PassbandWhat is a passband?Defines the probability of an incoming photon of a particular wavelength being passed through. (I concentrate here on modelling just the passband itself, not other measurement characteristics such as rest frame).FiltersThe simplest example is that of a red filter on an optical telescope; in principle any red photon (~ 5 x 10^12Hz ?) arriving is let through, and any other photons are blocked. In practice of course it's not that simple. No filter is perfect - in the above example, some red photons will be absorbed by the filter, and some other ones might get through. Also some photons are more red than others, and the filter will not be even handed; photons of one redness will be more or less likely to pass than a photon of another redness: Example Finally a filter might not be completely even across its width.SensitivityAn instrument's sensitivity also defines a passband. This might be tuned (radio) or part of the physical properties (CCDs).Instrument, Atmosphere | ||||||||
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< < | Other factors influence the probability of a photon being measured - the full chain is given in the Observation Data Model doc (2.8) | |||||||
> > | Other factors influence the probability of a photon being measured - the full chain is given in the Observation Data Model doc (2.8) | |||||||
- depending on where 'incoming' is defined. We can model this set of sequential passbands using a ChainedPassband
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How are we going to model it?I first present the public interface that the Passband object presents to the models that use it, and then how various Passband objects might be defined.Public InterfaceA passband presents the following to the world:
Implementations
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PassbandWhat is a passband?Defines the probability of an incoming photon of a particular wavelength being passed through. (I concentrate here on modelling just the passband itself, not other measurement characteristics such as rest frame).FiltersThe simplest example is that of a red filter on an optical telescope; in principle any red photon (~ 5 x 10^12Hz ?) arriving is let through, and any other photons are blocked. In practice of course it's not that simple. No filter is perfect - in the above example, some red photons will be absorbed by the filter, and some other ones might get through. Also some photons are more red than others, and the filter will not be even handed; photons of one redness will be more or less likely to pass than a photon of another redness: | ||||||||
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< < | Example - Has anyone got a better example? | |||||||
> > | Example | |||||||
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< < | Finally a filter is unlikely to be completely even across its width. | |||||||
> > | Finally a filter might not be completely even across its width. | |||||||
SensitivityAn instrument's sensitivity also defines a passband. This might be tuned (radio) or part of the physical properties (CCDs).Instrument, AtmosphereOther factors influence the probability of a photon being measured - the full chain is given in the Observation Data Model doc (2.8) - depending on where 'incoming' is defined. We can model this set of sequential passbands using a ChainedPassbandReferencesHow are we going to model it?I first present the public interface that the Passband object presents to the models that use it, and then how various Passband objects might be defined.Public InterfaceA passband presents the following to the world: | ||||||||
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-- MartinHill - 21 May 2004
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PassbandWhat is a passband? | ||||||||
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< < | Defines the probability of an incoming photon of a particular wavelength being measured. | |||||||
> > | Defines the probability of an incoming photon of a particular wavelength being passed through. | |||||||
(I concentrate here on modelling just the passband itself, not other measurement characteristics such as rest frame).
FiltersThe simplest example is that of a red filter on an optical telescope; in principle any red photon (~ 5 x 10^12Hz ?) arriving is let through, and any other photons are blocked. In practice of course it's not that simple. No filter is perfect - in the above example, some red photons will be absorbed by the filter, and some other ones might get through. Also some photons are more red than others, and the filter will not be even handed; photons of one redness will be more or less likely to pass than a photon of another redness: Example - Has anyone got a better example? Finally a filter is unlikely to be completely even across its width.SensitivityAn instrument's sensitivity also defines a passband. This might be tuned (radio) or part of the physical properties (CCDs).Instrument, AtmosphereOther factors influence the probability of a photon being measured - the full chain is given in the Observation Data Model doc (2.8) - depending on where 'incoming' is defined. We can model this set of sequential passbands using a ChainedPassbandReferencesHow are we going to model it?I first present the public interface that the Passband object presents to the models that use it, and then how various Passband objects might be defined.Public InterfaceA passband presents the following to the world:
Implementations
<--
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PassbandWhat is a passband?Defines the probability of an incoming photon of a particular wavelength being measured. | ||||||||
Added: | ||||||||
> > | (I concentrate here on modelling just the passband itself, not other measurement characteristics such as rest frame). | |||||||
FiltersThe simplest example is that of a red filter on an optical telescope; in principle any red photon (~ 5 x 10^12Hz ?) arriving is let through, and any other photons are blocked. In practice of course it's not that simple. No filter is perfect - in the above example, some red photons will be absorbed by the filter, and some other ones might get through. Also some photons are more red than others, and the filter will not be even handed; photons of one redness will be more or less likely to pass than a photon of another redness: Example - Has anyone got a better example? Finally a filter is unlikely to be completely even across its width.SensitivityAn instrument's sensitivity also defines a passband. This might be tuned (radio) or part of the physical properties (CCDs).Instrument, AtmosphereOther factors influence the probability of a photon being measured - the full chain is given in the Observation Data Model doc (2.8) - depending on where 'incoming' is defined. We can model this set of sequential passbands using a ChainedPassbandReferencesHow are we going to model it? | ||||||||
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< < | Simply, mainly. I concentrate here on modelling just the passband itself, not other measurement characteristics such as rest frame. | |||||||
I first present the public interface that the Passband object presents to the models that use it, and then how various Passband objects might be defined.
