
laboratory physical samples which would be outside of the HEIG
scope here and misleading.</div>
<div><br>
</div>
<div>There is a subtle difference between the widely used meanings
of the term “samples” used in statistical analyses and the term
“draws”, although they are often used interchangeably: </div>
<div> — “Samples” are the individual components of a statistical
sample selected from a larger population, and the sample is
typically used as representative of a population. This term is
commonly used in frequentist statistical analyses.</div>
<div> — “Draws” are very similar, but can be drawn either from a
population or from a probability distribution (such as the
posterior probability distribution used in Bayesian statistics).
This term is commonly used in Bayesian statistical analyses,
*but is also applicable to frequentist analyses* (in the former
case is sampling parameters of the distribution whereas for the
latter case one is sampling data points from the observed
population).</div>
<div>Because of this, we recommend the use of the term “draws”.
We note that the existing datasets that require this definition
are Bayesian posterior distributions where “samples” isn’t
really an appropriate choice.</div>
<div><br>
</div>
<div>————</div>
<div><br>
</div>
<blockquote type="cite">
<div>
<div>New Term: pdf</div>
<div><br>
</div>
<div>Action: Addition</div>
<div><br>
</div>
<div>Label: Probability Density Function of a quantity</div>
<div><br>
</div>
<div>Description: Probability density function of a quantity,
for example the Bayesian</div>
<div><span class="Apple-tab-span" style="white-space:pre"> </span>marginal
probability density function associated to the spectral
index of</div>
<div><span class="Apple-tab-span" style="white-space:pre"> </span>a
spectrum</div>
</div>
</blockquote>
<div><br>
</div>
Description: A dataset that records the probability density
function of a quantity, for example the Bayesian marginal
probability density
<p class="p1"
style="margin: 0px; font-width: normal; line-height: normal; font-size-adjust: none; font-kerning: auto; font-variant-alternates: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-position: normal; font-feature-settings: normal; font-optical-sizing: auto; font-variation-settings: normal;">function
for a random variable, or the DeltaTS associated with a quantity
from a Frequentist analysis. The probability density function
provides a robust estimation of the variable and allows
arbitrary confidence intervals to be computed directly from the
distribution.</p>
<div><br>
</div>
<div><br>
<blockquote type="cite">
<div>
<div><br>
</div>
<div>Relationships: none</div>
</div>
</blockquote>
<div><br>
</div>
Relationships: parent #measurements, child: #psf, #rmf, #edisp</div>
<div><br>
</div>
</blockquote>
same remarks as above : no reasonning involved between labels
<blockquote type="cite"
cite="mid:059C5E57-3EAD-4BAD-9FCC-3FF0D243DB7E@cfa.harvard.edu">
<div><br>
<blockquote type="cite">
<div>
<div><br>
</div>
<div>Used-in: --> please provide an example </div>
<div>by high energy photon and neutrino experiments, and by
cosmological observatories</div>
</div>
</blockquote>
<div><br>
</div>
Used-in: Example: aperture photometry (net counts, count rate,
photon flux, and energy flux) probability density function data
products (Chandra Source Catalog data product), e.g., <a
href="https://cda.cfa.harvard.edu/csccli/retrieveFile?filename=acisf14335_000N031_r2598b_phot3.fits&filetype=aperphot&version=rel2.1"
moz-do-not-send="true">https://cda.cfa.harvard.edu/csccli/retrieveFile?filename=acisf14335_000N031_r2598b_phot3.fits&filetype=aperphot&version=rel2.1</a></div>
<div><br>
</div>
</blockquote>
<br>
<blockquote type="cite"
cite="mid:059C5E57-3EAD-4BAD-9FCC-3FF0D243DB7E@cfa.harvard.edu">
<div><br>
<blockquote type="cite">
<div>
<div><br>
</div>
<div>Rationale:</div>
<div>When statistical analyses are in used to derive final
products like #spectrum, #sed, </div>
<div>#light-curve or #image in physical units, the
probability density function (PDF) associated</div>
<div>to a random variable can be derived. This PSF can be
the probability, the posterior or even</div>
<div>the prior of a random variable.</div>
<div><br>
</div>
<div>This is very useful when the distribution is highly
asymmetrical or multi-modal. If the </div>
<div>variable is for exemple the size of an object, the
knowledge of asymmetry of this PDF is </div>
<div>obviously more useful than symmetric errors.</div>
<div><br>
</div>
<div>Note that this PDF can be "differential" (e.g. a
probability at a given value), "integral" or</div>
<div>"average" (when bins are used for the random variable).
