michael.h.f.wilkinson

I have had similar issues with a secondary that had astigmatism when glued and none when hung from the perimeter (this on a much smaller scope), so I guess the gluing can put some strain on the glass

I used the ASI183MC cropped down to roughly the sensor size of the ASI178m and took some longer SER files (2000 frames) to see if I could improve the detail. These are all stacks of 250 frames (using AS!3), B&W version sharpened in ImPPG, colour balance of colour version restored in Registax 6, colour and sharpened B&W version L+RGB combined in FITSwork, and final tweaks in GIMP Plato, Sinus Iridium, and environs Saturation pushed once: Twice: Copernicus and environs: Saturation pushed once: Twice: Thrice: Four times: Tycho and environs: Saturation pushed once: Twice: Thrice: Four times: Best seen at full resolution

Although seeing was pretty atrocious, I managed to stack 100 frames out of 13 500-frame SER files captured with the C8 and ASI183MC camera. Not entirely pleased with the noise, but given the seeing I think this is just about as good as I could get it. Natural colour: Saturation pushed once: The background needs some leveling, I see, but clicking on the images for full resolution is recommended nonetheless

I have the Nagler 31T5 and it is indeed a beast. I also have the Nagler 22T4, which is my main galaxy hunting EP, and a wonderfully comfortable EP to use. I have used it in Olly's 20" F/4.1 Dob, and it performed magnificently. It is a lot lighter than the 31T5 (a.k.a. "Panzerfaust").

I have an APM 80 mm F/6 triplet which weighs in at just 2.4 kg. Great little scope for travel, which sits beautifully on my mini-giro mount, shown below on a wooden tripod, but it also works on a carbon photo tripod. TS have a similar scope (although I think they only come with a 2.5" focuser).

Very nice detail captured. The core does look a little pink on my monitor, but that could easily be corrected with some tweaks

I had a go at reprocessing the data obtained last Tuesday. I had one hour of data with the Canon EOS 550D, and one hour from the ASI183MC, both taken with the same scope (APM 80mm F/6 with 0.6x reducer. I merged the data with Astro Pixel Processor, cropped the result, and tweaked the output with GIMP. Quite pleased with the result, which is definitely less noisy than each of the single outputs. The ASI183MC clearly has the edge in sensitivity and resolution, but the additional hour's worth of data from the 550D definitely helps. Still some gradients there. Next time I will see that I get the flats sorted out properly S

I did my best to mask the artefacts by darkening the background. Apparently, my cunning plan has worked

Good news! Clouded here, alas

First deep-sky attempt with the ASI183MC, APM 80 mm F/6 triplet, and 0.6x focal reducer. I used the ZWO Mini-EFW with L-band filter to reach (roughly) the right distance from flattener to the sensor. This results of 1h worth of 30s lights, 20 darks and 40 flats. As can be seen I had problems with the original set of flats I got, so took new ones, but there are still some annoying gradients I cannot seem to get rid of. Star shapes are not that good in the outer parts. I must definitely look at the spacing again. Cropped, the result is noisy, but OK for just 1h

First M33 image, and first image using the 0.6x focal reducer on my APM 80mm F/6 triplet. I used the Canon EOS 550D. This is just 30 subs of 120s at ISO 800, with 20 flats and 20 darks. This is quite noisy (unsurprisingly, for just 1 h of data), and I don't like the star shapes that much. I might need to adapt the distance to the sensor a bit. I also have data grabbed with the ASI183MC with the same optics, but I am still processing those

Nice looking scope but surprisingly hefty at 13.4 lbs. My C8 is lighter than that

Great set of images and lovely set-up

Same here. Over 45 subs grabbed, and still going strong

Got the EQ3-2 with Canon EOS 550D and 200 mm telephoto out clicking away. I have got 120s unguided working neatly. Doing 60 subs on NGC 7000. After this I will have a go at M33 with the scope.

I prefer to make my tea from proper boiling water using loose leaf Keemun black tea (祁门, although I sometimes go for Lapsang Souchong (正山小種)) in a mug of the same proportions as used by my Chinese colleagues. No milk or sugar.

Indeed it is

The postman just brought it. Really chuffed with this

Actually, I tend to prefer statistical approaches to machine learning in this context. Our MTObjects program has shown that you can achieve high recall and precision just by detecting features for which the null hypothesis that the structure is noise must be rejected at the p=10^-6 level, without any assumptions on what sort of structure it is. With machine learning you run the risk that the method will find those structures it has been taught to find, and potentially ignore interesting new stuff. Our adaptive blurring filters are also being optimised by looking at the relationships with optimal kernel density estimation (after all, we want to estimate the density distribution of photons in the image optimally) .Machine learning can and will be used to do a classification of objects into known classes, preferably with an extra class label "Human help needed" for things that look like they don't fit into any known category well.

We are actually looking into exactly that issue in the development of a new software package named Sourcerer, in which we want to find the faintest possible real structures without finding heaps of false positives. Studying the effect of different blurring strategies is part of that research.

BTW, the banding I refer to in the 1.1x case should not so much be visible as dark and light stripes, but more noisier and less noisy stripes. This could perhaps be detected by calculating variance per column or row

The banding is easily explained in the 1.1x case, as the weights by which pixels are added vary periodically. Looking just at the horizontal binning, in the first pixel the weights are 10 and 1, the second 9 and 2, the third 8 and 3, then 7 and 5, 6 and 5, 5 and 6, etc. At the first pixel the expected gain in S/N is a modest 11/sqrt(101), or just 1.0945, with weights of 5 and 6, we get an improvement of 11/sqrt(36+25) or 1.4084. In the case of 1.5x this effect does not occur, because the weights alternate between 2, 1 and 1, 2 (again just looking at 1-D binning). What I would do to debug is to first bin horizontally, and then bin the result vertically.

It means some of the power in the Fourier power spectrum of the noise moves to lower spatial frequencies, so the 1.8x suppression at the highest frequencies will be offset partly by an increase at slightly lower frequencies. A certain bumpiness in the background might be seen if you go pixel peeping

As I take it, you are exchanging uncorrelated noise for correlated noise. In the case of integer binning (2x2, 3x3, etc) the noise in the resulting pixels is uncorrelated, because each of the pixels is the result of the sum of different pixels in the input image. However, in the case of fractional binning, Each pixel contributes to nine pixels in the output, meaning that if a given pixel has a bit of a noise spike this is spread out over 9 pixels in the surroundings, causing local bias.

That looks like a classic dust bunny. You can even make of out the central obstruction of the scope in it. A bit of dust on the window in front of the chip in the camera is the most likely culprit if it appears in all colour bands.