Images
- We chose the Alpha Persei cluster (Cr 39) to look at, but as it is a large cluster, we took images of two different sections of it, example images of which can be seen below in the visual range.
- Location 1 (Centred on HIP16001) image at 10s exposure.
- Location 2 (RA = 2h29m29s, DEC = 49deg44'16'') image at 60s exposure.
Analysis of Obervation 1
- First set about investigating the background levels for the images in order to be able to form thresholds in order to identify stars,
- Checked for both locations investigated, formed in tables below.
Location 1 (Centred on HIP16001)
Filter |
Typical Value of Mode(pixels) |
Exposure Time (s) |
Visual |
196 |
10 |
Blue |
200 |
90 |
Green |
210 |
40 |
Red |
230 |
40 |
Location 2 (RA = 2h29m29s, DEC = 49deg44'16'')
Filter |
Typical Value of Mode(pixels) |
Exposure Time (s) |
Visual |
590 |
60 |
Green |
355 |
120 |
Red |
411 |
120 |
- Note here that due to time constraints blue filtered exposures were not taken for the second location.
- It can be seen that for location 2, typical values for the mode are 2-3 times higher than for the first. This is perhaps due to the larger amount of stars in the image causing a brighter background, and perhaps due to the exposure times, although this does not appear to have a 1 to 1 correspondance with the mode.
Finding average dark and bias frames
- During our observations we took 5 120s dark frames.
- These were averaged in order to create a .fit file of these averaged values, which can be used to account for dark current effects etc in these images. The average dark frame is attached to this page.
- The 120s image can then be scaled down and used for the other exposure images seen above.
- 10 additional bias frames were also taken, and an average frame of these generated, for similar reasons as above.
Detecting pixels above threshold
- A threshold was set such that
- where a is some multiplicative factor, such that if we want something within 3 standard deviations,
.
- The following images are of one of the visual images for Location 1 at threshold where
and
respectively. White represent the pixels or the background removed.
- Threshold: Found the mode over the entire image, set threshold as described above.
- Image Scanning: Wrote a loop which runs over each element in the image, and compares the pixel value to its 8 nearest neighbours. If the central pixel has a larger pixel value than its neighbours, we define this as the centre of the star, and note the x and y coordinates.
- This was performed on one of the location 1 visual images for a equal to 5 and 10, see figures below for comparison overlays showing the original image and the identified points.
- When
we observe detections around the brightest star. This is not accurate and a zoomed in version can be seen below.
- It appears as if there is a luminous ring around the star and is separated from the star.
- At 10 standard deviation threshold the following is seen.
- So now we have x and y positions of each star which depend on the threshold value.
Gaussian Fitting
- Next, Gaussians were fitted to the found stars:
- Using x and y positions of each star, each star was cropped around within a arbitrary range (
20 pixels).
- Then a Gaussian function:
- Some of the fitted stars can be seen below. The contours are the contours of the fitting values.
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DavidHadden - 17 Nov 2015