The serialization of this data production</div>
<div>should then contain accurate metadata information.</div>
<div><br>
</div>
<div>When statistical analyses are employed to derive final
products such as #spectrum, #sed, #light-curve</div>
<div>or #image in physical units, the probability density
function (PDF) associated with a random variable</div>
<div>can be derived. This PDF may represent the probability,
posterior, or prior distribution of the random variable.</div>
</div>
</blockquote>
<div><br>
</div>
Rationale:</div>
<div><br>
</div>
<div>
<div>Statistical analyses used to establish parameter estimates
for measured or derived properties yield typically quantities
that describe the shape of the probability density function
(or pdf) of those parameters. For simple analyses, these may
be (e.g.) the mean and variance of a Gaussian distribution
that approximates the actual probability distribution. </div>
<div><br>
</div>
<div>High-energy astrophysics must employ statistical methods
for parameter estimation and to derive products such as
#spectrum, #sed, #light-curve, #image etc. in physical units.
In many cases the probability distribution is non-Gaussian
(indeed, non-analytic), and so a representation of the
*actual* probability distribution is needed for robust further
analysis (especially in HEA, where source counts in the
extreme Poisson regime are common and uncertainties in the
calibrations themselves [random and systematic] must also be
considered. </div>
<div><br>
</div>
<div>Estimates such as the mean/median/mode, and confidence
intervals etc. can be derived from the pdf; however many
modern analyses will use the pdf distribution directly. This
is very useful when the distribution is highly asymmetrical or
multi-modal. If the variable is for example the size of an
object, the knowledge of asymmetry of this PDF is obviously
more useful than symmetric errors.</div>
<div><br>
</div>
<div>There are two main types of pdfs in common use: (1) a
“differential” pdf (this is the most common) reports the
probability density as a function of the random variable so
that the pdf is a table of P(x) vs. x; in practical
representations, the random variable is quantized rather than
continuous, so the pdf is a table where each row typically
records the integral probability within a single x bin, i.e.,
P(x_lo-to-x_hi) vs. x; (2) an “integral” pdf (commonly termed
a cdf), which corresponds to the cumulative probability
P(-infinity-to-x) vs. x. A third type of PDF is the “average”
pdf, which provides the expected value (center of mass) of the
distribution; however these may be represented by a single
value and do not require a tabular representation.</div>
<div><br>
</div>
<div><br>
</div>
<div><br>
</div>
<div>
<div>
<blockquote type="cite">
<div>Discussion:</div>
<div>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.</div>
<div><br>
</div>
<div>It is important to note that the PDF can be
"differential" (e.g., the probability at a specific
value), "integral",</div>
<div>or "averaged" (when bins are used for the random
variable). Consequently, the serialisation of this data
product</div>
<div>must include precise metadata to ensure clarity and
reproducibility.</div>
<div><br>
</div>
<div>M.L: --> parameters to describe for PDF to be
retrieved : </div>
<div>probability_type = differential/integral/averaged</div>
</blockquote>
<blockquote type="cite"><br>
</blockquote>
<br>
</div>
</div>
<div>Discussion:</div>
<div><br>
</div>
<div>The serialization of the data product should preferably
include metadata to differentiate between the types of pdfs.