Public InterfaceA passband presents the following to the world: | ||||||||
Changed: | ||||||||
< < | getPassrate( Photon ) - returns the probability (0-1) of an incoming photon of the given wavelength/frequency being measured. [What do you get when you apply a Photon with a significant error? Should we just ignore the accuracy?] | |||||||
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< < | getMinWavelength() - returns the wavelength (as a Wave ) below which no photons will be passed. | |||||||
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< < | getMaxWavelength() - returns the wavelength (as a Wave ) above which no photons will be passed. | |||||||
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< < | getUCD() at least for the early days, as there are UCDs defined for some passbands | |||||||
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< < | getName() for humans. Note that the implementation type will also give information and allow other specific | |||||||
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< < | getCentralWave() - returns approximate central wavelength/frequency/Wave for convenience. | |||||||
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< < | We can then have passbands such as:
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> > | -- MartinHill - 21 May 2004 | |||||||
<--
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< < | Not finished (Work in progress) | |||||||
PassbandWhat is a passband?Defines the probability of an incoming photon of a particular wavelength being measured.FiltersThe simplest example is that of a red filter on an optical telescope; in principle any red photon (~ 5 x 10^12Hz ?) arriving is let through, and any other photons are blocked. In practice of course it's not that simple. No filter is perfect - in the above example, some red photons will be absorbed by the filter, and some other ones might get through. Also some photons are more red than others, and the filter will not be even handed; photons of one redness will be more or less likely to pass than a photon of another redness: | ||||||||
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< < | (Example red filter pasband). | |||||||
> > | Example - Has anyone got a better example? | |||||||
Finally a filter is unlikely to be completely even across its width. | ||||||||
Added: | ||||||||
> > | SensitivityAn instrument's sensitivity also defines a passband. This might be tuned (radio) or part of the physical properties (CCDs). | |||||||
Instrument, AtmosphereOther factors influence the probability of a photon being measured - the full chain is given in the Observation Data Model doc (2.8) - depending on where 'incoming' is defined. We can model this set of sequential passbands using a ChainedPassband | ||||||||
Deleted: | ||||||||
< < | How are we going to model it?From two sides; the interface the Passband object presents to the models that use it, and separately how those objects might be defined.Assumptions
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References | ||||||||
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> > | How are we going to model it?Simply, mainly. I concentrate here on modelling just the passband itself, not other measurement characteristics such as rest frame. I first present the public interface that the Passband object presents to the models that use it, and then how various Passband objects might be defined. | |||||||
Public InterfaceA passband presents the following to the world: | ||||||||
Changed: | ||||||||
< < | getPassrate( Wave ) - returns the probability (0-1) of an incoming photon of the given wavelength/frequency being measured. [What do you get when you apply a Wave with a significant error? Should we just ignore the accuracy?] | |||||||
> > | getPassrate( Photon ) - returns the probability (0-1) of an incoming photon of the given wavelength/frequency being measured. [What do you get when you apply a Photon with a significant error? Should we just ignore the accuracy?] | |||||||
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< < | getMinWavelength() - returns the wavelength below which no photons will be passed. | |||||||
> > | getMinWavelength() - returns the wavelength (as a Wave ) below which no photons will be passed. | |||||||
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< < | getMaxWavelength() - returns the wavelength above which no photons will be passed. | |||||||
> > | getMaxWavelength() - returns the wavelength (as a Wave ) above which no photons will be passed. | |||||||
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< < | [Do people prefer working in wavelengths or frequencies? Of course the above methods can be duplicated for frequencies, or we can use a Wave datamodel that can be represented as wavelength of frequency - MCH] | |||||||
getUCD() at least for the early days, as there are UCDs defined for some passbands
getName() for humans. Note that the implementation type will also give information and allow other specific
getCentralWave() - returns approximate central wavelength/frequency/Wave for convenience.
