Changes between Version 1 and Version 2 of GPC_Dark_Trends

Timestamp:

Feb 24, 2009, 4:31:33 PM (17 years ago)

Author:

trac

Comment:

--

GPC_Dark_Trends

@@ -3 +3 @@
 I analyzed a set of 140 dark exposures taken during run 1c in the period from 02/08/08-02/11/08. The exposuretimes range from 12 to 100s, the detector temperatures range from -90 to -60. The figure below shows expodure time vs. temperature of the dataset: 
 
-[[Image(htdocs:/images/time_vs_temp.jpg)]]
+[[Image(htdocs:/images/Time_vs_temp.jpg)]]
 
 ==== ccd36/cell2 analysis ====
@@ -9 +9 @@
 I selected a region x=461..510,y=81..130 in chip36/extension2 with a relatively prominent dark feature:
 
-[[Image(htdocs:/images/selected.jpg)]]
+[[Image(htdocs:/images/Selected.jpg)]]
 
 In the first step I took the average of the region and made a second order polynomial fit to the exposuretime (DARKTIME keyword). I found that the first two exposures of each of the 10 dark series have significantly lager counts that the other ones. I plot the residuals after subtracting the best fit as a function of exposure number withing series (different series color-coded):
 
-[[Image(htdocs:/images/dark_number3.jpg)]]
+[[Image(htdocs:/images/Dark_number3.jpg)]]
 
 The first blue point s not displayed and has a residual value of 200. From now on I dropped the first two exposures of each series. The two figures below shows the linear fit in exposure time of the remaining 120 dark images and the residuals as a function of temperature (DETTEM keyword):
 
-[[Image(htdocs:/images/timefit.jpg)]][[Image(htdocs:/images/tempres.jpg)]]
+[[Image(htdocs:/images/Timefit.jpg)]][[Image(htdocs:/images/Tempres.jpg)]]
 
 There seems to be no clear 2nd order trend like you would expect it(monotonically rising with temperature), so I decided to look at the 30s exposures only and fitted a linear relation to the dark current as a function of temperature:
 
-[[Image(htdocs:/images/temp_30s.jpg)]]
+[[Image(htdocs:/images/Temp_30s.jpg)]]
 
 The next step is to do the analysis pixel-by-pixel. I used again the 120 images with different exposure times and fitted a linear relation in time and linear relation in temperature. The coefficients for each pixel are stored in a fits file:
 
-[[Image(htdocs:/images/a1_1.jpg)]][[Image(htdocs:/images/a1_2.jpg)]]
-[[Image(htdocs:/images/a2_1b.jpg)]][[Image(htdocs:/images/a2_2.jpg)]]
+[[Image(htdocs:/images/A1_1.jpg)]][[Image(htdocs:/images/A1_2.jpg)]]
+[[Image(htdocs:/images/A2_1b.jpg)]][[Image(htdocs:/images/A2_2.jpg)]]
 
 Using this basis a dark of any arbitrary exposure time and temperature can be created: dark = a1_1 * time[s] + a1_2 + a2_2 * temperature[C] + a2_2.
 Of course, a1_2 and a2_2 could be combined. Below there are some example residual images:
 
-[[Image(htdocs:/images/res1.jpg)]][[Image(htdocs:/images/res2.jpg)]]
+[[Image(htdocs:/images/Res1.jpg)]][[Image(htdocs:/images/Res2.jpg)]]
 
 Also interseting to see are the nchi-images which show the quality of both fits, nchi = sqrt( SUM[ dark_residual^2 ] / N ):
 
-[[Image(htdocs:/images/nchi1.jpg)]][[Image(htdocs:/images/nchi2.jpg)]]
+[[Image(htdocs:/images/Nchi1.jpg)]][[Image(htdocs:/images/Nchi2.jpg)]]
 
 Cosmics are clearly visible and should be removed to identify the pixels that are really bad. To do this I included an iterative kappa-sigma-clipping to the fit. This is the resulting nchi-image of the time-fit:
 
-[[Image(htdocs:/images/nchi_better.jpg)]]
+[[Image(htdocs:/images/Nchi_better.jpg)]]
 
 Ok, this particular cell (chip36/extension2) seems to be quite 'friendly', meaning correctable. But what about the others?