APOD: Jupiter and Ring in Infrared from Webb (2022 Jul 20)

[Mel]

Sorry. The Fits-header shows filter: F322W2 and pupil: F323N. I think both were used.

[VictorBorun]

I thought there may be 2 filter wheels like 2 layers with light passing a filter in each wheel, but no, there is only one wheel:

We operated the Filter Wheel Assembly first, cycling it through all eight of its positions in both forward and reverse directions. Those eight filter wheel positions include five long-pass order-separation filters, two finite-band target acquisition filters, and an ‘opaque' position

[Chris Peterson]

I thought there may be 2 filter wheels like 2 layers with light passing a filter in each wheel, but no, there is only one wheel:

We operated the Filter Wheel Assembly first, cycling it through all eight of its positions in both forward and reverse directions. Those eight filter wheel positions include five long-pass order-separation filters, two finite-band target acquisition filters, and an ‘opaque' position

There are four wheels, two each for the long wavelength channel and the short wavelength channel (which have different optical paths). They are often used in combination. For instance, when the F323N filter (long wavelength pupil wheel) is used, it is typically paired with the F322W2 filter (long wavelength filter wheel) for blocking purposes.

This image was constructed from two data channels, one imaged through the F323N/F322W2 pair, and the other through the F212N filter (short wavelength filter wheel).

[sallyseaver]

Judy/Geckzilla, who processed the JWST data, confirmed the following on https://geckzilla.com/:

Red (screen): NIRCam F322W2-F323N (this is not a subtraction function, both filters were used at the same time)
Blue: NIRCam F212N
Background is a grayscale combination of both filters. There were gaps in the data that had to be filled in using either filter to complete the other.

This page, https://jwst-docs.stsci.edu/jwst-near-i ... am-filters, shows the wavelengths involved for these filters:
F322W-F323N: 2.5 to 4.1 microns
F212N: 2.1 to 2.15 microns

This page from NASA, https://science.nasa.gov/ems/07_infraredwaves, has an interesting discussion related to interpreting infrared [IR] imaging.

It has some images showing a calibration between temp and colors in a dog image. This Jupiter image is not calibrated to specific temperatures but we can get a sense of hot and cold. Here is my reasoning.

Consider the image of the dog at the NASA page noted above.

Consider that the light areas of the IR image go with areas of the dog where the temp is high and IR energy is reflected (not absorbed). Dark areas of the IR image go with areas of the dog where temp is not high and IR energy is absorbed.

Quoted from the above NASA page,

Infrared waves have longer wavelengths than visible light and can pass through dense regions of gas and dust in space with less scattering and absorption.

In the JWST image of Jupiter processed by Geckzilla, we can see that the horizontal bands, or zonal flows, have different temps. (Jupiter is characterized by bands that alternately have rotational momentum in opposite directions.) The JWST/Geckzilla image shows cold temps at the poles and interestingly, the Great Red Spot [GRS] is cold at the depth to which the IR rays penetrate.

Additional temp data for the GRS comes from Juno's Microwave Radiometer, which penetrates to about 350 km. Here is a slide from a presentation at the 2019 AGU Fall Meeting which shows a range of temps with depth - and even a range of temps within the same depth.

Click to view full size image

In this case, with microwave EM as imaged by Galanti et.al., darker colors indicate cooler temps and lighter colors indicate warmer temps. Note that at 0 km, the temp is mostly cooler. At 50 km, it is mostly warmer. At 350 km, about 25% to 33% of the depth range is cooler with the remaining 66% to 75% being warmer.

VictorBorun asked:

Why evening clouds gap, 670 km at the equator?
Why bright polar cups?

It seems to me that the gap that is noted on the right side of the image is due to IR waves passing through the dust and gases that are present so that they are not being scattered/reflected or absorbed. The gap probably does not exist in visible light.

