cconstantine

> why not put a whole bunch of filters in front of a mono camera and get much more frequency information?

Just rgb filters aren't really going to get you anything better than a bayer matrix for the same exposure time, and most subjects on earth are moving too much to do separate exposures for 3 filters.

The benefits of a mono camera and rgb filters is that you can take advantage of another quirk of our perception; we are more sensitive to intensity than color. Because of this, it's possible to get a limited amount of exposure time with the rgb filters, and use a 4th "luminance" filter for the majority of the time. During processing you can combine your rgb images, convert that to HSI and replace the I channel with your luminance image. Because the L filter doesn't block much light it's faster at getting signal, but it's only really a benefit for really dark stuff where getting enough signal is an issue.

For the vast majority of things in the sky, they'd see black. This stuff is incredibly dark, and we need hours of exposure time to get enough signal. Even after hours of exposure the raw stacked frame is a black field with some pinpoints of lights.

The exception to this is stuff in our own solar system.

The problem is that the noise can swamp the signal. Another example of this would be doing astrophotography during the day. The sun doesn't block anything, it just makes the sky glow with "noise". Theoretically it has exactly as much signal from space as it does at night, but because the sun adds so much noise it's completely lost.

I don't remember exactly what I did, but I do remember running into that kind of problem. I probably used starnet2 to remove stars before doing much processing, and recombining stars towards the end.

I've seen some pretty impressive stuff done with a relatively cheap / simple DSLR setup.

The basics of astrophotography aren't that expensive, but it gets exponentially more expensive to meaningfully "zoom in". Because DSLRs with typical lenses are pretty zoomed out you can get away with much cheaper gear. You might look into getting a "star tracker". It's like a telescope mount for a camera; it'll keep the still relative to the stars but because they don't need to be as accurate they're far cheaper to make. They'll probably work just fine for your 200mm 2.8 lens for a fraction of the cost of a mount.

Haha, yeah. I could go on for hours. I've had to learn that most people really don't want a lecture series on the finer points of astrophotography. Seabass's comment was pretty much perfect; a bit of detail, but not so much to get lost in the detail.

I tried to write a quick comment on my process a couple of times before they posted, and each time I had way too many words on a small detail.

It's a wonderful niche/technical hobby, but it's not cheap. You could even say it's "pay to win". I didn't buy all of my stuff at once, and I had some mistakes, but I'd guess I use on the order of $10k in equipment.

Oh nice! Except for Alnitak (I love me some spikes), I like yours more.

Well, if you think photography is too easy you could try taking up astrophotograhy :)

Thanks, but it if you look closely you'll see that the Webb image has almost an image worth of detail within each pixel of my image.

Thanks! I hadn't gotten to writing this out, but you've pretty much nailed it.

> They most likely used a secondary camera whose sole purpose is to guide the mount and keep it pointed at the target object.

I did use a guide camera with an off-axis guider, I'm not sure why it wasn't in the equipment list. I've added it.

> The RGB filters were presumably for the star colors and to incorporate the blue from Alnitak into the image.

This is primarily an RGB image, so the RGB filters were used for more than the star colors. This is a proper true color image. I could get away with doing that from my location because this target is so bright. The HA filter was used as a luminance/detail layer. That gave me a bunch of detail that my local light pollution would hide, and let me pick up on that really wispy stuff in the upper right :)

> The processing here was really tasteful in my opinion.

Awe shucks, thanks :blush:

I've been using terraform for 10-ish years, and this is very much not how I feel about it. Terraform absolutely makes life easier; I've managed infrastructure without it and it's a nightmare.

Yes, it can be awkward, and yes the S3 bucket resource change was pretty bad, but overall its operating model (resources that move between states) is extremely powerful. The vast majority of "terraform" issues I've had have actually been issues with how something in AWS works or an attempt to use it for something that doesn't map well to resources with state. If an engineer at AWS makes a bone-headed decision about how something works then there isn't much the terraform folks can do to correct it.

I've actually been pretty frustrated trying to talk about terraform with people who don't "get it". They complain about the statefile without understanding how powerful it is. They complain about how it isn't truly cross-platform because you can't use the same code to launch an app in aws and gcp. They complain about the lack of first-party (aws) support. They complain about how hard it is to use without having tried to manually do what it does. Maybe you do "get it", and have a different idea of what terraform should do. Could you give a specific example (besides the s3 resource change) where it fails?

It's a complicated tool because the problem it's trying to solve is complicated. Maybe another tool can replace it, and maybe someone should make that tool because of this license change, but terraform does the thing it intends to do pretty well.

Yes, you can :)

It all depends on the properties of the signal and the noise. In photography you can combine multiple noisy images to increase the signal to noise ratio. This works because the signal increases O(N) with the number of images but the noise only increases O(sqrt(N)). The result is that while both signal and noise are increasing, the signal is increasing faster.

I have no idea if this idea could be used for AI detection, but it is possible to combine 2 noisy signals and get better SNR.