M81/M82 in Ursa Major is a popular target of astro imaging, however only some astrophotographers know “IFN”, and even fewer have imaged IFN surrounding M81/M82. I captured my first M81/M82 with STF8300C OSC CCD exactly one year ago, which is shown here. Back then I have never heard IFN, needless to say that I did not try to capture it (I dont think I could even if I did). Since then I have read some articles and seen a few images on internet about IFN, which made me wonder how easy/difficult to capture it with my slow telescope (F/7). Since my new STF8300M should be more sensitive than STF8300C so I decided to give it a try.
It was a pleasant start at Henry Coe State Park, mild and calm weather, with excellent transparency and seeing, but wind started to pick up and seeing degraded drastically, so I had to stop taking Lum subs and switch to RGB. The winds subsidized ~4AM and seeing improved, though not as good as the beginning. I was able to take ~90min Lum and 60min RGB each.
I was not very confident whether 6 Lum subs were sufficient (my plan was 12 subs). After getting home I loaded Lum subs to Pixinsight, to my surprise I was able to see the hint of IFN without much processing.
The following two images are the final results, they are processed the same except that one is stretched with Masked Stretch, and the other with Histogram Transformation. As expected, Masked Stretch gives better color saturation. (however I have to use HT stretched L since HDRWaveletTransformation does not work well on MT stretched L, not sure why because it does work on MT stretched RGB).
In both images, IFN is clearly visible, esp in the region below M82, above M81, and below right tip of M81. It should be noted that due to light pollution, there was severe background gradient in RGB image, so the color of IFN on top left corner is probably the residual color of back ground extraction. The color of IFN below M81 and M82 should be more neutral though I am not completely sure.
There are two major challenges when processing the image:
– The background gradient of RGB image. The gradient is pretty bad due to light pollution from silicon valley, Pixinsight DBE tool removes majority of gradient but there is still some residual visible.
– Background noise reduction to bring out faint IFN. I find for this image (also many images with large portion of background), TGV tends to amplify the large scale background noise in linear image which is difficult to manage after stretch. It is more efficient to use AT for linear noise reduction and ACDNR for back ground noise reduction after stretch.
On 2/23, I took 8 Ha subs and re-processed the image. There is slight more blue tint on the back ground than the original image.
Location: Henry Coe State Park, Morgan Hill, CA
Condition: excellent transparency, excellent to poor seeing, calm to breezy
Location: MonteBello OSP, CA
Condition: excellent transparency, below average seeing, breezy, dry
Equipment: AT111EDT on CGEM, guided by SSAG+AT72ED. STF8300M+FW8+Baader LRGB
Exposure: L – 6x900s, R – 12x300s Bin2x2, G – 12x300s Bin2x2, B – 11x300s Bin2x2. Ha – 8x900s Bin1x1 (2/23/2015), Dark/Bias/Flat calibrated