forked from ivoa-std/PhotDM
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathPhotDM.tex
More file actions
2082 lines (1707 loc) · 80.1 KB
/
PhotDM.tex
File metadata and controls
2082 lines (1707 loc) · 80.1 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
\documentclass[11pt,a4paper]{ivoa}
\input ivoatex/tthdefs
\lstloadlanguages{sh,make,[latex]tex}
\lstset{flexiblecolumns=true,numberstyle=\small,showstringspaces=False,
identifierstyle=\texttt,defaultdialect=[latex]tex,language=tex}
\usepackage{todonotes}
\usepackage{float}
\usepackage{adjustbox}
\usepackage{lscape}
\usepackage{makecell}
\usepackage{amsmath, amsthm, amssymb, amsfonts}
\usepackage[english]{babel}
\usepackage[font=footnotesize,labelfont=bf]{caption}
\usepackage{titlesec}
\setcounter{secnumdepth}{6}
\titleformat{\paragraph}{\normalfont\normalsize\sffamily\bfseries}
{\theparagraph}{1ex}{}
\titlespacing*{\paragraph}{0pt}{6pt plus 4pt}{4pt}
\titleformat{\subparagraph}{\normalfont\normalsize\sffamily\itshape}
{\thesubparagraph}{1ex}{}
\titlespacing*{\subparagraph}{0pt}{4pt plus 2pt}{4pt}
\usepackage[utf8]{inputenc}
\usepackage{float}
\usepackage[titletoc]{appendix}
\usepackage{listings}
% define colors for syntax XML
% include listing settings
\include{syntaxXML}
% end define colors
\usepackage{booktabs,xcolor}
\definecolor{texcolor}{rgb}{0.4,0.1,0.1}
\definecolor{lightgray}{gray}{0.9}
\lstset{flexiblecolumns=true,basicstyle=\ttfamily}
\iftth
\newcommand{\BibTeX}{BibTeX}
\fi
\newcommand{\angstrom}{\mbox{\normalfont\AA}}
\title{IVOA Photometry Data Model}
\ivoagroup{Data Model Working Group}
\author{Jesús Salgado}
\author{Mireille Louys}
\author{Laurent Michel}
\author{Carlos Rodrigo}
\author{Pedro Osuna}
\author{Mark Allen}
\author{Jonathan McDowell}
\author{Deborah Baines}
\author{Jesús Maíz Apellániz}
\author{Evanthia Hatziminaoglou}
\author{Sebastien \mbox{Derriere}}
\author{Gerard Lemson}
\editor{Jesús Salgado}
\editor{Mireille Louys}
\begin{document}
\begin{abstract}
The Photometry Data Model (\textbf{PhotDM}) standard describes photometry
filters, photometric systems, magnitude systems, zero points and its
interrelation with the other IVOA data models through a simple data model.
Particular attention is given necessarily to optical photometry where
specifications of magnitude systems and photometric zero points are required
to convert photometric measurements into physical flux density units.
\end{abstract}
\section*{Acknowledgments}
We acknowledge the support of EuroVO Science Advisory Committee for the review of the
initial versions of PhotDMv1.0 , and the Spanish VO developers who have contributed
to the data model reference implementations.
The design changes and the modeling for the translation to VODML was supported in part by the
the ESCAPE project (the European Science Cluster of Astronomy and Particle Physics ESFRI Research Infrastructures)
that has received funding from the European Union?s Horizon 2020 research and innovation programme under the Grant Agreement n. 824064.
\pagebreak
\section*{Link to IVOA Architecture}
The figure below shows where IVOA Photometry Data Model fits within the
IVOA architecture:
%%%%%%%%%%%%%%%%%%%% Figure/Image No: 1 starts here %%%%%%%%%%%%%%%%%%%%
\begin{figure}[H]
\centering
% As of ivoatex 1.2, the architecture diagram is generated by ivoatex in
% SVG; copy ivoatex/archdiag-full.xml to archdiag.xml and throw out
% all lines not relevant to your standard.
% Notes don't generally need this. If you don't copy archdiag.xml,
% you must remove archdiag.svg from FIGURES in the Makefile.
\includegraphics[width=0.9\textwidth]{role_diagram.pdf}
\caption{PhotDM can be used to enhance and abstract SSAP and TAP access, in particular
to help on the automatic translation of magnitudes to fluxes and to add provenance
metadata to these magnitudes. Also it can be used to generate SEDs using the SpectralDM.
PhotDMv1.1 makes use of UTypes, for backward compatibility to PhotDMv1.0 , and of UCDs as well as VOUnits.