The reason for the bright polar caps and bright GRS, according to the model I submit in my published poster at AGU Fall Meeting 2020, https://agu2020fallmeeting-agu.iposters ... 5-8F-8C-6A is that Jupiter has gaseous debris on top of an ice shell. Gases on a rotating body accumulate at the equator, and are sparse at the poles. Evidently, the gases and other debris are about 3750 km thick at the equator; because, T. Guillot et. al. report in a paper that the gravity data from Juno reveals rigid-body rotation under 3500 km - 3750 km of equatorial gases. Guillot, T., Miguel, Y., Militzer, B. et al. A suppression of differential rotation in Jupiter’s deep interior. Nature 555, 227–230 (2018). https://doi.org/10.1038/nature25775. (To be clear the authors of the paper attribute the rigid-body rotation to electromagnetic effects, NOT an ice layer.)

My research uses UV data from Juno to reveal that Jupiter's poles exhibit complete absorption of UV in a characteristic wavelength range for UV absorption by ice in space (vacuum UV [VUV]). Brightness for water ice in space goes to zero for VUV at 165 nm to 180 nm. I show this in the 6th evidence section in the "Evidence from Juno, Cassini and Other" part of my poster. This is true for UV brightness readings from latitudes +90 deg to -74.5 deg and -90 deg to -74.5 deg. I found it to be true even in the area of the auroras.

A UV loss cone was reported by Allegrini et. al. 2020, "Energy flux and characteristic energy of electrons over Jupiter's main auroral emission." Journal of Geophysical Research; Space Physics, 125, e2019JA027693. The Allegrini team was focused on the aurora activity and did not track UV at specific wavelengths. I believe that this is why they did not see the UV loss at wavelengths 165 nm to 180 nm in the regions of the aurorae.

Jupiter's temperature story involves heat due to friction with the differential flows of debris on top of the ice shell. This heat maintains a thin water layer between the ice shell and the outer swirling debris... according to the model I assert.

To be clear, the trusted professors of space science do not even entertain the idea that Jupiter could have an ice shell. Even though Scott Bolton, the lead investigator of the Juno mission said that Jupiter is a whole new planet from what we thought, the team is still, for the most part, using the same models that they were prior to Juno's insertion into its planetary orbits.

Anyway, I thought that some of my fellow Asterisk participants might be interested in some of this info, Please forgive me if I have stepped out of bounds to bring up a nonstandard interpretation of data observations.

I sign off with my favorite image of Jupiter in infrared light, from the far side of Jupiter.

Click to view full size image

[Chris Peterson]

Judy/Geckzilla, who processed the JWST data, confirmed the following on https://geckzilla.com/:

Red (screen): NIRCam F322W2-F323N (this is not a subtraction function, both filters were used at the same time)
Blue: NIRCam F212N
Background is a grayscale combination of both filters. There were gaps in the data that had to be filled in using either filter to complete the other.

This page, https://jwst-docs.stsci.edu/jwst-near-i ... am-filters, shows the wavelengths involved for these filters:
F322W-F323N: 2.5 to 4.1 microns
F212N: 2.1 to 2.15 microns

This page from NASA, https://science.nasa.gov/ems/07_infraredwaves, has an interesting discussion related to interpreting infrared [IR] imaging.

It has some images showing a calibration between temp and colors in a dog image. This Jupiter image is not calibrated to specific temperatures but we can get a sense of hot and cold. Here is my reasoning.

Consider the image of the dog at the NASA page noted above.

Consider that the light areas of the IR image go with areas of the dog where the temp is high and IR energy is reflected (not absorbed). Dark areas of the IR image go with areas of the dog where temp is not high and IR energy is absorbed.

The dog image is single channel (that is, grayscale data) mapped to a pseudocolor palette. It is in a wavelength band that represents energy being emitted from the dog, not reflected in any way. Intensity has been mapped to color, so whether brighter is hotter is purely dependent upon the chosen palette.

The Jupiter image is constructed from multichannel data assigned to a false color palette. Again, we are not seeing reflected IR but emitted IR. In general, for any single channel, the signal strength is proportional to temperature. But a longer wavelength will show cooler temperatures, so as soon as you combine two or more wavelength channels, you can no longer assume that what is brightest in the image is also the warmest. You need to consider the intensity in each channel separately to make any unambiguous assessment of temperature.

[sallyseaver]

The dog image is single channel (that is, grayscale data) mapped to a pseudocolor palette. It is in a wavelength band that represents energy being emitted from the dog, not reflected in any way. Intensity has been mapped to color, so whether brighter is hotter is purely dependent upon the chosen palette.

