Nebulosity Free Download
Samir Quinlan
Hi, this is my first time writing in cloudynights. So first things first, my proplem is that i photographed pleiades last night and when im trying to process it in photoshop/gimp the nebulosity won't pop up. There is literally no nebulae in the picture. When im trying to look with threshold it shows only gradient and stars. I hope someone can help me.
The other factor is transparency. If it is not particularly good then it can wipe out nebulosity like an eraser was taken to your image. That happened more than once to me on hour-long exposures on film of the Pleiadies (coincidentally) in very dark skies. Lots of stars, very little nebulosity.
The nebulosity is not emission as in OIII and Hb but reflection, and the filter reads that it is constructed along the lines of a UHC filter. And I would half think the wavelengths you want are blocked, or reduced.
In general, however, in skies where you can, on a good night, barely see the Milky Way (that also describes my own skies from home), one hour is not really enough for a decent image of any nebulae except the brightest like M31 and M42. If you are shooting through a light dome, it's even worse. If you remove that filter and try for the Pleiades again, one hour will get you some nebulosity but it will look a little grainy. You could smooth it with noise reduction.
I have decided what to photograph today or should i say tonight is soul nebula IC 1838. It has red emission nebulosity wich is very good for my filter. I now see the nebulosity in my cameras raw images (not processed, im currently photographing it) This is my first day on cloudynights and this site is very good thank you for all the replies. Clear skies!
Or alternatively, how dark do your skies need to be? I've always thought that the Pleiades were gorgeous with that beautiful nebulosity, but of course, I've never seen it. Can it be seen visually? Under what circumstances?
I have observed the beautiful filaments of M45 through my 10" f/6. As others have posted, you need good dark skies also I have found in the winter after a cold front has passed through and the cold temperatures have dropped below 32 or more the nebulosity stands out even brighter. I usually start out with a wide field 32mm eyepiece and then a 26mm or a 20mm Meade research grade eyepiece.
A broad-band "LPR" filter like the Lumicon Deep-Sky or Orion Skyglow might help a little, but from heavily light-polluted skies, you still probably won't be able to see the nebulosity well even with a filter. I find that from my home (ZLM 5.3 to 5.6 or so), I get a mild boost in the Pleiades nebulosity with my Skyglow filter on my 100mm f/6 refractor, as it is often difficult to see without the filter from my house. Outside of town, I have little trouble on moonless nights seeing at least the Merope component, although most of the rest of the nebulosity is more of a diffuse glow than anything with a lot of structure. The Merope component is a broad slightly curved diffuse fan of light that extends around and south of Merope up to about 15 to 20 arc minutes away from the star, with a slight darker area in the fan's south-western portion. In my 10 inch from my dark sky site (ZLM 6.2 to 6.8 typically), I can sometimes see a very faint broad filament going north from Maia and maybe an enhancement of the glow towards Sterope, but otherwise, there is little other structure. Clear skies to you.
I live to the West of Seattle, and I've not measured the skies for darkness at our site, but we had a public event last night, and one of the scopes out was our big 16" Dob. As Pleiades came up in the east, I could look at it through this magnificent telescope and a 2" 40mm EP, and with all that aperture, I was unable to see any of the nebulosity. Lost in the sky glow, even at midnight. On the other hand, as it moves up past 30 or so above the horizon, I expect to be able to get some awesome views, and photographs of it.
I am having trouble bringing out nebulosity in M45 Pleiades during processing. I use Photoshop CS5 with Annies Astro Actions, Astronomy Tools, and GradientXterminator. Here is a Dropbox link to the raw 16 bit TIFF file that was stacked in DSS with 27 x 7min subs (3.2 hours total integration), 30 flats, 30 bias, and 30 darks: =0
I used an unmodified Pentax K1 at ISO 200 on a Takahashi FC-76DCU with flattener mounted on a Takahashi EM-11 Temma 2Z and autoguided with a Lacerta MGEN II. I have seen other images of M45 that have less integration time using unmodified Pentax K3 and K5's with far far far more nebulosity than my image of M45 using a K1, so I am clearly doing something wrong here!
It depends very much on how dark your skies are. You're almost certainly looking at images of M45 taken from darker skies than yours. The nebulosity is quite dim, and easily washed out in typical suburban skies.
My biggest issue with editing nebulae so far has been that whenever I stretch the image, I always really intensify the stars too. So while I can bring out nebulosity, in the end it almost gets drowned in stars.
An argument based on the relative rates of contractive and nuclear (hydrogen-burning) evolution for stars of masses 3 to 20 m0 enables an estimate to be made of the number of still-contracting stars of these masses within an observable distance of the sun. For example, within 1 kpc of the sun and 100 pc of the galactic plane one would expect there to be, somewhere in their contractive phase, about 18 stars that will in time reach the main sequence at types B2 and B3. A purely empirical attempt was made to identify some of these objects by examining in detail a list of 26 Be- and Ae-type stars that both lie in obscured regions and illuminate nearby nebulosity. The list contains such well-known variables as T Ori, AB Aur, RR Tau, Z CMa, and R Mon, as well as some newly found emission-line stars. In the course of the investigation two new variable nebulae were found. Two main types of stars were encountered: one with emission lines mainly of hydrogen plus absorption features due to a weak overlying shell; and another group with higher velocities of ejection, stronger emission lines, and line structure of the P Cygni type. Although it is entirely possible that this list of peculiar objects does contain examples of still-contracting stars of large mass, no convincing proof of this supposition could be found. The essential reason was that, although there are some striking spectroscopic peculiarities among the stars examined, at the dispersions employed in this investigation the peculiarities did not appear to be unique to this group: they may be found as well in stars that are not associated with nebulosity.
