WAC Notes July 6 2021

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RCDB HALOG HAPLOG DocDB Online Prompt BlueJeans Link Runlist spreadsheet, respin 1 spreadsheet

PREX Main << Weekly Analysis Coordinator << WAC Notes CREX << WAC Post-Run_Meeting Notes

WAC Notes June 22 2021 << >> WAC Notes July 13 2021


Date and time: July 6, 2021, 11am
Bluejeans ID: 564945377
Meeting link: https://bluejeans.com/564945377
Runlist spreadsheet

July 6th

No update, but Ryan will talk next week about AT stuff

June 22nd

Decided to turn these meetings into a series of thesis-work updates and long-paper technote presentations.

May 18th 2021

Comparing respin2 to 1, changes, etc.

  • Conclusions decided during the meeting:
    • Respin2 run list looks pretty good. Documented on the HAPLOG
    • Dithering vs. regression outputs from respin2 multipletwise plots look good - see HAPLOG
    • Aq wien state dependence actually increased a bit, likely due to the inclusion of more data in respin2 dataset
    • The known Aq wien state dependent large Aq immediately after a trip (obtained from cutting SinceLastTripEnd < 250) got much larger, which I suspect is due to Fewer fake-trips being included which had diluted the post-trip large Aq problem in the respin1 results (respin 1 had a bunch of mislabeled beam trips due to the bcm_an_diff burp cut being considered a beam trip)
    • There are no noteworthy main detector asymmetry OR diff_bpm dependences on wien state or SinceLastTripEnd (which is comforting)
      • There is a 1 sigma effect in the BPM 12X data, which could really be some sort of Aq pickup... our BPM pedestals are not better than 2% precise
    • The data-inclusion comparison between respin1 and 2 looks good. A lot of new data in CREX part 3 is found, partly due to fixing the bcm_an_diff burp cut, and also due to improving cuts everywhere
    • A pull plot of reg_asym_us_avg looks really nice
      • The only non-gaussian portions can be attributed to lower current running and/or to a few outlier events
      • It would be nice to do a weighted histogram filling, where the weight comes from the minirun-wise mean-error, which doesn't exist in the mul-plot data, but does exist in the aggregator, and would be feasible to add into these trees and see what kind of result we get that way