Spektrophotometrische Farb-Kalibrierung
Warnung
Die Kalibrierung der Farben durch Photometrie muss unbedingt an einem linearen Bild durchgeführt werden, dessen Histogramm noch nicht gestreckt wurde. Andernfalls wird die photometrische Messung falsche Ergebnisse liefern und es gibt keine Garantie für die Korrektheit der erhaltenen Farben.
Spectrophotometric Color Calibration (Ctrl + Shift + C) is the newest method of color calibration available in Siril. This method uses the extensive spectral data available in the Gaia DR3 catalogue [GaiaDR3]. This can be accessed either through direct querying of the online catalogue or by downloading a local extract and querying the local catalogue.
Warnung
Beachten Sie, dass die SPCC-Funktionalität von Siril über den Remote-Katalog nicht verfügbar ist, wenn das Gaia-Archiv aufgrund von Wartungsarbeiten oder einer Störung offline ist. Glücklicherweise ist das Archiv normalerweise sehr zuverlässig, jedoch ist eine Statusanzeige in den SPCC-Dialog integriert. Der Archivstatus wird beim Start des Dialogs überprüft und kann durch Klicken auf die Status-Schaltfläche erneut überprüft werden.
The offline Gaia SPCC extract will still work fine if the Gaia archive is offline.
Dialogfenster Spektro-Photometrische Farbkalibrierung.
Tipp
Was ist der Unterschied zwischen SPCC und PCC? Wann soll man die Eine oder die andere Variante benutzen? SPCC ist eine genauere Version der PCC und macht letztere überflüssig. SPCC berücksichtigt den Sensor und die Filter Ihres Equipments. Dadurch ist die erzeugte Farbe viel näher an der "Realität". Das Beispiel in der Abbildung unten veranschaulicht den Unterschied in den Ergebnissen.
Vergleich zwischen PCC (links) und SPCC (rechts): Zum Vergrößern anklicken. (Mit freundlicher Genehmigung von Ian Cass)
Local SPCC Catalog
From 1.4.0 an offline SPCC catalog is available using Gaia DR3 data. Note that the catalog is chunked into 48 files covering each level 1 HEALpixel.
Theorie
HEALpix (Hierarchical Equal Area isoLatitude Pixelisation) is an algorithm for pixelising a sphere based on subdivision of a distorted rhombic dodecahedron. Mathematical details can be found on Wikipedia [Wiki_HEALPIX]. Gaia sources use a Level 12 NESTED HEALpix scheme and the HEALpixel number is encoded into the source_id. The specification of the Gaia DR3 catalogue extracts and their file format is documented here (PDF).
The nested nature of the scheme means that HEALpixels that are close together in the sky have numbers that are close together. The hierarchical property also means that it is possible to index sources in HEALpixels at a deep HEALpixel level and divide the catalog into chunks at a shallower level while still supporting a highly efficient catalog search algorithm.
It is possible to download the entire catalog or only the chunks you need. The folder location to store the catalog files is set in .
Sirilpy Skript
The easiest way to install the catalog is to use the built-in Python script
Siril_Catalog_Installer.py in the menu.
This provides an interface that allows you to install either the whole catalog, or only the chunks that are visible from your observing latitude above a certain elevation, or only sets of chunks corresponding to certain themes (Milky Way, Summer Triangle, Galaxy Season etc.) Select the latitiude / elevation or area of interest if desired, and then select the selection method (All, Visible from Latitude or Area of Interest).
You can preview the coverage using the Preview coverage button.
Finally, clicking Install will download, verify, uncompress and install the selected chunks and also set the catalog path in Siril's preferences. A default catalog path is suggested in the text entry widget, but can be changed to a different location if you prefer.
If you wish to install the offline SPCC catalog files manually, they can be downloaded from Zenodo. Either individual level 1 HEALpixels can be downloaded or the entire catalog can be downloaded as an archive.
Tipp
When you download "All Files" from the Zenodo record, the download is a zip archive that you will need to extract, however the zip archive is just a convenient way of bundling all of the individual files; the data files inside the zip archive are themselves compressed with bzip2 compression, and you will need to decompress the individual .bz2 files before Siril can use them. Support for this compression format is available by default in Linux and MacOS, and is provided in Windows by various archive programs including 7-Zip and Pea-Zip, which are both Free and Open Source software.
