Vera C. Rubin Observatory first images (opens in new tab)

Doing some extremely rough math along these lines to double check myself:

* Gemini says that a dinosaur-extincting asteroid hits Earth about once every 100 million years. So in any given year that's 0.000001%.

* Economists say a human life is worth about 10 million dollars. There are about 8 billion people on Earth. So the total value of all human life is $80,000,000,000,000,000 (or 8e+16).

* So in any given year, the present value of asteroid protection is $800,000,000 (likelihood of an impact that year times value of the human life it would wipe out).

* The Guardian says the Vera Rubin telescope cost about $2,000,000,000 (2 billion).

By that measure, assuming the Rubin telescope prevents any dinosaur-extinction-level asteroid impacts, it will pay for itself in three years.

https://www.npr.org/transcripts/835571843

Tracking large near earth objects is wise for several global and domestic security reasons.

Have a great day =3

The thing that really saddens me is that the military gets to filter the data first and scientists only get to see the already manipulated data instead of a raw feed from their own instrument.

Both because they can't be made invisible, and because you need to avoid collisions.

https://noirlab.edu/public/images/iotw2421b/ ("thought to be right next to each other — both at a distance of about 50 million light-years")

This one's extra-special! The pattern is multiple + shapes, rotated and superimposed on top of each other. And they're different colors! That's this telescope's signature scanning algorithm—I don't know what that is, but, it's evident it takes multiple exposures, in different color filters, with the image plane rotated differently relative to the CCD plane in each exposure. I assume there's some kind of signal processing rationale behind that choice.

edit: Here's one of the bright stars, I think it's HD 107428:

https://i.ibb.co/HTmP0rqn/diffraction.webp

This one has asteroid streaks surrounding it (it's a toggle in one of the hidden menus), which gives a strong clue about the timing of the multiple exposures. The asteroids are going in a straight line at a constant speed—the spacing and colors of the dots shows what the exposure sequence was.

I think this quote explains the reason they want to rotate the camera:

> "The ranking criteria also ensure that the visits to each field are widely distributed in position angle on the sky and rotation angle of the camera in order to minimize systematic effects in galaxy shape determination."

https://arxiv.org/abs/0805.2366 ("LSST [Vera Rubin]: from Science Drivers to Reference Design and Anticipated Data Products")

However, the brightness of the diffraction effects is much lower than the light of the focused image itself. Where the image is itself dim, the diffraction effects might not add up to anything noticeable. Where the image supersaturates the detector (as can happen with a 1-pixel-wide star), the "much lower" fraction of that intensity can still be annoyingly visible.

My favourite fact about these in relation to astronomy is that you can actually get rid of the diffraction spikes if your support vanes are curved, which ends up smearing out the diffraction pattern over a larger area [2]. However this is often not what you want in professional astronomy, because the smeared light can obscure faint objects you might want to see, like moons orbiting planets, planets orbiting stars, or lensed objects behind galaxies in deep space. So you often want sharp, crisp diffraction spikes so you can resolve these faint objects next to or behind the bright object that's up front.

[1] https://www.celestron.com/blogs/knowledgebase/what-is-a-diff...

[2] https://www.fpi-protostar.com/img/spikes.gif

However, what we strive for is being accurate to "if your eyes COULD see like this, it would look like this". To the best our our ability of course. We did a lot of research into human perception to create this and tired to map the information of color and intensity in a similar way to how your brain constructs that information into an image.

Let me tell you, I did not appreciate how deep a topic this was before starting, and how limited our file formats and electronic reproduction capabilities are for this. The data has such a range of information (in color and intensity) it is hard to encode into existing formats that most people are able to display. I really want to spend some time to do this in modern HDR (true HDR, not tone-mapping) where the brightness can actually be encoded separately than just RGB values. The documentation on these (several competing) formats is a bit all over the place though.

Edit: I wanted to edit to add, if anyone reading this is an expert in HDR formats and or processing, I'd live to pick your brain a bit!