However, this may not be critical since the type of pdf can
be determined from the sum of the probabilities over the
distribution (the sum of the probabilities of a differential
pdf that includes only the instantaneous probabilities at the
x values will be < 1, for a binned differential pdf the sum
will be 1, and for a cdf the sum will be > 1) provided the
pdf spans the distribution adequately.</div>
<div><br>
</div>
<div>
<div>————</div>
<div><br>
</div>
</div>
<blockquote type="cite">
<div>
<div>New Term: region</div>
<div><br>
</div>
<div>Action: Addition</div>
<div><br>
</div>
<div>Label: Region</div>
<div><br>
</div>
<div>Description: dataset that encodes (one or more) regions
of parameter space, for example </div>
<div>a spatial region or a region of phase space covered by
a dataset. The set of dimensions</div>
<div>represented by the region can be arbitrary</div>
</div>
</blockquote>
<blockquote type="cite">
<div>
<div><br>
</div>
<div>Relationships: none</div>
</div>
</blockquote>
<div><br>
</div>
<div>Relationships: parent #measurements</div>
<div><br>
</div>
<div><br>
</div>
<blockquote type="cite">
<div>
<div><br>
</div>
<div>Used-in: %todo: provide a real example </div>
</div>
</blockquote>
<div><br>
</div>
Used-in: Example: region data products (Chandra Source Catalog
data product), e.g., <a
href="https://cda.cfa.harvard.edu/csccli/retrieveFile?filename=acisf15546_000N030_r3154_reg3.fits&filetype=srcreg&version=rel2.1"
moz-do-not-send="true">https://cda.cfa.harvard.edu/csccli/retrieveFile?filename=acisf15546_000N030_r3154_reg3.fits&filetype=srcreg&version=rel2.1</a></div>
<div><br>
</div>
<div><br>
<blockquote type="cite">
<div>
<div>Rationale: </div>
<div>%todo: clarify the role and dimensionality of this
dataset kind</div>
<div>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. </div>
</div>
</blockquote>
<div><br>
</div>
Rationale:</div>
<div><br>
</div>
<div>Existing astronomical data archives record region information
in many different formats (typically not related to IVOA
standards, since in many cases they pre-date those standards).
For example, Chandra X-ray Observatory typically records
spatial regions using the FITS Spatial Region File Registered
Convention, which is supported by the widely use CFITSIO FITS
I/O software library as well as Astropy. XMM and Fermi support
ds9 format region data products, and the NRAO Common Astronomy
Software Applications (CASA) radio package supports the CRTF
region file format. Within the IVOA, a MOC data product is a
type of region data product. Different region data products
standards may include information regarding the shape, whether
it is a source or background region, whether it is an inclusion
or exclusion region, whether it can be
edited/moved/rotated/deleted, region color and width, and
associated metadata.</div>
<div><br>
</div>
<div>Advanced data products (ObsCore calib_level > 2) may
result from analyses of (possibly multiple) existing data
products and may not want to attach region information to
existing data products. For example, a catalog such as the
Chandra Source Catalog may identify (detect) tens of thousands
of sources from an existing data product and then analyze
properties for each of the sources; information about the source
and background regions and cutouts is essential to correctly
compute various source properties (for example, to compute
aperture corrections for aperture photometry), but in general
one would not want to add these region definitions to existing
data products and would not want to duplicate this information
in multiple other data products. Recording the region
information as queryable data products that work with current
software is a sensible solution.</div>
<div><br>
</div>
<div>The purpose of region is to provide a data product type that
can be used to query existing archives for those data products,
irrespective of the internal format or serialization of the data
product.</div>
