![]() ![]() With making a few assumptions about setup, such as the slit widths and separations being << the distance to the detector and applying various constants of proportionality, you'll find that in the limiting case, the diffraction pattern is the magnitude squared of the spacial Fourier transform of the slits. As it happens, diffraction patterns are intimately related to Fourier analysis. ![]() Ī bit of a side note: If you're familiar with Fourier analysis, it might help here. It all depends on how these troughs/nulls line up. With that, there isn't any general rule about the off-center peaks of the pattern, or even the average pixel value of an off-center lobe. Move the slits farther apart and you'll get more frequent nulls. Move the slits closer together (while keeping the slits widths unchanged) and you'll get fewer, additional nulls in the pattern. The additional, and more frequent, troughs/nulls of the double slit pattern are dependent on the separation of slits. If you make the slit width more narrow, the whole pattern becomes wider. "nulls") of the single slit pattern are a function of the slit widths. In other words, if you were to switch to a new Bahtinov mask by a different manufacturer, they might line up differently. (1293 vs 648) terms of the peaks of any of the side-lobes (anything other than the central peak), it depends how the troughs (from destructive interference) from a single slit pattern line up with the additional troughs from having two slits.Īnd whether they line up at all, or how they relate to each other even if they don't line up, is dependent on a) the width of the individual slits and b) the separation of the slits. It’s also interesting to note if you look on the tables on the right hand side for the single versus double slit, in the selected area of interest, the max pixel value is almost exactly double not quadruple (1.99x higher). Since measuring the average background noise and then subtracting this value from each pixel doesn't remove either dark current or the bias offset I'm confused why you would so so. You could use flat frames, but as long as we're dealing with only a small part of the sensor and there aren't large dust particles on the filters and sensor window that are casting shadows on this area we don't really need to worry about flats. These should be the worst sources of unwanted counts in your images and their removal is just about all you need to do if you're just worried about counting pixel values. This will get rid of both the bias value and the average dark current in each pixel. To remove dark current (not dark noise, which cannot be removed) one must take dark frames, combine them together to get a master dark frame, and then subtract this frame from each target exposure. What can be removed is the average dark current and the bias value (the other sources of unwanted signal can also be removed, but it is much more difficult and well beyond the scope of this post). The funny thing with noise is that it simply cannot be removed. But all sources of unwanted signal add noise because they are real sources and obey the same statistical nature as your target. The bias value itself isn't noise, as it's just a set value added to each pixel and is easily removed. In addition, the camera itself typically adds some set value to each pixel, called an offset value or bias value. The major sources of unwanted signal are dark current, background light that falls onto the same area of the sensor as your target, and stray light from internal reflections or other optical issues. The point of calibration is to remove counts that aren't due to capturing your target's light. Just so we're on the same page, noise is the random variation of counts or pixel values between pixels in an image or between the same pixel in multiple images. Why would you subtract the average background noise? Unless I'm mistaken, that's not going to get rid of the counts from the dark current nor the bias counts. I've never heard of this calibration method. I did… I sampled the dark noise and subtracted it…
0 Comments
Leave a Reply. |