All compressed files have accompanying sha256sums and there is a file containing all the sha256sums of the uncompressed files as well, for additional validation. The Zenodo record also provides a DOI reference that can be used to cite the dataset if you use it in academic work.
Siril uses an optimized extract of the Gaia DR3 xp_sampled datalink product. As with the astrometric extract, the offline catalogue is capped at the 127 brightest sources per level 8 HEALpixel. The catalogue contains fewer sources than the astrometric extract as xp_sampled spectra are typically only provided for sources brighter than magnitude 17.6 and therefore more HEALpixels in emptier parts of the sky have fewer than 127 sources compared with the astrometric extract (i.e. these HEALpixels contain all the available Gaia DR3 sources with xp_sampled data), but this approach still avoids overpopulation of the catalogue in extremely crowded parts of the sky while providing the best SNR. In those HEALpixels with fewer than 127 xp_sampled sources, the local catalog is as comprehensive as using the online Gaia archive directly.
The xp_sampled is converted from float32 to float16 data with an additional byte setting the exponent to be applied to the xp_sampled data for the source to overcome limitations on exponents expressible with float16. This is entirely justifiable given the error bars on the xp_sampled data and makes no practical difference to the accuracy of the results. It means that we can provide a highly effective, purpose-optimized local SPCC catalogue in under 21GB of data.
How it Works
SPCC erfordert Kenntnisse über Ihren Sensor und die von Ihnen verwendeten RGB-Filter. Diese werden über ein Online-Repository bereitgestellt, das Siril entweder automatisch beim Start oder bei Bedarf manuell synchronisiert. Sensor- und Filterinformationen werden über dieselbe Synchronisierungsmethode aktualisiert, die auch für das Online-Skript-Repository verwendet wird. (Dies bedeutet, dass Daten zu neuen Filtern oder Sensoren, sobald sie verfügbar sind, dem Repository hinzugefügt werden können, ohne dass eine Aktualisierung der Anwendung erforderlich ist.)
In the GUI you select your sensors and filters from the widgets in the SPCC dialog. Don't worry if there isn't an exact match for your equipment, just pick the closest option, or the appropriate default option. You also need to select a white reference. The default reference is the Average Spiral Galaxy reference which is suitable for a wide range of astrophotographic scenes, however there is an extensive range of galaxy and star types to choose from. The Sun's spectral type is G2(v) so if you want to balance your image using sunlight as a white reference, you would pick Star, type G2(v) from the list.
SPCC then uses the stellar spectra in Gaia DR3 and knowledge of your imaging sensor and filters to compute for each star in the catalogue that matches a star detected in the image by Siril the expected flux in each color channel. It then compares this with the actual flux measured in each channel using Siril's photometric capabilities.
Given the sensor and filter knowledge, SPCC computes the expected flux in each channel for the specified white reference. A robust linear fit is obtained to give the best fit of catalogue to image R/G and B/G flux ratios for each star and for the white reference. This fit is used to derive correction coefficients which are applied multiplicatively to each channel, resulting in spectrophotometrically accurate color channels.
Your image must be plate solved for SPCC to work: if it is not already, this should be done with the dedicated tool. It is important to make sure that the plate solving information is correct, as some software is known to add inaccurate WCS data to images.
Grafische Benutzeroberfläche
Selection of Sensor In order to select your sensor, ensure that the mono / OSC toggle button is set correctly. You will then see the appropriate dropdown to choose from the available sensors.
Selection of Filters SPCC can operate in two modes.
The default mode is broadband operation. In this mode, the Narrowband mode check box should be unchecked. You can choose either red, green and blue filters (for composited images made with a mono sensor) or OSC filters, for example light pollution filters, for images made with an OSC sensor.
Warnung
If you select a DSLR OSC sensor (e.g. a Canon EOS 600D) an additional widget will become visible to select a DSLR Low Pass Filter. This allows you to tailor whether your camera has been astro-modded or not. You must select an option here or the process will complain that you haven't set all the necessary filters!
Options exist for Canon and Nikon OEM low-pass filters as well as the popular Baader BCF astro-mod filter that lets Ha and Sii through but stil blocks longer IR wavelengths and "Full spectrum" which is modelled as a perfect clear filter.
If you have an unmodified camera of a different model or brand, select any of the Canon or Nikon low-pass filters: the effect is very minor as these wavelengths are right at the edge of human visual perception anyway.