PhotDM can have serialisation in the VOTable format. This version relies on the VODML modeling principles and
is described in a VODML xml reference document .}
\label{fig:archdiag}
\end{figure}
\section*{Changes from Version 1.0} \label{changesTable}
%%%%%%%%%%%%%%%%%%%% change log starts here %%%%%%%%%%%%%%%%%%%%
\begin{table}[H]
\centering
\begin{tabular}{p{3.in}p{1in}p{0.8in}}
\hline
%row no:1
\multicolumn{1}{|p{3.75in}}{\textbf{Change}} &
\multicolumn{1}{|p{0.72in}}{\textbf{Section}} &
\multicolumn{1}{|p{0.9in}|}{\textbf{Date}} \\
\hline
%row no:2
\multicolumn{1}{|p{3.75in}}{First PhotDM 1.1 Latex version} &
\multicolumn{1}{|p{0.72in}}{All} &
\multicolumn{1}{|p{0.9in}|}{{\fontsize{10pt}{12.0pt}\selectfont
2021/10/07}} \\
\hline
%row no:3
\multicolumn{1}{|p{3.75in}}{Use the Modelio class diagram
figure} &
\multicolumn{1}{|p{0.72in}}{\ref{datamodel}} &
\multicolumn{1}{|p{0.9in}|}{{\fontsize{10pt}{12.0pt}\selectfont
2021/10/25}} \\
\hline
%row no:4
\multicolumn{1}{|p{3.75in}}{Data model summary updates and
correction of UCD tags} &
\multicolumn{1}{|p{0.72in}}{All} &
\multicolumn{1}{|p{0.9in}|}{{\fontsize{10pt}{12.0pt}\selectfont
2021/12/21}} \\
\hline
%row no:5
\multicolumn{1}{|p{3.75in}}{Add changes table} &
\multicolumn{1}{|p{0.72in}}{\ref{datamodel}} &
\multicolumn{1}{|p{0.9in}|}{{\fontsize{10pt}{12.0pt}\selectfont
2022/02/02}} \\
\hline
%row no:6
\multicolumn{1}{|p{3.75in}}{Add mapping example} &
\multicolumn{1}{|p{0.72in}}{\ref{appendixmapping}} &
\multicolumn{1}{|p{0.9in}|}{{\fontsize{10pt}{12.0pt}\selectfont
2022/03/01}} \\
\hline
%row no:7
\multicolumn{1}{|p{3.75in}}{v1.1 Proposed Recommendation} &
\multicolumn{1}{|p{0.72in}}{All} &
\multicolumn{1}{|p{0.9in}|}{{\fontsize{10pt}{12.0pt}\selectfont
2022/03/01}} \\
%row no:8
\multicolumn{1}{|p{3.75in}}{v1.1 PR / answer to RFC comments} &
\multicolumn{1}{|p{0.72in}}{All} &
\multicolumn{1}{|p{0.9in}|}{{\fontsize{10pt}{12.0pt}\selectfont
2022/05/23}} \\
\hline
\end{tabular}
\end{table}
\pagebreak
\section{Introduction}
A key role of the VO is to help astronomers find data and to
combine that data in a scientifically meaningful way. A Spectral
Energy Distribution (SED) is an example of combining data whereby
flux density measurements of an astrophysical source at different
spectral energy coordinates (wavelengths/frequencies/energy)
\citep{doi:10.1146/annurev.astro.41.082801.100251,longo,connell,brujine}
are plotted as a
graph of flux density versus a spectral energy coordinate. SEDs that
cover a wide range of the electromagnetic spectrum are particularly
useful for identifying the underlying physical processes operating
in the astrophysical source, and the use of SEDs is becoming more
prevalent as astronomy takes an increasingly multi-wavelength
approach. To combine individual flux density measurements and their
spectral energy coordinates into an SED, these photometric measurements
must be described in sufficient detail to allow for the conversion to
compatible flux density and spectral energy units, taking into
account the nature of the spectral energy bandpass of the measurements,
as well as the apertures and other details of the measurements.
This document outlines a photometry data model to describe photometric
measurements in a standard way.
The photometry data model aims to describe the essential elements
of flux density measurements made within all spectral energy domains
across the electromagnetic spectrum. In some domains this is
relatively straight forward, such as in radio astronomy where
measurements are commonly expressed in flux density units, and
where data are readily combined into SEDs. The data model fields
required to describe such a radio flux density measurement includes
a specification of the bandpass, the units of the measurement and
the associated uncertainties. Optical photometry measurements are
however commonly expressed in magnitudes, and a greater level of
description of the magnitude systems and bandpasses are required
to support the conversion of these measurements into flux densities
that could be combined into an SED. As such, much of this document
is necessarily devoted to defining the data model fields required
to describe optical photometry measurements.
Astronomical flux density measurements will often require a
greater level of description than provided by this simple model.
The level of accuracy required depends strongly on the scientific
use of the data. A study of broadband SEDs of active galaxies may
tolerate 20$\%$ uncertainties in the flux density measurements,
and it is usually sufficient in these cases to use average values
for the spectral energy coordinates of the bandpasses. Fitting to
stellar models or science that employs photometric measurements to
derive photometric redshifts requires a much greater level of accuracy.
To manage the different levels of description we take the overall
approach that the photometry data model should include the most
generic elements required to describe photometric measurements,
and that the photometry data model is intended to be used in
coordination with the IVOA Spectrum Data model and the IVOA
Characterization Data Model.