<div><br>
</div>
<div><br>
<blockquote type="cite">
<div>
<div><br>
</div>
<div>Discussion:</div>
<div>Not clear how universal this can be in the High Energy
domain. </div>
<div>Some data collections like XMM, SVOM, etc. may store
this information in a FITS file extension , or a S_MOC
extension.</div>
<div>If the dataset is multidimensional, it does not fit
into the tree proposed in
<a class="moz-txt-link-rfc2396E" href="https://www.google.com/url?q=http://www.ivoa.net/rdf/product-type&source=gmail-imap&ust=1779901952000000&usg=AOvVaw1sXRg5TwJRZb6Q_jNR67f6">"https://www.google.com/url?q=http://www.ivoa.net/rdf/product-type&source=gmail-imap&ust=1779901952000000&usg=AOvVaw1sXRg5TwJRZb6Q_jNR67f6"</a>,
which is based on the number and kind of data axes </div>
</div>
</blockquote>
<div><br>
</div>
Discussion:</div>
<div><br>
</div>
<div>The region data product is intended to be universal for those
facilities and archives that include region information recorded
in data products that are separate from associated data. There
are some data products that record region information as FITS
file extensions or perhaps an S_MOC extension. In such cases, a
separate region data product may not be necessary.</div>
<div><br>
</div>
<div>We have intentionally not restricted the dimensionality of
region data products. However, most existing archival region
data products are restricted to 2 spatial dimensions, although
there are some that include spectral and temporal dimensions.</div>
<div><br>
</div>
<div>
<div>————</div>
</div>
<div><br>
</div>
<div>Thanks,</div>
<div>—Ian</div>
<div><br>
<blockquote type="cite">
<div><br>
</div>
</blockquote>
<div>
<div>
<blockquote type="cite">
<div>On May 20, 2026, at 13:12, Mireille Louys
<a class="moz-txt-link-rfc2396E" href="mailto:mireille.louys@unistra.fr"><mireille.louys@unistra.fr></a> wrote:</div>
<br class="Apple-interchange-newline">
<div>
<meta http-equiv="content-type"
content="text/html; charset=UTF-8">
<div text="#000000" bgcolor="#FFFFFF">
<p>Hi everyone, </p>
<p>The discussion about the UCD terms proposed in the
note is summarized on the request for modification
page for UCD: <br>
<a moz-do-not-send="true"
href="https://www.google.com/url?q=https://wiki.ivoa.net/twiki/bin/view/IVOA/UCDList_1-7_RFM&source=gmail-imap&ust=1779901952000000&usg=AOvVaw166if-Cy2BboO_Q0vrAKow"
class="moz-txt-link-freetext">https://wiki.ivoa.net/twiki/bin/view/IVOA/UCDList_1-7_RFM</a></p>
<p>The terms are described in the VEP-UCD description
files available from their specific link from the
page above. <br>
All the VEP-UCD files are also available at <a
moz-do-not-send="true"
href="https://www.google.com/url?q=https://voparis-gitlab.obspm.fr/vespa/ivoa-standards/semantics/vep-ucd&source=gmail-imap&ust=1779901952000000&usg=AOvVaw3qfBXSz0zl3imUQflsSD56"
class="moz-txt-link-freetext">https://voparis-gitlab.obspm.fr/vespa/ivoa-standards/semantics/vep-ucd</a></p>
<p>We need to update the UCD section following the
decisions taken . </p>
<p>There is another topic with semantics : the
analysis products vocabulary . </p>
<p>I attach here a draft version of a VEP for analysis
data product type : <br>
What is needed are </p>
<p>- a revision of the definitions in order to
encompass various kinds of HE experiments <br>
- file examples for the Used-in section <br>
- clarification of the #region data product</p>
<p>Thanks for helping for this.</p>
<p>Mireille</p>
<pre class="moz-signature" cols="72">--
--
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</pre>
</div>
<span id="cid:DD56F987-5BD5-48D3-9B37-604FC7709187"><VEP-analysis-products-MLouys-2026-04-22.txt></span></div>
</blockquote>
</div>
<br>
<div>
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Dr. Ian Evans</span></div>
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<pre class="moz-signature" cols="72">--
--
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</pre>
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