By checking the Narrowband mode check box, you enable narrowband mode. This is intended either for images composited from narrowband filters used with a mono sensor or for images made using an OSC sensor with a dual, tri-band or quad band narrowband filter. In this mode the available controls change, and for each color channel you enter the nominal wavelength and bandwidth of the filter passband. For ultra-narrowband mono filters the passband may be as little as 3nm; for a quadband OSC filter like the Altair QuadBand V2 the passbands may be as much as 35nm. Note that for a HOO composition where two channels are set to the same data, the nominal wavelength and bandwidth should be set equal in the SPCC interface too.
Kalibriertes HOO-Bild (Bildquelle: Cyril Richard).
Tipp
Some manufacturers specify a center wavelength and FWHM. It is fine to use the FWHM as the bandwidth: these filters have very sharp cutoffs.
Warnung
Erwarten Sie nicht, dass Sie die Hubble-Palette für SHO-Abbildungen mit den Wellenlängen der Filter SII, \(\mathrm{H}\alpha\) und OIII abrufen können. Das Ergebnis wird ein Bild mit einem starken Grünstich sein. Dies lässt sich leicht dadurch erklären, dass die SII-Emissionslinie viel schwächer ist als die des Wasserstoffs und das SPCC eine Darstellung der tatsächlichen Intensitäten liefert. Dies ist bei der Hubble-Palette jedoch nicht der Fall. Mit der manuellen Farbkalibrierung werden bessere Ergebnisse erzielt.
SHO image calibrated by SPCC compared to the same, manually calibrated one. The entire nebula was taken as a white reference during manual calibration. Image by Cyril Richard.
Selection of DSLR Low Pass Filter (LPF) DSLRs contain a low-pass filter (sometimes also called a 'hot mirror'. These reduce transmittance at wavelengths of interest to astronomers (Ha at 656nm and S-II at 674nm). If the selected OSC is a DSLR, a dropdown will be provided from which you can the appropriate LPF profile. Options exist for stock LPFs as well as astro-modified LPFs and an ideal Full spectrum filter model for if the LPF has been removed altogether.
Selection of White Reference SPCC requires an absolute white reference spectrum. The default is Average Spiral Galaxy and the source spectra used to create this white reference are taken from the SWIRE templates [SWIRE] in a manner consistent with other astrophotography software providing the same white reference. A wide range of other white references is available, covering the full range of galaxy and star classifications [Stellar]. If you wish to use sunlight as your white reference, you would choose the white reference Star, type G2(v) as the Sun is a type G2(v) star.
Graphs showing white reference data from spiral galaxies. At around 350 nm, the Average Spiral Galaxy data become identical to the Sc galaxies, which are also a good representation of the white reference.
NGC 4414 is a great example of a Sc-type galaxy, the type closest of the average spiral galaxy used as white reference by default. Image Credit: NASA, ESA, W. Freedman (U. Chicago) et al, & the Hubble Heritage Team (AURA/STScI), SDSS; Processing: Judy Schmidt.
Tipp
Summary of Stellar Spectral Classifications Stellar classifications have two parts, a Morgan-Keenan type and a Luminosity index.
The first part of the spectral classification (G2 in the case of the Sun) takes one of the following letters: O, B, A, F, G, K, M. O represents extremely hot blue stars, while M represents cool red stars. The sun is roughly in the middle of the spectrum. The number represents intermediate cases, for example a B5 star is halfway between type B and type A.
The second part of the spectral classification is the luminosity index ranging from i to v. Stars with luminosity index i are supergiants, whereas stars with luminosity index v are dwarfs. Main sequence stars such as the sun have a luminosity index of iv.
Graphs showing white reference data for a set of two different star classes, G and K.
Difference in color calibration depending on the choice of white reference. On the left, an M-type star, on the right the average spiral galaxy. Please note that the data are linear, and only an autostretch has been applied to the visualization.
Atmospheric Correction Siril's SPCC supports atmospheric correction. When imaging from Earth we image through the atmosphere. This does not have perfect transmittance and therefore acts as another, non-optional, filter in the imaging chain between the sensor and the astronomical object. Whether or not to correct for this is an artistic choice each astrophotographer must make, but the option is provided.
Theorie
Atmospheric extinction arises from several sources. The most important are:
Rayleigh scattering. This is the elastic scattering of light by particles that are small compared with the wavelength of light. The strong wavelength dependence of the Rayleigh scattering (\(\approx λ^{−4}\)) means that shorter (blue) wavelengths are scattered more strongly than longer (red) wavelengths.
