1 / 10

Data Acquisition and Readout Performance Report - DCOPS at University of Wisconsin-Madison

This report details the recent updates on the DAQ system performance for DCOPS, outlined by James N. Bellinger on July 13, 2008. Key improvements include a reduction in readout failures to 0.17%, attributed to previous contention issues. The data quality evaluations include signal-to-noise ratios and various fit parameters, emphasizing the importance of proper sensor alignment and efficient cuts for data integrity. Detailed results from read cycles and adjustments made for laser safety measures are also discussed, highlighting ongoing enhancements to data acquisition processes.

ryan-gibson
Télécharger la présentation

Data Acquisition and Readout Performance Report - DCOPS at University of Wisconsin-Madison

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Cruzet-3 DCOPS James N. Bellinger University of Wisconsin-Madison 13 July 2008

  2. James N. Bellinger 13-July-2008 DCOPS DAQ Situation • Readout failures described on 9-July are down to acceptable levels (0.17%) • Apparently problem was due to contention • Increased the sleep in piped script: readout 20 min long now • Read out 8 Endcap stations, and Endcap portions of Transfer Lines: MAB DCOPS LV not available • Cannot run entire system during day: laser safety • 10 read cycles completed 11-July 0:15-3:35 • 11 read cycles completed 12-July 0:15-3:40 • All full SLMs are blocked • No useful reconstruction w/o 2 reference sensors

  3. James N. Bellinger 13-July-2008 ME+3/SLM2 CCD1 Pt 2 Laser A Laser Line Blocked Laser Line Blocked Pt 5 Laser

  4. James N. Bellinger 13-July-2008 Transfer Line 4 CCD2 Plus Laser No MAB DCOPS LV No MAB DCOPS LV Minus Laser Disks are not aligned. Don’t expect lasers to reach across.

  5. James N. Bellinger 13-July-2008 Variation of Fitted Means RMS of order 1 pixel=14 micron Mean CCD1 from “A” on slide 3 (ME+3/SLM2) Both DCOPS are similar distances from their laser ? Means of CCD1 and 2 for ME+2/SLM1 innermost DCOPS Requiring sane fit (sigma>0) disposes of outlier

  6. James N. Bellinger 13-July-2008 Data Quality • DAQ returns various fit quantities: • Mean • Error on mean • Sigma • Area of signal • Area of background • NDoF • Chi-squared • Error bits

  7. James N. Bellinger 13-July-2008 Data Quality Online Poor signal/noise Perfect Online fit sets bits w/ loose limits. Demanding perfect fit (bits=0) loses some useful data (esp. saturation recovery) Plots are of profile fit means. We took time to center these, so peaking at 1000 is expected and desirable Units are pixels Mean or sigma exceeded loose limits

  8. Data Quality Offline 2 • Simple cuts: • Mean ≥ -10 and mean ≤ 2058 • Fiducial region 10 pixels outside sensor • Sigma ≥ 25 and sigma ≤ 591 • 591=2048/√12 • 25 from looking at plot (WAG) • Signal area > 100 and < 10000000 • Very loose limits from looking at plot and expectations of signal Sigma in pixel units 25 James N. Bellinger 13-July-2008

  9. James N. Bellinger 13-July-2008 Effects of Cuts • Count only DCOPS expected to be illuminated in SLMs: ignore shadowing • No cut 9064 • Mean cut 8576 • Sigma cut 8012 • Area cut 7148 • Mean+Area 6925 • Mean+Sigma 7732 • Area+Sigma 7060 • All cuts 6897 (76%) On the Plus side I have 26/180=14% shadows, so est. 10% unaccounted for

  10. James N. Bellinger 13-July-2008 DAQ is working • May have to increase sleep further when • MAB DCOPS ports are added: 8→10 ports • Failure rate is low • Simple data quality checks probably suffice to clean up oddities.

More Related