The scientific use case that has guided the choice of the level
of description of the metadata fields in the Photometry Data Model
is the use of the large collections of photometric data that are
published in catalogues (e.g. Vizier, \url{http://vizier.u-strasbg.fr/})
in SEDs. The Photometry Data Model provides the metadata fields for
describing the photometry measurements in catalogues, so that those
data could then be added to, or compared with an SED.
The intended practical use of the Photometry Data Model is that the
metadata fields defined here will be included in the metadata of
catalogues, or of photometry data stored as a pseudo-spectrum. These
data would then be made accessible using Simple Spectral Access Protocol
(SSAP) or Table Access Protocol (TAP) services so that the photometric
measurements can be used and combined in scientific software tools.
The proposed model is based on the description of the photometry
filters, and the description of how\ the\ units of the measurement
are related to flux density. The photometry filter description may
be as simple as specifying a central spectral energy coordinate and
a bandpass width. The more detailed description of optical bandpasses
is supported by allowing for specification of filter transmission
curves, and the photometric zero points necessary for the conversion
of magnitudes to flux densities.
Information on the properties of filters is not always easily available,
and is sometimes only specified in manuals or in the literature and often
not in digital form. To aid the use of filters information, in particular
as part of the Photometry Data Model metadata fields, we propose a
mechanism for referencing external filter information. Such a
\textit{Filter Profile Service} has been implemented in the Spanish Virtual Observatory
\citep{2012ivoa.rept.1015R}, \citep{2020sea..confE.182R} and exposes this information so software
client applications could discover it.
The following sections of this document summarize some key points
about astronomical photometry (Section 2). The detailed metadata
structure of the data model is presented in Section 3. Section 4
describes use cases in which the model description could be used in
making photometry data available through VO protocols, and, very briefly,
how scientific tools could use this information.
PhotDM is related to other IVOA Data models (Spectrum DM, Characterization DM,
Observation and Provenance DM), and is intended to provide photometry
metadata for data that would be accessed via the IVOA Data Access Protocols
such as SSAP (Simple Spectra Access Protocol) or TAP (Table Access Protocol).
As with most of the VO Data Models, PhotDM makes use of STC, UTypes, Units
and UCDs. PhotDM will be serialisable with a VOTable.
\section{Astronomical Photometry}
Astronomical photometry refers to measuring the brightness, flux or
intensity of an astrophysical object. Consider an astronomical source
with a flux density at the observer $F(x)$, where $x$ is a spectral coordinate
(wavelength, frequency or energy). The photometry measurement will be related
to $<F>$ a flux weighted integral of this flux density over an observed band
with a relative spectral response $T(x)$. The flux weighted integral in its most
simple form is defined as
\begin{equation} \label{eq:1}
\langle F\rangle = \int F(x)T(x)dx
\end{equation}
Calibration of photometric measurements is in general done by comparison to a
reference spectrum that has a known effective flux density $f_0$ at a specific
spectral band.
For this reference spectrum, the flux weighted integral is defined as:
\begin{equation} \label{eq:2}
\langle F_R \rangle= \int F_R (x)T(x)dx
\end{equation}
so that the effective flux density of the source can be evaluated as:
\begin{equation} \label{eq:3}
f = f_0 ( \frac{\langle F\rangle }{\langle F_R \rangle } )
\end{equation}
This represents the most simple and easy to use flux measurement. Flux
measurements expressed in physical flux density units can be easily combined
into SEDs. Many flux measurements published in catalogues of radio sources
for example are simple flux densities of this form.
\par
In optical photometry measurements are often expressed as magnitudes and it
is necessary to define the magnitude system being used, and the zero point
fluxes of the reference spectrum.
\par
Pogson magnitudes are defined as:
\begin{equation} \label{eq:4}
m = -2.5\log_{10} (\langle F\rangle )
\end{equation}
which when compared to a reference spectrum $F_R$ leads to:
\begin{equation} \label{eq:5}
m = m_R -2.5 \log_{10} \left(\langle F\rangle /\langle F_R \rangle \right)
\end{equation}
As explained above, this is equivalent to:
\begin{equation} \label{eq:6}
m = m_R - 2.5 \log_{R} (f/f_0 )
\end{equation}
so that
\begin{equation} \label{eq:7}
f = f_0 10^{-0.4(m - m_{R})}
\end{equation}
Using this expression a measurement in magnitudes can be converted to a
flux density, given the zero point flux of the reference spectrum. The
magnitude of reference $m_{R}$ and the zero point $f_0$ will be defined
in the document. $m_{R}$ is initially chosen to be zero (or one for
linear photometric systems) in most of the photometric systems although,
continuous recalibration of the photometric system could
produce a deviation of this initial value.
\par
There are a number of magnitude systems that are defined by the reference
spectrum. The three most commonly used magnitude systems are the Vega
magnitude, $AB_{\nu }$ magnitude and $ST_{\lambda }$ magnitude systems.