Aerosol scattering. This is scattering of light by particles that are larger than the wavelength of light. This is quite variable but (in the absence of significant short term dust or smoke effects) relatively spectrally flat and less significant than Rayleigh scattering.
Molecular absorption lines.
Siril models only Rayleigh scattering. This is the most important contribution in most atmospheric conditions, and is highly predictable making it easy to model without requiring the user to provide complex data.
The formula for the Rayleigh transmittance of the atmosphere as a function of wavelength \(\lambda\) nm, observer height \(H\) m and pressure \(p\) hPa is:
\(\tau_R(\lambda, H, p) = \left( \frac{p}{1013.25} \right) \left( 0.00864 + 6.5 \times 10^{-6} \cdot H \right) \lambda^{-(3.916 + 0.074 \lambda + \frac{0.050}{\lambda})}\).
Under normal circumstances aerosol scattering has a roughly flat response in the visible region. This changes in specific conditions, for example when there is high atmospheric smoke particle concentration after wildfires or, in parts of Europe, when Saharan dust is carried into the atmosphere. However these effects are very difficult to model accurately as they depend on the concentration of sand or smoke particles in the atmosphere at the time. Siril therefore does not model this effect.
The main molecular absorption lines in the visible spectrum are the Chappuis stratospheric ozone bands and the Fraunhofer B molecular oxygen absorption line. However the Fraunhofer B line is very narrow and does not have a significant effect on overall calibration. The Chappuis bands are broad but with a low peak absorption, with a much smaller overall impact than Rayleigh scattering. Molecular absorption bands are not currently modelled in Siril.
When selecting the Atmospheric correction check box, the following options become available:
Observer Height. This allows setting of the observer height, which is used in the Rayleigh extinction calculation. Set this to the altitude of your observatory above sea level. Some capture software sets the FITS header
SITEELEVcard: if this is present, the height from this card will be used, otherwise the value is editable and defaults to 10 m.Atmospheric pressure. This allows setting atmospheric pressure at the time of observation. For convenience it can be specified as sea level pressure (as provided by weather forecasts) or as local pressure (as measured by a barometer at the observatory). In case you are unsure, the default is standard atmospheric pressure at sea level (1013.25 hPa).
Theorie
If the pressure is provided as a sea-level pressure measurement, the local pressure at the observer's height is calculated according to the barometric formula:
\(P(h) = P_0 \left( 1 - \frac{L h}{T_0} \right)^{\frac{g M}{R L}}\),
wobei:
\(L = 0.0065~\text{K}/\text{m}\) (Temperature lapse rate),
\(T_0 = 288.15~\text{K}\) (Sea level standard temperature),
\(g = 9.80665~\text{m}/\text{s²}\) (Acceleration due to gravity),
\(M = 0.0289644~\text{kg}/\text{mol}\) (Molar mass of Earth's air),
\(R = 8.3144598~\text{J}/(\text{mol}·\text{K})\) (Universal gas constant).
Airmass. This is not an editable parameter but shows the airmass that will be used in the calculations. It is obtained, in order of preference, from the
AIRMASSFITS header card; by calculation using theCENTALTFITS header card; or as a last resort by using the average zenith angle of all parts of the more than 30° above the horizon. The tooltip shows which source the used figure is based on.Theorie
If the
AIRMASSheader is unavailable the calculation used to derive airmass from zenith angle is calculated in accordance with [Young1994]:\(X(z) = \frac{1.002432 \cos^2 z + 0.148386 \cos z + 0.0096467}{\cos^3 z + 0.149864 \cos^2 z + 0.0102963 \cos z + 0.000303978}\).
The interface allows you to view details of the selected sensor, filter and white reference using the Details button next to each combo box. From the details information box that this brings up you also have the option to plot the Quantum Efficiency (for sensors) or transmittance (for filters) or relative photon count (for white references) against wavelength. A Plot All button is also available in the main SPCC dialog which allows you to see the responses of all your filters and your sensor and the white reference spectrum all plotted together.
Plotting all the responses of all your filters and your sensor and the white reference spectrum all plotted together
When you are happy, click Apply and SPCC will run. It will cache catalogue data but the first time you apply it to an image it will take a few seconds to perform the online catalogue searches and retrieve the source and spectral data. SPCC will then be applied to the image. Additional plots showing the linear fit of the catalogue Red / Green and Blue / Green to image Red / Green and Blue / Green ratios.
