[INSTRM-992] Characterize CCD cosmetics Created: 27/May/20 Updated: 08/Oct/20 Resolved: 08/Oct/20 |
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| Status: | Done |
| Project: | Instrument control development |
| Component/s: | None |
| Affects Version/s: | None |
| Fix Version/s: | None |
| Type: | Task | Priority: | Normal |
| Reporter: | hassan | Assignee: | arnaud.lefur |
| Resolution: | Done | Votes: | 0 |
| Labels: | SPS | ||
| Remaining Estimate: | Not Specified | ||
| Time Spent: | Not Specified | ||
| Original Estimate: | Not Specified | ||
| Story Points: | 4 |
| Sprint: | SM1PD-2020 G, SM1PD-2020 G2, SM1PD-2020 H, SM1PD-2020 J |
| Description |
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Following discussions during ICS-DRP-SpS telecons in April 2020, it was pointed out that cosmetics were missing in the SM2 characterization slides distributed in email [PFS-SpS:04886]. Murdock Hart provided cosmetic information in his B1/R2 reports: Check if there is a means of doing this using DRP or an alternative. |
| Comments |
| Comment by hassan [ 27/May/20 ] |
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jeg in his email [PFS-SpS:04908] proposes the following: Naoyuki reminded us about cosmetics. I hope we have data. The simple way to identify hot pixels is to median together at least 3 bias-sibtracted darks, scaled if necessary by their exposure times if those are different. Find the 3-sigma clipped standard deviation of this frame. If you have at your disposal in the processing libraries make a median-smoother, make a version of this fram with smoothing boxes maybe 65 pixels on a side (?) to get rid of large-scale gradients and subtract this smoohed frame from the original. (This is probably not necessary). Look at the result. Identify pixels above about 10 sigma and catalog them The other kind of defect is parallel traps. Without imaging, they are very hard to localize, though one can find the columns in which they occur by looking for tails from medium-level flats into the vertical overscan. Charge-transfer inefficiency will typically result in one high pixel in a `good' column. A trap will result in a several-pixel tail extending up the column in which the trap occurs. I do not believe these detectors have significant traps, but we should look. They can be localized by the 'pixel-pumping' technique, but we have not implemented it in the CCD code, though we could if they exist and we need to. |
| Comment by hassan [ 27/May/20 ] |
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Merlin Fisher-Levine (LSST) suggested to use the following LSST code: https://github.com/lsst/cp_pipe/blob/master/python/lsst/cp/pipe/defects.py
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| Comment by hassan [ 14/Aug/20 ] |
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CTE and trap analysis outstanding. |
| Comment by arnaud.lefur [ 08/Oct/20 ] |
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I have some traps finding routine. |