The Jupiter image is constructed from multichannel data assigned to a false color palette. Again, we are not seeing reflected IR but emitted IR. In general, for any single channel, the signal strength is proportional to temperature. But a longer wavelength will show cooler temperatures, so as soon as you combine two or more wavelength channels, you can no longer assume that what is brightest in the image is also the warmest. You need to consider the intensity in each channel separately to make any unambiguous assessment of temperature.

Chris, Thank you for straightening me out about the temperature interpretation of the IR imaging. I know about black body radiation, of course, but I obviously need more understanding about how this meshes with the usual absorption and refraction of incident EM waves in the IR range.

[VictorBorun]

Click to view full size image

the rgb coding is inverse:
2.0 μm ↦ red
2.14 μm ↦ green
2.16 μm ↦ blue


If we change the coding to
2.0 μm ↦ blue
2.14 μm ↦ green
2.16 μm ↦ red

[Chris Peterson]

Click to view full size image

the rgb coding is inverse:
2.0 μm ↦ red
2.14 μm ↦ green
2.16 μm ↦ blue


If we change the coding to
2.0 μm ↦ blue
2.14 μm ↦ green
2.16 μm ↦ red
Sharpening up Jupiter.jpg

Which demonstrates exactly why monotonic coding can be a very bad idea. The first mapping shows MUCH more detail than the second.

[johnnydeep]

Click to view full size image

the rgb coding is inverse:
2.0 μm ↦ red
2.14 μm ↦ green
2.16 μm ↦ blue


If we change the coding to
2.0 μm ↦ blue
2.14 μm ↦ green
2.16 μm ↦ red
Sharpening up Jupiter.jpg

Which demonstrates exactly why monotonic coding can be a very bad idea. The first mapping shows MUCH more detail than the second.

I'm probably showing my ignorance again, but isn't the second encoding still monotonic, just opposite in direction to the first?

[Chris Peterson]

the rgb coding is inverse:
2.0 μm ↦ red
2.14 μm ↦ green
2.16 μm ↦ blue


If we change the coding to
2.0 μm ↦ blue
2.14 μm ↦ green
2.16 μm ↦ red
Sharpening up Jupiter.jpg

Which demonstrates exactly why monotonic coding can be a very bad idea. The first mapping shows MUCH more detail than the second.

I'm probably showing my ignorance again, but isn't the second encoding still monotonic, just opposite in direction to the first?

Yes. Not the best word choice, but a hard thing to describe without a lot of words. We have been somewhat consistently using it in several discussions to refer to a mapping where there is a one-to-one correspondence between the input and output wavelengths. (Still not great wording, but hopefully you get the drift.)

[johnnydeep]

Which demonstrates exactly why monotonic coding can be a very bad idea. The first mapping shows MUCH more detail than the second.

I'm probably showing my ignorance again, but isn't the second encoding still monotonic, just opposite in direction to the first?

Yes. Not the best word choice, but a hard thing to describe without a lot of words. We have been somewhat consistently using it in several discussions to refer to a mapping where there is a one-to-one correspondence between the input and output wavelengths. (Still not great wording, but hopefully you get the drift.)

I would think any true one-to-one mapping of input to output wavelengths would preserve all detail, though it could be less visible if the output range is compressed versus the input range. And on the other hand a many to one mapping would clearly lose info/detail, and a one to many mapping might show false details that don't exist in reality. Or am I misunderstanding yet again?

[Chris Peterson]

I'm probably showing my ignorance again, but isn't the second encoding still monotonic, just opposite in direction to the first?

Yes. Not the best word choice, but a hard thing to describe without a lot of words. We have been somewhat consistently using it in several discussions to refer to a mapping where there is a one-to-one correspondence between the input and output wavelengths. (Still not great wording, but hopefully you get the drift.)

I would think any true one-to-one mapping of input to output wavelengths would preserve all detail, though it could be less visible if the output range is compressed versus the input range. And on the other hand a many to one mapping would clearly lose info/detail, and a one to many mapping might show false details that don't exist in reality. Or am I misunderstanding yet again?

I just mean a mapping where longer wavelengths in the source correspond to longer ones in the final image. And that does not necessarily result in the clearest image.