By default, Siril outputs graphs showing the fits used in the process. In this example the magnitude was limited to 17.
Tipp
How do I process L-RGB images? We recommend processing only RGB with SPCC. The L layer must be added at a later stage, when the histograms have been stretched.
Tipp
Für Bilder, die mit einem OSC-Sensor aufgenommen wurden, empfehlen wir die Verwendung von Bayer Drizzle zur Wiederherstellung der Bildfarben. Dies gewährleistet genauere Farben, wie im folgenden Bild gezeigt wird.
SPCC in identischer Weise auf dasselbe Bild angewendet. Links: konventionelles Demosaicing mit dem VNG-Algorithmus; rechts: Bayer-Drizzle-Technik. Auf dem konventionell debayerten Bild ist ein dominanter Grünton deutlich sichtbar. **Der VNG-Algorithmus wurde für dieses Beispiel gewählt, weil die hier erläuterten Effekte stärker ausgeprägt sind. In Siril ist der Standard-Demosaikierungsalgorithmus jedoch RCD. Klicken Sie zum Vergrößern auf das Bild.
SPCC-Filter- und Sensordatenbank
Daten konvertieren
Für die Datenbank wird das JSON-Format (ein schlankes Datenaustauschformat abgeleitet von der Notation für JavaScript-Objekte) benutzt. Wir empfehlen, mit einer vorhandenen Datei aus der Datenbank zu beginnen, die Ihren Anforderungen entspricht, und diese unter dem Namen Ihres Sensors oder Filters zu speichern. Sie können dann einfach die Werte in der Datei durch die von Ihnen erhaltenen Daten ersetzen.
Geben Sie im Feld
WellenlängeIhre Wellenlängenmessungen ein. Stellen Sie sicher, dass das FeldEinheitenauf einen der folgenden Werte eingestellt ist:Angström,nm,Mikrometeroderm.Geben Sie im Array
WerteFolgendes ein:Durchlasswerte für Filter
Quanteneffizienzwerte für Sensoren
Legen Sie das Feld
Bereichentsprechend Ihrer Datenskala fest (z.B.Bereich: 100, wenn es sich bei Ihren Werten um Prozentwerte handelt,Bereich: 1, wenn sie auf 1 normalisiert sind).
Wie man selbst beitragen kann
Die SPCC-Datenbank dient zum Speichern von JSON-Dateien von OSC/monochromen Sensoren und Filtern, die auf dem Markt erhältlich sind. Ihr Hauptziel ist das Sammeln umfangreicher Daten und die Förderung der Zusammenarbeit innerhalb der Community.
Wir legen großen Wert auf Beiträge aus der Community und ermutigen zur aktiven Teilnahme. Wir benötigen Daten, die idealerweise von 300 nm bis 1100 nm reichen. Mithilfe von Softwaretools können Kurven/Diagramme aus dem Internet extrahiert werden. Sie können sich auch direkt an die Hersteller wenden, um Daten anzufordern.
Read this page and help us by contributing.
Wichtig
Wir schließen keine Schmalbandfilter ein. Diese hochspezifischen Filter werden in Siril synthetisiert, was Präzision gewährleistet. Dies gilt auch für Duo-Schmalbandfilter.
Formatreferenz für das JSON-Dateiformat
Hier ist die Vorlage für die in der SPCC-Datenbank verwendeten JSON-Dateien:
[
{
"model": "sensor model / filter set",
"name": "sensor / filter name",
"type": "MONO_SENSOR | OSC_SENSOR | MONO_FILTER | OSC_FILTER | OSC_LPF | WB_REF",
"dataQualityMarker": 1 - 5,
"dataSource": "Describe where the data came from",
"manufacturer": "Manufacturer name",
"version": 1,
"channel": "RED | GREEN | BLUE | LUM",
"wavelength": [Comma separated array of wavelengths],
"values": [Comma separated array of values]
}
]
Wichtige Hinweise
Definition des Feldes
dataQualityMarker:Daten unbekannter Herkunft. Nicht akzeptiert für das Siril-SPCC-Datenbank-Repository.
Von OEMs oder anderen namhaften Anbietern im Bildformat gescannte Daten.
