Hello,
For the 2024 solar eclipse I had 3 cameras set up and dialed in, I had never done an eclipse before so I threw everything I had at it. I had practiced for months before. I used two laptops to run two astro cams with dedicated OTAs and I had a backup Canon T7i with a 600 sigma lense which did amazingly well actually. The entire rig was mounted on an AM5, polar aligned the night before and then uncovered the next day. The canon rig and 178MM rig used the same kind of filter (the thousand oaks stuff you get on amazon) whereas the redcat 2600MC rig used a more pricey glass filter I got from Agena.
The other two imaging setups were a 2600MC on a redcat71 and a 178MM on an askar 61 EDPHIII hoping I could get more sensitivity out of these setups (which I think I did!). All of the cameras worked flawlessly and I got a lot of data that I am still trying to process to this day.
This was a shot of a practice run.
During the eclipse, everything worked.
I already processed the Canon images and I am pretty happy with them, I learned a lot from naztronomy on youtube and a few others. However I have a question that has been perplexing me for a while now, it has to do with the 2600MC and 178MM rigs.
Below is an image from the canon with sigma lens, that is not the point of this thread though.
My question lies with the 2600MC rig. This sensor is by far the best sensor out of the bunch that I used. It has the highest sensitivity, lowest noise and highest dynamic range. Despite this, I seem to be getting blown out white areas at low exposure times, and barely showing any stringers at the edges (they are there but it's almost like the camera struggled to pick them up at low exposure settings). This surprised me because my canon was able to pick up what seemed to be more dynamic range (I know this is impossible, hence my question).
This is a sample of what the 2600MC data looks like at different exposures. The detail is amazing, however only when not blown out.
I am using the pellet method to stack the 2600mc data, and of course the results are wonky, the central areas near the sun are blown out and showing up as ugly bright cutoffs in the stack. This is expected. Below is an example of what the data can look like with the blown out areas averaged into the corona data.
Here are some options I proposed to fix this issue, and still use the clean data that reaches farther out to the corners, those stringers are there and the detail is nice and I want to use it in my stack, but not at the cost of averaging in blown out data.
1. Use a mask of some kind in photoshop to cover the blown out area (cut it out??), so it doesn't add to the stack and add just the stringers to the stack. This mask can have a radial gradient so it doesnt look like a weird cutoff in the stack. Or is there some kind of script that can create a mask that only selects blown out data? And maybe dilate that a little bit with a gradient so it can sort of look natural when averaged in? Just thinking out loud here.
2. Stack all the images, but for with a central radial mask, use some kind of opacity multiplier that reduces its impact on the total average. Maybe set it to zero or some smaller number? This is sort of like the above suggestion but a slightly different implementation.
I am not sure exactly if this method will work, and that is why I am asking you.
Thanks!