The Vega magnitude system uses the spectrum of Vega (Alpha Lyrae) as the
reference spectrum $F_R (x)$. The $AB_{\nu }$ magnitude system uses
reference spectrum defined by a constant flux density per unit frequency
($F_{\nu }$) and the
$ST_{\lambda }$ magnitude system uses a reference spectrum of a constant
flux density per unit wavelength ($F_{\lambda }$). The values of $F_{\nu }$
and $F_{\lambda }$ that respectively define the zero points
$m_{AB,\nu } =0$ and
$m_{ST,\lambda } =0$ have been chosen to be the mean flux density of
Vega in the Johnson V band.
\begin{equation} \label{eq:7_0}
m_{AB,\nu } = 0
\end{equation}
\begin{equation} \label{eq:8}
f_\nu = 3.63\times 10^{-20}\,erg\,cm^{-2}\,s^{-1}\,Hz^{-1}
\end{equation}
\begin{equation} \label{eq:9}
m_{ST,\lambda } = 0
\end{equation}
\begin{equation} \label{eq:10}
f_\lambda = 3.63\times 10^{-9}\,erg\,cm^{-2}\,s^{-1}\,\angstrom^{-1}
\end{equation}
A convenient graphical representation of these systems is shown in
Figure 3.1 of the Synphot manual:
\url{http://stsdas.stsci.edu/Files/SynphotManual.pdf}.
\par
For a photometric system that uses Vega magnitudes, the zero point
flux for each filter is the average flux density of Vega over that
bandpass $f_{Vega}$. Some typical values of $f_{Vega}$ are tabulated in
\citep{2001eaa..book.....M} for the Johnson photometric system. Although
the agreed Vega spectrum has changed historically, the commonly referred to
spectrum of Vega in digital form described in \citep{2004AJ....127.3508B}
is available as file at \emph{alpha\_lyr\_stis\_010.fits} at:\par
\url{https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/}
\par
In the AB system, the flux density (in units of $erg\, cm^{-2}\, s^{-1}\, Hz^{-1}$)
corresponding to a given magnitude is simply obtained via:
\begin{equation} \label{eq:11}
f_{\nu} = 10^{-0.4(m_{AB}+48.6)}
\end{equation}
And, in the same way, in the ST system, the flux density (in units of $erg\,
cm^{-2}\, s^{-1}\, \angstrom^{-1}$) corresponding to a given magnitude is:
\begin{equation} \label{eq:12}
f_{\lambda} = 10^{-0.4(m_{ST}+21.1)}
\end{equation}
Another magnitude system is the Asinh magnitude system in which magnitudes
are defined as
\begin{equation} \label{eq:13}
m = \frac{2.5}{ln(10)} \left[ sinh^{-1}(\frac{f}{2bf_0}) +ln(b) \right]
\end{equation}
where b is known as the softening parameter. Details of the Asinh magnitude
system and the softening parameters are described in
\url{http://www.sdss.org/DR7/algorithms/photometry.html}
\par
\section{Photometry Data Model} \label{datamodel}
The model shown in Figure~\ref{fig:overview}, organizes the structure and detailed metadata
fields of the Photometry Data Model in a logical manner, and shows the
relationship to other IVOA data models. The metadata fields for each class
specify the essential elements required to describe a photometric measurement.
\par
The main class in this diagram is Photometry Filter. This class contains
all the attributes necessary to describe a filter from the data discovery
point of view.
\par
A Photometric System is a grouping of individual Photometry Filters. This may
represent a particular set of filters that are related in some way.
\par
A magnitude system is characterised for a certain reference spectrum that
will produce a certain zero point for a certain photometry filter. This
reference spectrum could be an ideal one (as in STmag and ABmag systems), a
Vega-like spectrum (as in VegaMag systems) (please notice that different Vega
spectrum versions have been historically used) or any other. In many cases,
the reference spectrum has been calculated as an average of spectra from several
astronomical objects. This would be characterised by a set of Source instances.
\par
A zero point would then be a flux value that can be considered as zero
magnitude, so its value will allow conversions from fluxes to magnitudes
and the other way around. It has associated a photometry filter and it also
depends on the magnitude system (reference spectrum) used to calculate this
magnitude.
\par
There are different types of zero points (Pogson, asinh, linear etc) that will
essentially differ in the way that getFluxFromMagnitude and getMagnitudeFromFlux
operators are implemented plus extra information that could be needed to do
these conversions.
\par
An intermediate class, PhotCal, can be understood as a certain photometry
filter instance, i.e., a certain photometry filter using a certain magnitude
system and linked to a certain zero point class.
%mir as suggested by MCD
PhotDM will interact with Spectral data models (any version) with using PhotCal
as a node. It binds the filter and
zeropoint information to the Flux Axis calibration. It can be understood as the
calibration configuration used, bringing together a specific photometry filter
instance with magnitude system and zero point. It leverages the handling of
photometric data through IVOA protocols e.g. SSAP or TAP services.