Implementations
<--
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PassbandWhat is a passband?Defines the probability of an incoming photon of a particular wavelength being measured.Filters | ||||||||
Changed: | ||||||||
< < | The simplest example is that of a red filter on an optical telescope; in principle any red photon (~ 5^12Hz ?) arriving is let through, and any other photons are blocked. | |||||||
> > | The simplest example is that of a red filter on an optical telescope; in principle any red photon (~ 5 x 10^12Hz ?) arriving is let through, and any other photons are blocked. | |||||||
In practice of course it's not that simple. No filter is perfect - in the above example, some red photons will be absorbed by the filter, and some other ones might get through. Also some photons are more red than others, and the filter will not be even handed; photons of one redness will be more or less likely to pass than a photon of another redness: (Example red filter pasband). | ||||||||
Added: | ||||||||
> > | Finally a filter is unlikely to be completely even across its width. | |||||||
Instrument, AtmosphereOther factors influence the probability of a photon being measured - the full chain is given in the Observation Data Model doc (2.8) - depending on where 'incoming' is defined. We can model this set of sequential passbands using a ChainedPassbandHow are we going to model it?From two sides; the interface the Passband object presents to the models that use it, and separately how those objects might be defined.Assumptions
ReferencesPublic InterfaceA passband presents the following to the world: | ||||||||
Changed: | ||||||||
< < | getPassrate(wavelength) - returns the probability (0-1) of an incoming photon of the given wavelength being measured. | |||||||
> > | getPassrate( Wave ) - returns the probability (0-1) of an incoming photon of the given wavelength/frequency being measured. [What do you get when you apply a Wave with a significant error? Should we just ignore the accuracy?] | |||||||
getMinWavelength() - returns the wavelength below which no photons will be passed.
getMaxWavelength() - returns the wavelength above which no photons will be passed.
[Do people prefer working in wavelengths or frequencies? Of course the above methods can be duplicated for frequencies, or we can use a Wave datamodel that can be represented as wavelength of frequency - MCH]
getUCD() at least for the early days, as there are UCDs defined for some passbands
getName() for humans. Note that the implementation type will also give information and allow other specific | ||||||||
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< < | getPassband(minWavelength, maxWavelength) - returns a new Passband object based on the old. [Hmm not sure about this one - MCH] | |||||||
> > | getCentralWave() - returns approximate central wavelength/frequency/Wave for convenience. | |||||||
Implementations
<--
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PassbandWhat is a passband?Defines the probability of an incoming photon of a particular wavelength being measured.FiltersThe simplest example is that of a red filter on an optical telescope; in principle any red photon (~ 5^12Hz ?) arriving is let through, and any other photons are blocked. In practice of course it's not that simple. No filter is perfect - in the above example, some red photons will be absorbed by the filter, and some other ones might get through. Also some photons are more red than others, and the filter will not be even handed; photons of one redness will be more or less likely to pass than a photon of another redness: (Example red filter pasband).Instrument, AtmosphereOther factors influence the probability of a photon being measured - the full chain is given in the Observation Data Model doc (2.8) - depending on where 'incoming' is defined. We can model this set of sequential passbands using a ChainedPassbandHow are we going to model it?From two sides; the interface the Passband object presents to the models that use it, and separately how those objects might be defined.Assumptions
ReferencesPublic InterfaceA passband presents the following to the world:getPassrate(wavelength) - returns the probability (0-1) of an incoming photon of the given wavelength being measured.
getMinWavelength() - returns the wavelength below which no photons will be passed.
getMaxWavelength() - returns the wavelength above which no photons will be passed.
[Do people prefer working in wavelengths or frequencies? Of course the above methods can be duplicated for frequencies, or we can use a Wave datamodel that can be represented as wavelength of frequency - MCH]
getUCD() at least for the early days, as there are UCDs defined for some passbands
getName() for humans. Note that the implementation type will also give information and allow other specific
getPassband(minWavelength, maxWavelength) - returns a new Passband object based on the old. [Hmm not sure about this one - MCH]
Implementations
<--
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