1. CCD Image Processing: Issues & Solutions

2. CCDs: noise sources dark current –signal from unexposed CCD read noise –uncertainty in counting electrons in pixels sky “background” –diffuse light from bright sky (usually variable) photon counting –intrinsic uncertainties in reliably counting incoming photons

3. Dark Current Issue: CCD produces signal in every pixel whether or not it’s exposed to light –signal strength is proportional to time Solution: subtract image(s) obtained without exposing CCD –leave CCD covered: dark frame –match dark frame exposure time to source exp. time –obtain multiple images, to decrease uncertainty in dark current

4. Read Noise Issue: detector electronics subject to uncertainty in reading out the number of electrons in each pixel Solution: collect enough photons that read noise is less important than photon counting noise –Some CCD-like devices enable “nondestructive readout” of detector pixels CIDs: “charge injection devices” (used for IR work) multiple reads of CID pixels reduces uncertainty

5. Background Sky Issue: signal from (possibly variable) bright sky introduces source photon counting uncertainties –how much signal was from the source as opposed to the intervening atmosphere? Solution: measure and subtract sky signal –obtain independent images of the sky must be near source images in both time and space –use off-source region(s) of source image

6. Photon Counting Issue: counting of source photons is governed by Poisson statistics –if I detect N photons, the uncertainty in my photon count is root(N) Solution: collect as many photons as possible! –uncertainty decreases like root(N) –so, maximize telescope collecting area (aperture) and exposure time so as to maximize source illumination of detector

7. CCDs: artifacts and defects bad pixels –dead, hot, flickering… pixel-to-pixel differences in quantum efficiency –every CCD pixel has a unique QE saturation –each pixel can only hold so much charge (limited well depth) charge loss during pixel charge transfer & readout –a pixel’s value at readout may not be what it was when light was collected

8. Bad Pixels Issue: a certain fraction of a typical CCD’s pixels will be “dead” (never reporting any charge collected) or “hot” (always reporting more charge than actually collected) Solutions: –replace bad pixel with average value of the pixel’s neighbors –dither telescope take a series of images, move telescope slightly to ensure source(s) falls on good pixels must then register and appropriately combine dithered images

9. Pixel-to-Pixel Differences in QE Issue: each pixel has its own response to light Solution: obtain and use a flat field image to correct for pixel-to-pixel nonuniformities –construct flat field by exposing CCD to a uniform source of illumination image the sky or a white screen pasted on the dome –divide source images by the flat field image for every pixel x,y, new source intensity is now S’(x,y) = S(x,y)/F(x,y) where F(x,y) is the flat field pixel value; “bright” pixels are suppressed, “dim” pixels are emphasized

10. Saturation Issue: each pixel can only hold so much charge (limited well depth), so a bright source may saturate detector –at saturation, pixel stops detecting new photons (like overexposure) –saturated pixels can “bleed” over to neighbors, causing streaks in image Solution: put less light on detector in each image –take shorter exposures and add them together telescope pointing will drift; need to re-register images read noise can become a problem –use neutral density filter a filter that blocks some light at all wavelengths uniformly fainter sources lost

11. Charge Loss Issue: no CCD transfers charge between pixels with 100% efficiency –charge loss introduces uncertainty in converting signal to light intensity (optical) or to photon energy (X-ray) Solution: build a better CCD –most modern CCDs have charge transfer efficiencies of 99.9999% –some don’t, though (soft X-ray sensitive CCDs)

12. Data Pipelining Issue: now that I’ve collected all of these images, what do I do? Solution: build an automated data processing pipeline –Space observatories (e.g., HST) routinely process raw image data and deliver only the processed images to the observer –ground-based observatories are slowly coming around to this operational model –RIT’s CIS is in the “data pipeline” business NASA’s SOFIA South Pole facilities