\par
A Spectrum would have a Characterization Coordsys element that will have
associated a certain PhotCal element in the case of photometry data. Using
this information, magnitudes from different photometric systems could be
compared between them or compared to spectroscopic data expressed in flux.
\par
%%%%%%%%%%%%%%%%%%%% Figure/Image No: 22 starts here %%%%%%%%%%%%%%%%%%%%
\begin{figure}[H]
\includegraphics[angle=90,width=5.98in,height=7.19in]{./schema/PhotometryOverviewDiagram_20220520.png}
\caption{Overview class diagram of the Photometry Data Model maintained with Modelio 3.8.}
\label{fig:overview}
\end{figure}
\begin{figure}[H]
\includegraphics[angle=0,width=5.98in ]{./schema/BaseDataTypesDiagram_PR_20220520.png}
\caption{Attributes in the PHOTDM classes belong to the base data types defined in the ivoa: VODML template model which is extended here by two classes : UCD for the UCD semantic tag, and ISOTime for time stamps defined and formatted following the DALI specification \citep{2017ivoa.spec.0517D} .}
\end{figure}
%%%%%%%%%%%%%%%%%%%% Figure/Image No: 22 Ends here %%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% Starting New Page here %%%%%%%%%%%%%%
\newpage
%%%%%%%%%%%% Starting New Page here %%%%%%%%%%%%%%
\newpage
In order to fully describe values of the magnitudes inside photometry point
instances, the former PhotDM v1.0 used physical quantity classes which used to
aggregate all the basic fields that compose a physical measurement: value,
error, units, etc. However, within the present specification, we will describe
individual attributes of the different quantities separately.
This is due to the fact that the granularity of quantities as defined in the former PhotDM 1.0
did not match the one proposed in the VODML ivoa template. The new UTypes for PhotDM1.1 are also
generated from these individual attributes to facilitate the use within IVOA Data Access Layer protocols.
\par
\subsection{PhotometricSystem Class}
This class briefly describes the photometric system that contains a set of
photometry filters. Photometry filters can be contained in a certain
photometric system as part of the same observatory/telescope or as part
of a known system.
\par
\subsubsection{PhotometricSystem.description: String}
This String contains a human readable short-text representation of the
photometric system. This will allow client applications to display
textual information to final users.
\par
Examples:
%%%%%%%%%%%%%%%%%%%% Table No: 2 starts here %%%%%%%%%%%%%%%%%%%%
%row no:1
Sloan \par
Johnson
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 2 ends here %%%%%%%%%%%%%%%%%%%%
\subsubsection{PhotometricSystem.detectorType: integer}
Detector type associated to this photometric system. Possible
values are:
%%%%%%%%%%%%%%%%%%%% Table No: 3 starts here %%%%%%%%%%%%%%%%%%%%
\begin{table}[ht]
\centering
\begin{tabular}{p{2.42in}p{0.8in}p{1.55in}}
\hline
%row no:1
\multicolumn{1}{|p{2.42in}}{Type of detector} &
\multicolumn{1}{|p{0.8in}}{Value} &
\multicolumn{1}{|p{1.55in}|}{Examples} \\
\hline
%row no:2
\multicolumn{1}{|p{2.42in}}{Energy Counter} &
\multicolumn{1}{|p{0.8in}}{0 (default)} &
\multicolumn{1}{|p{1.55in}|}{Energy amplifiers devices} \\
\hline
%row no:3
\multicolumn{1}{|p{2.42in}}{Photon Counter} &
\multicolumn{1}{|p{0.8in}}{1} &
\multicolumn{1}{|p{1.55in}|}{CCDs or photomultipliers} \\
\hline
\end{tabular}
\caption{Detector Types}
\end{table}
%%%%%%%%%%%%%%%%%%%% Table No: 3 ends here %%%%%%%%%%%%%%%%%%%%
This will be used in order to decide how to calculate the
flux average in, e.g., the synthetic photometry calculations.
% mir MCD Suggestion
At present, this list is exhaustive.
See the description of the photometry filter
transmission curve ( section \ref{sec:transmissioncurve} to understand how to use this field.
\par
\subsection{PhotometryFilter Class}
This is the main class that describes a photometry filter.
\par
\subsubsection{PhotometryFilter.identifier: String}
This field identifies, in a unique way, within a certain Photometry
Filter Profile service, a filter. Although the main requirement of
this data model field is to be unique within a Filter Profile Service,
the suggested syntax would be:
\par
%%%%%%%%%%%%%%%%%%%% Table No: 4 starts here %%%%%%%%%%%%%%%%%%%%
%row no:1
Facility/Subcategory/Band[/Suffix]
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 4 ends here %%%%%%%%%%%%%%%%%%%%
where \textit{Facility} is the telescope, observatory, space mission,
etc that has this filter, \textit{Subcategory} is a meaningful
classification of filters within a facility (usually instrument),
\textit{Band} is the generic name used to describe the wavelength
band used by this filter and \textit{Suffix} is optional metadata added
to the unique identifier string to ensure uniqueness within a Filter
Profile Service.
