Processing raw images

DAMIC raw images are stored in the Flexible Image Transport System (FITS) le, a standardized format commonly used in astronomy. The data are formatted as N-dimensional arrays (2D for an image) and stored together with a human readable ASCII header. This header is organized in keywords/value pairs and has the purpose to provide any information about the data stored. In the particular case of DAMIC, it contains variables such as the CCD identication number, an identication number for the run, CCD's voltage settings, readout amplier integration window, the overscan region, total exposure time and readout time for example.

After being taken and stored, the raw images need to be processed to prepare it for data analysis. This processing aims to remove eects such as the constant baseline on the charge measurement coming from the amplier and defective pixels. Also, it minimizes

the noise on the image, suppressing correlated noise induced by the charge transfer. The nal image obtained after this, is ready to one nally searches and recognizes a particle hit at the detector. The manipulation of the raw 2D histogram is done using ROOT software together with an extra library available in the ROOT installation built to deal with FITS le format called FITSIO. In chapter 3, the hardware solutions employed to build a low noise scientic-graded CCD were presented. The following procedures take advantage of the two readout ampliers, as well as the generation of an overscan region and blank images. Figure 4.4 shows an example of a raw image taken by DAMIC in the 1×1mode showing separately each one of these features.

Figure 4.4: DAMIC raw image taken in1×1mode with the overscan region and the real image region identied.

Overscan bias correction

The overscan is a region with virtual pixels generated by setting the CCD clocking to transfer charge further than the actual physical size of the device. As a result, the system reads out pixels containing charge generated by the amplier only. This region is congured in the data taking settings and the information of which rows and columns

belong to the physical CCD or overscan are available at the FITS le header. By making a histogram of the charge collected in the overscan one can study the noise and the baseline generated by the sense node. Figure 4.5 shows an example of this histogram.

8700 8750 8800 8850 8900 8950 9000 9050 9100

Charge q (ADU) 0

200 400 600 800 1000

noisehist Entries 74000 Mean 8902 Std Dev 30.08

Figure 4.5: Example of a charge histogram of the overscan region taken from DAMIC CCD. The mean and and standard deviation are a measure of the amplier baseline and noise, respectively.

In this histogram, the standard deviation is a measure of the amplier noise level.

The mean value is a constant oset value present in the whole image. The overscan bias correction consists in removing this baseline oset intrinsic to the amplier from the entire image.

Noise reduction and bias correction

In general, the CCD raw image presents a pixel-to-pixel structure due to correlated noise. This correlated noise is induced by the charge transfer process and it is a bias that varies across the CCD. Beside the correlated noise, some small non-uniformities could also appear in the image with a variety of causes (a defect in the crystalline structure, for

example). Figure 4.6 shows a raw image taken by DAMIC with symmetric patterns due to correlated noise.

Figure 4.6: DAMIC raw image taken in 1×100 with symmetric patterns on both sides due to correlated noise.

There are dierent methods to remove these structures from the images. One, faster and more suitable for a preliminary analysis such as monitoring, takes advantage of the two-sided image with identical patterns and just takes the dierence between the right and the left side¹. It is an analysis that can be done per image taken, allowing an evaluation of the CCD without the whole processing chain. The main drawback is that this direct combination actually increases the charge uctuations in the pixel as σ_q²_end = 2σ²_qinitial. However, this is not a problem for monitoring purposes in which one wants to verify critical problems in the data taking such as part of the images glowing. Problems like this would not be covered by the charge uctuations.

Making use of the blank image, an image taken with zero exposure that would also present this structure, two possible approaches are followed. The rst, completely equiv-alent to the previous one presented, just takes the dierence between the raw image and the blank. This method presents the same purpose as well as the main drawback discussed before. The other, is the method that DAMIC uses in practice to fully process the raw images. Using multiple images acquired by the same CCD, with a minimum number of images N_images = 3, one can construct a new average image as the linear combination:

Rˆ =R−

N_images

X

i=0

a_iN_i (4.1)

1By convention, right side is the actual CCD exposed image while the left is just the amplier's eect

whereR is the original raw image and N_i are noise frames. Calculating the variance, one Now, one can nd the set of parameters {a_i} that minimizes this variance:

∂

∂a_k

Var( ˆR)

= 0 (4.3)

This equation has an analytic solution and, as a result, one obtains the nal image with the minimum noise possible. For a per CCD analysis, one can use the combination of blanks and the left side image to apply this method. In DAMIC nal processing, used to perform dark matter data analysis, this set of parameters{a_i} is obtained combining all images acquired. Also, the combination of those images is used to create what is called the master bias frame, a frame containing a statistical description of the set of images calculating its mean, median, variance, skeweness and kurtosis. The master bias is used to smooth the image non-uniformities.

Mask

For monitoring analysis, one is not concerned about the particle hits in the CCD. A mask is needed to remove the pixels with high charge deposition belonging to the passage of a particle in the detector. For a given section of the image, one can calculate the median and variance σ_noise of this particular set of pixels. Using this statistical description, one removes pixels that deviate from the median by more than6σ_noise. As DAMIC is located in a low background environment, events on the image are really rare.

In the case of DAMIC nal analysis, mainly the WIMP search, the full processing chain is able to identify a particle passing by the detector by a clustering algorithm.

Charge deposition above the noise level,q_dep >5σ_noise, is counted as an event. Here, the mask is used to remove from the image the so called hot pixels, the one ones that present

consistent high charge measurement over many images. With the statistical variables determined by the master bias, one can identify these pixels.