[johnnydeep]

Yes. Not the best word choice, but a hard thing to describe without a lot of words. We have been somewhat consistently using it in several discussions to refer to a mapping where there is a one-to-one correspondence between the input and output wavelengths. (Still not great wording, but hopefully you get the drift.)

I would think any true one-to-one mapping of input to output wavelengths would preserve all detail, though it could be less visible if the output range is compressed versus the input range. And on the other hand a many to one mapping would clearly lose info/detail, and a one to many mapping might show false details that don't exist in reality. Or am I misunderstanding yet again?

I just mean a mapping where longer wavelengths in the source correspond to longer ones in the final image. And that does not necessarily result in the clearest image.

Alright. So the details are still present (i.e., not totally lost), just obscured to our eyes due to the mapping choice.

[Chris Peterson]

I would think any true one-to-one mapping of input to output wavelengths would preserve all detail, though it could be less visible if the output range is compressed versus the input range. And on the other hand a many to one mapping would clearly lose info/detail, and a one to many mapping might show false details that don't exist in reality. Or am I misunderstanding yet again?

I just mean a mapping where longer wavelengths in the source correspond to longer ones in the final image. And that does not necessarily result in the clearest image.

Alright. So the details are still present (i.e., not totally lost), just obscured to our eyes due to the mapping choice.

Well, if you have three or less channels going in, and you map them directly to some combination of red, green, and blue, nothing is lost. But, as you say, details will be more or less visible to our eyes depending on the order of the mapping. If you have more than three input channels, or channels that are mapped to mixes of RGB (e.g. a channel mapped to yellow) then you lose information in the final image.

[johnnydeep]

I just mean a mapping where longer wavelengths in the source correspond to longer ones in the final image. And that does not necessarily result in the clearest image.

Alright. So the details are still present (i.e., not totally lost), just obscured to our eyes due to the mapping choice.

Well, if you have three or less channels going in, and you map them directly to some combination of red, green, and blue, nothing is lost. But, as you say, details will be more or less visible to our eyes depending on the order of the mapping. If you have more than three input channels, or channels that are mapped to mixes of RGB (e.g. a channel mapped to yellow) then you lose information in the final image.

Ok, got it.

[bystander]

Well, if you have three or less channels going in, and you map them directly to some combination of red, green, and blue, nothing is lost. But, as you say, details will be more or less visible to our eyes depending on the order of the mapping. If you have more than three input channels, or channels that are mapped to mixes of RGB (e.g. a channel mapped to yellow) then you lose information in the final image.

I know it probably doesn't come up much, but what about CMY (or CMYK)? Would you have information loss with that?

[Chris Peterson]

Well, if you have three or less channels going in, and you map them directly to some combination of red, green, and blue, nothing is lost. But, as you say, details will be more or less visible to our eyes depending on the order of the mapping. If you have more than three input channels, or channels that are mapped to mixes of RGB (e.g. a channel mapped to yellow) then you lose information in the final image.

I know it probably doesn't come up much, but what about CMY (or CMYK)? Would you have information loss with that?

I don't know of any display devices that use CMY. Subtractive color schemes like CMY are used in printing. They also have a smaller gamut, so there is more possibility for information loss. But in principle, the same thing applies to any mapping. The main point for not losing information is that the input channels be mapped to native output channels, not to mixes of those channels.

[VictorBorun]

I thought there may be 2 filter wheels like 2 layers with light passing a filter in each wheel, but no, there is only one wheel:

We operated the Filter Wheel Assembly first, cycling it through all eight of its positions in both forward and reverse directions. Those eight filter wheel positions include five long-pass order-separation filters, two finite-band target acquisition filters, and an ‘opaque' position

There are four wheels, two each for the long wavelength channel and the short wavelength channel (which have different optical paths). They are often used in combination. For instance, when the F323N filter (long wavelength pupil wheel) is used, it is typically paired with the F322W2 filter (long wavelength filter wheel) for blocking purposes.

This image was constructed from two data channels, one imaged through the F323N/F322W2 pair, and the other through the F212N filter (short wavelength filter wheel).

I finally got what you were saying all along: one of the 2 near-IR wheels has no clear filter and must be positioned as to let the light pass one of the wide filter surrogate