\par
Example:
\par
%%%%%%%%%%%%%%%%%%%% Table No: 5 starts here %%%%%%%%%%%%%%%%%%%%
SDSS/SDSS.G/G
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 5 ends here %%%%%%%%%%%%%%%%%%%%
\subsubsection{PhotometryFilter.fpsIdentifier: String}
IVOA identifier of the filter profile service where this photometry
filter is registered to be used in the discovery of all the relevant
photometry filter properties.
\par
This identifier follows the IVOA syntax defined for IVOA
identifiers \citep{2016ivoa.spec.0523D} which gives a string built up as:
\par
%%%%%%%%%%%%%%%%%%%% Table No: 6 starts here %%%%%%%%%%%%%%%%%%%%
ivo://<ivoa authority id>/<resource key>
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 6 ends here %%%%%%%%%%%%%%%%%%%%
Example:
\par
%%%%%%%%%%%%%%%%%%%% Table No: 7 starts here %%%%%%%%%%%%%%%%%%%%
ivo://svo.cab/fps
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 7 ends here %%%%%%%%%%%%%%%%%%%%
where svo.cab is the authority id, fps is the resource key of the service.
\par
Whenever the definition of the Filter Profile Service (FPS) is standardised,
the service url of the filter profile service could be obtained
from the registry by requesting the associated information of this
registry resource, e.g., once registered the service URL associated
to this Filter Profile Service would be, e.g.:
\par
%%%%%%%%%%%%%%%%%%%% Table No: 8 starts here %%%%%%%%%%%%%%%%%%%%
http://svo.cab.inta-csic.es/theory/fps/
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 8 ends here %%%%%%%%%%%%%%%%%%%%
At this stage, only one filter profile service exists so the service
URL would be the previously mentioned.
\subsubsection{PhotometryFilter.name: String}
This String contains a human readable representation of the filter
name. This will allow client applications to display information
to the final user.
\par
Example:
\par
%%%%%%%%%%%%%%%%%%%% Table No: 9 starts here %%%%%%%%%%%%%%%%%%%%
SDSS.G
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 9 ends here %%%%%%%%%%%%%%%%%%%%
\subsubsection{PhotometryFilter.description: String}
This String contains a verbose human readable string description of the
filter. This will allow client applications to display text information
to the final user.
\par
\subsubsection{PhotometryFilter.bandName: String}
This String contains a standard representation of the spectral band
associated to this filter (if any). This information is useful for human
interpretation but it is discouraged to use it for discovery purposes. The
reason is that a filter is not always properly represented by a standard
band so filters could be lost in a query response.
\par
Examples:
\par
%%%%%%%%%%%%%%%%%%%% Table No: 10 starts here %%%%%%%%%%%%%%%%%%%%
U \par B \par V
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 10 ends here %%%%%%%%%%%%%%%%%%%%
Where U,B,V corresponds to ultraviolet, blue and visible respectively.
\par
\subsubsection{PhotometryFilter Time Validity Range}
The following fields will be used to characterize the validity time range of
this specific photometry filter configuration. This is particularly useful
for ground based telescopes where filter, electronics, etc. could easily
change generating versions of the same photometry filter.
Validity time stamps are expressed as ISOTime as specified in DALI \citep{2017ivoa.spec.0517D}
timestamps\par
%%%%%%%%%%%%%%%%%%%% Table No: 11 starts here %%%%%%%%%%%%%%%%%%%%
YYYY-MM-DD[T[hh[:mm[:ss[.s]]]]]
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 11 ends here %%%%%%%%%%%%%%%%%%%%
\paragraph{PhotometryFilter.dateValidityFrom: ISOTime}
Start time of the time coverage when this filter configuration is
applicable.
\paragraph{PhotometryFilter.dateValidityTo: ISOTime}
End time of the time coverage when this filter configuration is
applicable.
\subsubsection{PhotometryFilter.transmissionCurve}
\label{sec:transmissioncurve}
Here we consider how wavelengths/frequencies are filtered in the whole
acquisition chain for a calibrated observation stemming from a given
data collection.
\par
This means that within the same data collection most observations will
point to the same PhotometryFilter.transmissionCurve.
\par
The effective transmission curve may be represented as a 2-D graph that
describes the transmission properties of the filter over a wavelength
range defined by the filter bandpass.
\par
It is composed of a spectral coordinate in the x-axis and a scalar in
the y-axis. This effective response curve encloses all the possible
components that modify the energy/photon collection, including detector,
telescope and even atmosphere for transmission curves referenced in
measurements.
Most modern surveys try to reduce everything according to a given airmass
(e.g. 1.3) and this is particularly important for ground-based filters
with $\lambda < 4000 \angstrom $ or $\lambda > 7000 \angstrom $.