Tabellarische Daten mit niedrigerer Auflösung, die vom OEM bereitgestellt werden, oder akademische Daten in Bezug auf die ideale Standardfilterdurchlässigkeit (z. B. generische Standard-Photometriefilter).
Tabellarische Daten mit hoher Auflösung (nicht mehr als 2nm Abstand), vom OEM bereitgestellt.
Daten, die spezifisch für Ihren eigenen Filter sind, den Sie persönlich mit geeigneter Ausrüstung kalibriert haben. Dies ist der höchstmögliche Qualitätsmarker und wird niemals an .json-Dateien im Repository vergeben, die immer nur allgemein für ein Ausrüstungsmodell und nicht spezifisch für Ihr individuelles Ausrüstungselement sein können. Beachten Sie, dass die tatsächliche Qualität dieser Daten vollständig von der Qualität Ihrer Kalibrierungsausrüstung abhängt – das alte Sprichwort „Müll rein, Müll raus“ gilt.
Die Anforderungen an den Modellnamen
model:Muss für alle zugehörigen JSON-Objekte in einem Set identisch sein
Beispiele:
RGB Filtersatz:
"model": "Chroma RGB"OSC Sensor:
"model": "ZWO ASI2600MM"
Das Feld Kanal
channel:Nur erforderlich für
"type": "OSC_SENSOR"oder"type": "MONO_FILTER"Für OSC-Sensoren fügen Sie ein JSON-Objekt pro Kanal ein (
RED,GREEN,BLUE)Bevorzugte Reihenfolge der Kanäle:
RED,GREEN,BLUE
Die Anforderungen des Array
wavelength(Wellenlänge):Minimale Abdeckung: 380nm bis 700nm
Maximal sinnvoller Bereich: 336nm bis 1020nm (Gais DR3 Spektralgrenzen)
Die Werte müssen monoton steigend sein
Doppelte Werte sind nicht erlaubt
Es müssen bestimmte Einheiten verwendet werden (
angstroms,nm,micrometres,m).
Bemerkung
Wenn Ihre Sensordaten nur bis 400nm reichen (was bei einigen Herstellern üblich ist), ist es akzeptabel, einen einzelnen Punkt bei 380nm zu extrapolieren. Die Sensorreaktion unter 400nm folgt bei verschiedenen Sensoren normalerweise einem vorhersehbaren Muster. Das Hinzufügen dieses extrapolierten Punkts bei 380nm ist besser, als die Kurve bei 400nm enden zu lassen, was die Empfindlichkeit unter 400nm effektiv als Null behandeln würde. Die Auswirkung dieser Extrapolation ist minimal, da die CIE 1931-Empfindlichkeit in diesem Wellenlängenbereich sehr gering ist.
Die Anforderungen des Arrays
values(Werte):Für Filter: enthält Transmissionswerte
Für Sensoren: enthält Quanteneffizienzwerte
Legen Sie einen geeigneten
Bereichswert,rangefest (z. B. 100 für Prozentsätze).Siril skaliert alle Werte intern auf einen Bereich von 0,0 - 1,0.
Gespeicherter Einstellungen
As most users are likely to do most of their imaging with one setup, or maybe two, it would be tedious to reselect the sensor and filters each time. The user choices are therefore automatically remembered when set and restored next time the tool is used, even if Siril is closed and restarted in between. This works using the preferences system but there is no need to use the preferences dialog to remember the set sensor and filters, it is done automatically.
The chosen white reference is not remembered: the default Average Spiral Galaxy is a suitable choice for most astronomical scenes, and alternative white references would normally be set for a specific image to draw out a particular aspect of the color.
Siril Kommandozeile
spcc [-limitmag=[+-]] [ { -monosensor= [ -rfilter= ] [-gfilter=] [-bfilter=] | -oscsensor= [-oscfilter=] [-osclpf=] } ] [-whiteref=] [ -narrowband [-rwl=] [-gwl=] [-bwl=] [-rbw=] [-gbw=] [-bbw=] ] [-bgtol=lower,upper] [ -atmos [-obsheight=] { [-pressure=] | [-slp=] } ]
spcc_list { oscsensor | monosensor | redfilter | greenfilter | bluefilter | oscfilter | osclpf | whiteref }
Quellenverzeichnis
Vallenari, A., et al. "Gaia Data Release 3-Summary of the content and survey properties." Astronomy & Astrophysics 674 (2023): A1. 99(613), 191.