This challenge is about seeing nebulosity around the brighter stars of the cluster but, before we do that, how many stars can you see with the naked eye? Most people will see around six under dark skies but there are good reports of people who have seen more than 10!
For many years the nebulosity and cluster were assumed to be two sides of the same coin, however, we now know that the dust cloud which creates the nebula is moving in a different direction to the stars in the cluster, meaning they must have come from two different places. We now expect that one day, many, many years from now, the nebula and stars will part company.
Most object cataloguing software works by generating a "smooth" local single pass map of the background, or sky, on some specified scale and then proceeds to detect significant features, or objects, with respect to this local background estimate (e.g. Irwin 1985; Bertin & Arnouts 1996). The most common method used to estimate a local background is to split the image into a coarse grid of background pixels (e.g. each with, say, 100x100 image pixels), robustly estimate some sort of clipped "average" background value, and then construct an interpolated "smooth" background at the original pixel sampling. This method works extremely well in most cases and provides an excellent compromise between tracking (following) small scale background variations -- which are robust against both systematic and rms noise problems -- and being executable in an efficient and routine manner.
However, in certain situations, particularly in regions of bright, spatially-varying nebulosity, traditional background following is insufficient. This is illustrated in Figure 1, which shows WFCAM K-band observations of M17, taken as part of the UKIDSS GPS survey. Panel (a) shows the original image, panel (b) the view as seen by object detection software after removing the varying background using a standard grid-based approach, in this case with the CASU pipeline default 128x128 pixel (25x25 arcsec) sub-sampling for 2x2 interleaved data.
To some extent using ever finer grids alleviates the problems seen in these background-subtracted data. However, one quickly hits the buffer of introducing significant magnitude-dependent systematic errors in object photometry caused by dark, over-corrected halos around brighter objects (see e.g. Figure 1(c), which shows the results of using a 64x64 pixel grid).
A similar, more exaggerated problem occurs in conventional unsharp masking, due to the (even more) inherently linear nature of the procedure. However, unsharp masking does have one excellent attribute: a superbly flat background (Figure 1(d))!
Conventional unsharp masking works by subtracting a smoothed version of an image from itself (usually the image is smoothed using a Gaussian kernel). The smoothing, or convolution, suppresses the higher spatial frequency components of the image; the difference image then does the opposite and suppresses the lower frequency components. Any similar filter in Fourier space has the same effect and the same issues, namely that some part of the lower spatial frequency components of objects of interest in the image also get affected. The outcome is typically a dark halo (a hole) in the background around each object. These halos are particularly obvious around brighter objects, and lead to systematic problems with the photometry.
However, the difference image background is beautifully flat over most of the area (apart from the induced "ringing" artifacts around the brighter objects). A simple improvement is to swap the linear smoothing operator for a non-linear scheme based, for example, on non-parametric two-dimensional median filters. In practice something like a bi-linear median filter (a cross-shape) followed by a simple linear box-car filter (also a cross-shape) does a much better job of separating components of the image that are varying on different scales. The "negative-going" artifacts around fainter objects do then disappear quite effectively, although the dark halos around bright objects are still apparent (albeit at a much lower level than before).
The next stage in the improvement is based on the fact that the difference image is mostly comprised of a flat background + noise + objects with adjacent artifacts. Both the background level and the equivalent rms noise can be robustly estimated using iteratively clipped medians and "MADs" (Median of Absolute Deviation from median, e.g. Hoaglin, Mosteller and Tukey 1983). The remaining features, the objects and artifacts, can then be masked out using k-sigma clipping and the filtering operation repeated. In practice this procedure converges within a few iterations and effectively decouples features within the image into large scale structures (the background and/or most of the nebulosity) and small scale structures (the objects of interest). This is illustrated in Figure 2, where the image has been spatially split into features smaller in scale than 10 arcsec (the left-hand panel) and greater in scale than 10 arcsec (the right-hand panel).
Object detection and parametrisation in the "nebulosity filtered" image is now much simpler and more reliable. Tests on astrometry, photometry and morphological shape discriminators suggest the process improves all three and has a negligible impact on biassing the photometry of brighter point sources until they have already saturated. For large, extended sources of similar or larger scale to the filtering scale length, this is obviously not the case -- but then even conventional survey-style object cataloguing has problems with these objects.
Our nebulosity filtering method can be applied to almost any type of background variation, since the non-linear nature of the filtering even allows for good tracking of step changes in the background, e.g. as seen in reflection halos around bright stars. Indeed, the filtering can convert unusable images into something you might even want to do photometry on -- as in the MegaCam image of the region around Beta Andromedae (Mirach) and Mirach's ghost, NGC 404 (Figure 3).
Figure 1: A WFCAM K-band imaging of M17. (a) the original image; (b) the image after removing the varying background using a standard grid-based approach (128x128 pixel grid); (c) after removing the background using a smaller 64x64 pixel grid; (d) the effects of unsharp-masking.
Figure 2: Spatially splitting the image of M17 up into small-scale features (the stars) and large-scale features (the background and nebulosity) using iteratively clipped non-linear filtering (see text for details).
Figure 3: Images of the bright star Beta Andromedae (Mirach), before (left) and after (right) nebulosity filtering. Mirach's ghost, NGC404, can be seen much more clearly in the right-hand panel. Note particularly, the absence of dark halos around other saturated stars and the still intact edges and level changes around Mirach.
See attachment for figures.