\par
%%%%%%%%%%%%%%%%%%%% Figure/Image No: 3 starts here %%%%%%%%%%%%%%%%%%%%
\begin{figure}[H]
\begin{center}
\includegraphics[width=4.24in,height=3.12in]{./media/image25.png}
\caption{Transmission Curve example}
\end{center}
\end{figure}
%%%%%%%%%%%%%%%%%%%% Figure/Image No: 3 Ends here %%%%%%%%%%%%%%%%%%%%
\par
This curve can be used, e.g. for the creation of synthetic photometry
\citep{1996BaltA...5..459S,2004A&A...422..205G} from an observational
or a theoretical spectrum by applying it to the spectrum in the filter band-pass.
Taking as input a certain flux, the effective flux as seen using a certain
filter would be, for energy counters \citep{2007ASPC..364..227M}:
\begin{equation} \label{eq:14}
f(\lambda_{eff}) = \frac{\int T(\lambda)F(\lambda)d\lambda}{\int T(\lambda)d\lambda}
\end{equation}
And for photon counters (like CCDs or photomultipliers):
\begin{equation} \label{eq:15}
f(\lambda_{eff}) = \frac{\int T(\lambda)F(\lambda)\lambda d\lambda}{\int T(\lambda)\lambda d\lambda}
\end{equation}
Where $T(\lambda)$ is the transmission curve, $f(\lambda_{eff})$ is the flux of
the spectrum. As the transmission curve is defined only in the filter
band-pass, the limits of the integrals corresponds to the spectral range where
the transmission curve is defined (stored as \textit{PhotometryFilter.bandwidth}
in this data model)
\par
The transmission curve can be closely (although not fully) identified as an array
of points as in a spectrum. There are various ways to provide this information
either directly in an embedded table, or using a reference to a serialised table
file.
\par
Spectral and transmission coordinates can be gathered directly as a table using
TransmissionPoint attributes (see \ref{serialisationfilter}). Transmission points
of the curve are stored into a simple table using spectrum data fields with their utypes, ucds and units.
\par
\paragraph{PhotometryFilter.transmissionCurve.access}
If the transmission curve is hooked as an external file, we use the \textit{Access} class
defined in the Observation Core Components data model \citep{2017ivoa.spec.0509L} and inherited
from the SSA specification \citep{2012ivoa.spec.0210T}.
\par
\subparagraph{PhotometryFilter.transmissionCurve.access.reference}
The access reference is a URI (typically a URL) which can be used to retrieve the
specific dataset described in a row of the query table response. \par
\subparagraph{PhotometryFilter.transmissionCurve.access.format}
The PhotometryFilter.transmissionCurve.access.format data model field tells the MIME
type of the file pointed to and used to store the curve points. Values for this
string can generally be:\par
%%%%%%%%%%%%%%%%%%%% Table No: 13 starts here %%%%%%%%%%%%%%%%%%%%
application/fits \par
application/x-votable+xml \par
text/csv \par
text/xml
\bigskip
%%%%%%%%%%%%%%%%%%%% Table No: 13 ends here %%%%%%%%%%%%%%%%%%%%
The file content will be a spectrum serialisation with
\textit{PhotometryFilter.transmissionCurve.spectrum.Dataset.DataModel} set to
$``$Spectrum1.1$"$ for instance, and all necessary fields for the spectral and
flux coordinates.
\par
\subparagraph{PhotometryFilter.transmissionCurve.access.size}
Approximate estimated size of the dataset, specified in kilobytes. This would
help the client estimate download times and storage requirements when generating
execution plans. Only an approximate, order of magnitude value is required (a value
rounded up to the nearest hundred kB would be sufficient).\par
\paragraph{PhotometryFilter.transmissionCurve.transmissionPoint}
The transmission curve is a mathematical function that describes the transmission
fraction of a certain filter in a defined spectral range. This function can be discretised
as a set of transmission points and every point will be composed by five attributes:
\par
\begin{itemize}
\item{spectralValue, type real, with the spectral coordinate (wavelength, energy or frequency) value}
\item{spectralErrorValue, type real, with the error on the spectral coordinate}
\item{unit, type Unit, with the unit used for the spectralValue. This is also linked to the type of spectral coordinate used}
\item{ucd, type UCD, used to indicates the type of spectral coordinate from the ontological point of view}
\item{One transmissionValue, unitless real, with a value between 0 and 1}
\end{itemize}
One example for PhotometryFilter.transmissionCurve.transmissionPoint.ucd could be, for example:
\par
%%%%%%%%%%%%%%%%%%%% Table No: 14 starts here %%%%%%%%%%%%%%%%%%%%
em.wl
%%%%%%%%%%%%%%%%%%%% Table No: 14 ends here %%%%%%%%%%%%%%%%%%%%
Where \textit{em.wl }indicates that the spectral coordinate is provided in wavelength.
\par
The Unit and UCD strings follow specific constraints defined in the IVOA standards and are
implemented using type restrictions on strings.
