This is true. The telescope is much more than just the primary mirror.
Roman has two science instruments, a wide-field imager (“WFI”) and a coronagraph (“CGI”) and they were both built by NASA for this purpose (optics, electronics, detectors, deformable mirrors).
The WFI optics includes filters and a prism and grism for spectroscopy, and of course the very large CCD arrays.
The coronagraph is a rather special tech demo and allows imaging of exoplanets (Jupiter-like) while blocking the light of the host star. (https://science.nasa.gov/mission/roman-space-telescope/coron...). It’s a precursor to HWO which is mentioned down-thread.
OP is considering power laws in the frequency domain.
You are considering power laws in the heavy tail of a distribution.
Different things! But there are confounders that make discussion seem similar:
- questions about moments and convergence (OP: do we have finite energy in the Fourier domain; your comment: do the tails of the distribution fall off fast enough to have finite moments of order 1 or 2)
- questions about averaging (OP: in the time domain; your comment: as an expectation obtained by integrating a distribution, or as a closure property of the stable class of distributions)
Yes. My PhD advisor had a research interest in axiom systems for probability that are weaker than the familiar Kolmogorov axioms, which are sometimes abbreviated "CMP" for "conventional mathematical probability".
I'll try to remember the setup. The CMP axioms imply that, in a shift-invariant system X(t) (which is a different class than "stationary" -- not necessarily implying existence of second moments), if the mean of X(t) exists finite, then a long-term average of X(t) must converge.
However, you can observe time-invariant physical systems (such as a noisy resistor in a static environment) with spectra that obey the 1/f law down to very low frequencies (i.e., over very long time baselines) -- the time average does not converge. My advisor had a stack of magnetic tapes on his bookshelf with such samples.
These systems would seem to be disobeying the axioms of CMP, thereby motivating searches for alternative formulations that are more general.
Look under the section “Surprise”. This gives a construction that produces a new function g, whose transform is itself, that is a simple modification of any function ”f” that you supply.
In essence, the class of “functions that transform into themselves” is surprisingly broad and non-specific for those of us who key in on that fact about the Gaussian.
This kind of observation is a big deal for solar physics.
It's been believed for decades that these small-scale (~100km and below) turbulent features are critical to understanding how energy dissipates in the Sun. And thus, how sunspots and flares form.
The subject has been very qualitative but is yielding on both observational and simulation fronts. I worked adjacent to this area from the 1990s-2010s, and it had been true that MHD numerical simulations of significant volumes of the Sun (but at a scale fine enough to resolve these features) were not possible. That has obviously changed!
Additionally, it had been that the best solar observatories could not quite resolve these features. In the late 1990s some of the best images came from a couple of observatories in the Canary Islands (e.g., the 1-meter Swedish telescope -- https://svs.gsfc.nasa.gov/4715/). The spatial resolution was perhaps in the ~100km range.
Of course, these are absolutely mind-boggling images. You're looking at a slice of the solar photosphere that has a temperature such that it activates a spectral line around 400nm. By isolating that wavelength, we can see what's happening at that temperature, and thus, sample a slice of the photosphere.
So, that had been the state of affairs. Now DKIST (4m aperture), with the particular instrument highlighted in OP, appears to be at a spatial resolution ~5x finer than the above imagery -- see Fig. 1c in the Nature paper (https://www.nature.com/articles/s41586-026-10871-3). It appears also (https://dkist.virtualsolar.org/vanNoortfastcam/) to be observing at 740Hz (!) for speckle reconstructions at ~1Hz.
At this scale, vortices of the flow are well-resolved -- where before you just resolved the convective cells but not the turbulent features around them. It's these turbulent features that are transporting energy.
To contextualize with respect to a HN perennial topic: DKIST (commissioned 2021) is funded by NSF, from the same pile of money that once funded Arecibo (up to 2020).
I worked adjacent to this area from the 1990s-2010s when the topic is solar physics is definitely why this forum is one of the last "places" of the internet.
Remote sensing of the Sun is different from anything else, because you have so many photons. The idea of binning it down so fine (20x20km, 740Hz, a single nm of spectrum around a band center) is unheard of for any other target.
It's highly filtered, you are only getting a small slice of all of the wavelengths. Only a tiny fraction of all that energy hits the photo collector. I believe the collector is also actively cooled, but I might be wrong there.
If the pixels really are 20x20km, located at 1AU, I get 500k photons (within that bandpass) per pixel per frame at 740 Hz, not counting optical losses.
Space solar instruments like HMI on SDO (https://science.nasa.gov/mission/sdo/) have one or more pre-filters in front of the instrument to block some light far from the passband and keep heating under control. I’m not sure if DKIST has such filters.
This is not just about the Sun. As we progress towards developing nuclear fusion reactors, a better understanding of the underlying physics may have practical implications for reactor design and could even reveal factors currently being overlooked. At the very least, it will improve modelling. Better models could help fusion reactors retain heat for longer and increase fusion gain (Q), while reducing uncertainty, improving plasma-edge control, and predicting divertor heat loads more accurately.
People were identifying gross features (like the convection cells in the first website I linked), and giving those features names. Like “sunspots”, “faculae”, “pores”, “granulation”, “bright points”, etc. That’s on the observational side.
I’m less informed about simulations in this era - perhaps some simplified MHD simulations had been more quantitative in linking equations of state to the emergence of these features - but I believe they were not realistic enough to provide definite constraints on the scale of the structures.
It was clear that these features had to do with energy transport and some of the mechanisms were hypothesized. But the spatial scale of the gross energy transfer in these convective cells was not known, either through simulations or observation.
"Qualitative" means that the field had good conceptual theories, but didn't have sufficient observational and computational resolution to determine the detailed numerical values
Interesting that you use the phrase "quality vs quantity". Normally, I think of precision being on the side of "quality". As in, you carefully make a thing to meet a certain standard instead of making a lot of them.
In the case of "qualitative vs quantitative", despite having the same roots, I think of precision being on the side of the quantitative --with qualitative work answering yes/no questions and quantitative yielding precise measurements.
Some phrases have stuck with me, like “mandatory defaults“, “they told their users to see figure 1 a long time ago” and the flippant “sometimes we blow it though”.
Roman has two science instruments, a wide-field imager (“WFI”) and a coronagraph (“CGI”) and they were both built by NASA for this purpose (optics, electronics, detectors, deformable mirrors).
The WFI optics includes filters and a prism and grism for spectroscopy, and of course the very large CCD arrays.
The coronagraph is a rather special tech demo and allows imaging of exoplanets (Jupiter-like) while blocking the light of the host star. (https://science.nasa.gov/mission/roman-space-telescope/coron...). It’s a precursor to HWO which is mentioned down-thread.
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