Sensors of largest digital camera snap first 3,200-megapixel images at SLAC
www6.slac.stanford.edu
www6.slac.stanford.edu
Okay, that's just a nerd swipe. What's the field of view! Oh journalism...
Maybe they "left it as an exercise for the reader"? ;-)
The FOV is "40 full moons", so 40•0.5° = 20°. For a plane 15 miles away that comes to an image covering 2•15•tan(10°) miles = 5.3 miles.
So you can spot a golfball on an image that's 5.3 miles large.
So using your calculation it could be, approximately: 7*0.5° = 3.5° in FOV.
It is just a darn weird thing to say when they could have just used a more standard analogy for contextualizing resolution.
What's the point of even posting that. What were they testing? Is this an issue with focusing on things that are close?
What is however kind of surprising to me is the amount of hot pixels and also dust / debris on the sensors. You’d think that as expensive and fragile as they claim the sensor array is, they’d keep the environment cleaner. Example: https://www.dropbox.com/s/rhldlwyqzuvbf2o/2020-09-13%2007.36...
I also question the “five human hairs” wide gap between the sensor pods given that the gaps are clearly visible in the press photos of the sensor array.
Not at the distance and resolution of a photo like this one:
https://www6.slac.stanford.edu/sites/www6.slac.stanford.edu/...
See the orangish lines between the sensors.
The sensors are in a cryostat, and the effective size you of what you might thing as “dust” in these pictures would be quite large if they had been on the sensors (several mm)
https://www.flickr.com/photos/slaclab/albums/721577137971034...
It’s worth noting that astronomical pictures often consist of multiple overlapping photographs. And I guess it’s plausible to describe a digital sensor as capturing many one pixel photographs.
Anybody got real technical information about that?
Edit found it: https://www.lsst.org/scientists/keynumbers
If there were intelligent like out there advertising it's presence, are we likely to detect it given the sheer quantity of sources?
They would therefore be intelligent enough to know to/how to broadcast such a message in a way that can be universally understood.
Humans are pretty intelligent. Intelligent enough to ask that question, anyway.
So flip it around. If humans were to advertise our own presence to possible species living tens of billions of light years away, how would we go about doing it?
Sadly, even if they noticed and wrote back, our sun will have since burned out, our time in this universe long past.
We‘ve been trying: https://en.m.wikipedia.org/wiki/List_of_interstellar_radio_m...
I expect this project is more geared towards detecting the geometry of the universe e.g. star and galaxy formation and evolution
"We've had to update our model of how galaxies form to include engineering as the most parsimonious explanation for SLAC 79813 and its satellites 79828 and 79829. Light from SLAC 79833 indicates only class O stars whereas both satellites seem to contain only class M stars. Very pretty! But no natural process except precise engineering on a cosmic scale could lead to such homogeneity.
If our models are correct, tidal forces will stretch the two satellite galaxies into orthogonal tidal loops over the next billion years. It takes a species with significantly longer life-span than ours to appreciate the fireworks. But you can watch our simulation."
Such unlikely discoveries might very well start with a photo of the sky.
If we assume it's a broadcast message (a poor guess for Andromeda, but reasonable for further galaxies), with energy projected in all directions, we can use the surface area of a sphere given a distance to get an idea for how much energy would need to be broadcasted to detect a single red photon: A=4πr^2
A = 4π * (2.4×10^22 meters)^2 = 7.238×10^45 m^2 (square meters)
7.238×10^45 m^2 / 221.7 m^2 * 2.8*10-19 J = 9.14136×10^24 J (joules)
Wolfram alpha helpfully says that is "≈ ( 0.024 ≈ 1/42 ) × energy output of the sun in one second ( 1 s L_ )"
Which is better than I expected, honestly.
So, even for detecting life in the closest galaxy, an intelligent species would need to be working on the energy level of output of stars to have a hope of light reaching earth. Not even counting getting above the noise of the rest of the stars, and then sending something identifiable as intelligent.
Imagine something like a DLP chip, but super thin: a reflective foil with tiny baffles that can be opened and closed using something like a simple electrostatic system. Make these into an enormous light-sail like sheet the same radius as the star. Place it between the star and the target system, and you have something akin to a signal lamp using the star as the light source.
You don't need to generate power on the order of a star, you just need to modulate the light field of one. That's a much lower requirement.
Bit of back-of-the-envelope maths is that this is 4*10^12 kg, assuming a sail the radius of our sun, 1 nm thick, and made of aluminium.
That's a huge amount of mass, but conceptually manufacturable if using automated asteroid mining or somesuch.
I feel like I'm making a mistake in my calculations for the energy requirements to toggle the shutters, but it's certainly low enough that embedded solar cells can trivially provide the energy required even if they're tiny and far apart.
There are many (smaller) galaxies much closer than Andromeda: https://en.wikipedia.org/wiki/List_of_nearest_galaxies
There's no reason life would only emerge in large galaxies.
Presumably for astronomical applications they don't matter.
3200 megapixels is around 200x bigger than a typical today's photo, so around 200x slower. It'd also still fit in e.g. 10GB of RAM.
Since applying a filter takes way less than a second on a standard photo (well, depends on what kind of filter you mean, let's say an approximated gaussian blur), single threaded, it'd be around a minute on a huge one, and with e.g. 8 or 16 threads, much faster.
The problem is more with bandwidth and working memory. For example, if you'd stream the image to a GPU (at 6.4 GB per image, assuming it's greyscale 16 bit), you're just being bottlenecked by PCIe plain and simple. GPU memory sizes aren't favorable to these sizes, either, most models don't have enough memory to have one input and one output buffer (assuming you also want 16 bit out). So, with a single GPU the bus would limit you to around 1-2 pictures per second.
However, the quoted throughput is "30 TB per night", that's only one GB per second. So it's plausible (but unlikely they do) to process all of the data on a single desktop PC with a GPU and a dual 10 GbE NIC card.
It's hard to engineer right and doesn't scale, so people rarely do it...
The images also aren’t that large, just high res for a single focal plane image. I have personally taken WAY higher resolution photos than 3.2 gigapixel before using post-process stitching of hundreds (or thousands in one case) of photos. My highest resolution one I’ve taken was from the roof of a building in San Francisco. It was just over 2,000 twenty five megapixel images using a 400mm lens and produced a combined image over 10x higher resolution (Around 42 billion pixels) than this does.
Fun fact, 3.2Gp can still be stored as a single jpeg. The JPEG file format tops out at 4Gp (64k x 64k). PNG can’t go that high generally because it tops out at a 2GB file size (limited by bytes rather than pixels). For the previously mentioned 40Gp image, the file format I had to use was PSB. It’s been almost a decade since I took that pano, but if memory serves the file was around 150GB.