\par
\subsubsection{PhotometryFilter.spectralLocation.value: real}
A spectral coordinate value that can be considered by the data provider as the
most representative for this specific filter band-pass. The selection of this
value should take into account the filter transmission curve profile and in
general should be close to the wavelength mean value, defined
in \citet{1982AJ.....87..670O} as:
\par
\begin{equation} \label{eq:16}
\lambda_{mean} = \frac{\int T(\lambda)\lambda d\lambda}{\int T(\lambda)d\lambda}
\end{equation}
where $\lambda_{mean}$ is the spectral bounds mean value, $T(\lambda)$ is
the transmission curve (see below), $\lambda$ is the wavelength. Please
notice that, since the transmission curve will only be defined in a specific
spectral range, the integrals will also be effectively defined in this
spectral range.
\par
Another convenient definition of an effective wavelength is the
$``$pivot wavelength$"$ defined as follows:
\begin{equation} \label{eq:17}
\lambda_{pivot} = \sqrt{\frac{\int T(\lambda)\lambda d\lambda}{\int T(\lambda)\frac{d\lambda}{\lambda}}}
\end{equation}
It can be proved that the pivot wavelength fulfills the following
relation between the $f_\lambda$ and $f_\nu $:
\begin{equation} \label{eq:18}
\langle f_\nu \rangle =\langle f_\lambda \rangle \lambda^2_{pivot}/c
\end{equation}
Other definitions for effective wavelengths commonly used in the literature
are source dependent as, e.g., the isophotal wavelength:
\begin{equation} \label{eq:19}
\lambda_{mean} = \frac{\int \lambda F_\lambda(\lambda)T(\lambda)d\lambda}{\int F_\lambda(\lambda)T(\lambda)d\lambda}
\end{equation}
Or the photon distribution based effective wavelength:
\begin{equation} \label{eq:20}
\lambda_{mean} = \frac{\int \lambda^2 F_\lambda(\lambda)T(\lambda)d\lambda}{\int \lambda F_\lambda(\lambda)T(\lambda)d\lambda}
\end{equation}
but these source dependent definitions have two caveats:
\begin{itemize}
\item{Real spectra do not necessarily satisfy the requirements of the mean value theorem,
which could produce multiple values for the wavelength.}
\item{Calculation of these wavelengths implies the knowledge of $F_\lambda $ (usually
what you want to measure) and it does not look like an intrinsic property of the
photometry filter.}
\end{itemize}\par
\subsubsection{PhotometryFilter.bandwidth: Bandwidth}
A reference range along the spectral axis coverage of the referenced photometry filter.
\par
The basic elements of this object are described
within the context of a photometry filter as follows.
\par
\paragraph{PhotometryFilter.bandwidth.UCD: String}
Unified Content Description (UCD) string that specifies the nature of the bandwidth object.
\par
\paragraph{PhotometryFilter.bandwidth.unit: IVOA.Unit}
Field that specifies the units of the bandwidth object.
\par
\paragraph{PhotometryFilter.bandwidth.extent: real}
For square filters (100$\%$ between the minimum and maximum wavelength and 0$\%$ otherwise),
the bandwidth could be described as $\lambda_{max} - \lambda_{min}$.
\par
However, for real filters, the bandwidth is not very usable to describe the band-pass of the
filter, but the effective width, that can be described as follows:
\begin{equation} \label{eq:21}
w = \frac{\int T(\lambda)d\lambda}{Max(T(\lambda))}
\end{equation}
where $w$ is the effective width, $T(\lambda)$ is the transmission curve (see below)
and $Max(T(\lambda))$ the maximum value of the transmission curve. As in previous points,
please notice that, since the transmission curve will be only defined in a specific spectral
range, the integrals will also be defined in this spectral range.
\par
\paragraph{PhotometryFilter.bandwith.start: real}
%mir suggestion from MCD review
Also called $\lambda_{min}$ in the rest of the document, this is a spectral value that better describes
the reasonable minimum value of the spectral range of the filter band-pass. In general,
although this
will not be imposed in order to allow a better description for different types of
transmission curves, this quantity will be close to:
\begin{equation} \label{eq:22}
%mir suggestion from MCD review
\lambda_{min} = \lambda_{mean} - \frac{w}{2}
\end{equation}
In practice, this could be taken as the minimum value of the filter transmission curve.
\par
\paragraph{PhotometryFilter.bandwith.stop: real}
Also called $\lambda_{max}$ in the rest of the document, this is a spectral value that
better describes the reasonable maximum value of the spectral range of the filter band-pass.
In general,
although this will not be imposed in order to allow a better description for different
types of transmission curves, this quantity will be close to:
\begin{equation} \label{eq:23}
\lambda_{max} = \lambda_{mean} + \frac{w}{2}
\end{equation}
In practice, this could be taken as the maximum value of the filter transmission
curve.\par
\subsection{PhotCal Class}
PhotDM is a class to describe the use of a photometry filter by using a certain magnitude system
configuration. It has associated a certain zero point object.