https://pfspipe.ipmu.jp/jira This file is an XML representation of an issue en-us 8.3.4 803005 13-09-2019 https://pfspipe.ipmu.jp/jira/browse/INSTRM-992 Instrument control development <p>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 <span class="error">&#91;PFS-SpS:04886&#93;</span>.</p> <p>Murdock Hart provided cosmetic information in his B1/R2 reports:</p> <p><a href="https://pfs.ipmu.jp/internal/webdav/tech/SpS/Document_Project_LAM-Mirror/JHU/Detector_systems/AIT-V/PFS_FPA_B1_characterization_report.pdf" class="external-link" rel="nofollow">https://pfs.ipmu.jp/internal/webdav/tech/SpS/Document_Project_LAM-Mirror/JHU/Detector_systems/AIT-V/PFS_FPA_B1_characterization_report.pdf</a></p> <p><a href="https://pfs.ipmu.jp/internal/webdav/tech/SpS/Document_Project_LAM-Mirror/JHU/Detector_systems/AIT-V/PFS_FPA_R2_characterization_report.pdf" class="external-link" rel="nofollow">https://pfs.ipmu.jp/internal/webdav/tech/SpS/Document_Project_LAM-Mirror/JHU/Detector_systems/AIT-V/PFS_FPA_R2_characterization_report.pdf</a></p> <p>Check if there is a means of doing this using DRP or an alternative.</p> INSTRM-992

Characterize CCD cosmetics

Task Normal Done Done arnaud.lefur hassan SPS Wed, 27 May 2020 12:08:12 +0000 Wed, 7 Oct 2020 16:47:09 +0000 Wed, 7 Oct 2020 15:47:24 +0000 0 1 <p><a href="https://pfspipe.ipmu.jp/jira/secure/ViewProfile.jspa?name=jeg" class="user-hover" rel="jeg">jeg</a> in his email <span class="error">&#91;PFS-SpS:04908&#93;</span> proposes the following:</p> <div class="code panel" style="border-width: 1px;"><div class="codeContent panelContent"> <pre class="code-java"> 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 <span class="code-keyword">if</span> necessary by their exposure times <span class="code-keyword">if</span> those are different. Find the 3-sigma clipped standard deviation of <span class="code-keyword">this</span> frame. If you have at your disposal in the processing libraries make a median-smoother, make a version of <span class="code-keyword">this</span> fram with smoothing boxes maybe 65 pixels on a side (?) to get rid of large-scale gradients and subtract <span class="code-keyword">this</span> 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 <span class="code-keyword">for</span> 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 <span class="code-keyword">do</span> not believe these detectors have significant traps, but we should look. They can be localized by the <span class="code-quote">'pixel-pumping'</span> technique, but we have not implemented it in the CCD code, though we could <span class="code-keyword">if</span> they exist and we need to. </pre> </div></div> <p>Merlin Fisher-Levine (LSST) suggested to use the following LSST code:</p> <p><a href="https://github.com/lsst/cp_pipe/blob/master/python/lsst/cp/pipe/defects.py" class="external-link" rel="nofollow">https://github.com/lsst/cp_pipe/blob/master/python/lsst/cp/pipe/defects.py</a></p> <p> </p> <p> </p> <p>CTE and trap analysis outstanding.</p> <p>I have some traps finding routine.<br/> I will continue my work on it while processing CCD_B3 data.</p> Development Rank 0|02qpuc:i6001gr068fhw9qhza Sprint SM1PD-2020 G SM1PD-2020 G2 SM1PD-2020 H SM1PD-2020 J Story Points 4.0