Difference between revisions of "SoLID Ecal Weekly 20230608"

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(→‎Update from Tuesday Analysis/Sim Meeting)
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**for data: use individual shower calibrated by MIP, not showersum directly
 
**for data: use individual shower calibrated by MIP, not showersum directly
 
**for data, run 4680 had SC-A.and.SC-D and SC-A did not work well, should try the newer run
 
**for data, run 4680 had SC-A.and.SC-D and SC-A did not work well, should try the newer run
−
**for sim, separate the 4 particle types and weigh each by the cross section.
+
**for sim, separate the 4 particle types and weigh each by the cross-section.
  
 
*Darren/Zhiwen is working with Kishan on AI/ML
 
*Darren/Zhiwen is working with Kishan on AI/ML
−
**how does AI/ML PID compare with classical PID for beam test sim?
+
**How does AI/ML PID compare with classical PID for beam test sim?
−
**how well does the trained ML PID work for beam test data?
+
**How well does the trained ML PID work for beam test data?
−
*high-level question to be answered by our study:
+
*High-Level Question to be answered by our study:
−
**is AI/ML PID complimentary to classical method (thus assist classical method), or superior?  
+
**Is AI/ML PID complimentary to classical method (thus assist classical method), or superior?  
  
 
*Update on prescale in data:
 
*Update on prescale in data:
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*with the PS understood, we now have very good agreement in ECal ShowerSum spectrum between different runs (different PS, trigger setup, and threshold). All should be within 15% of the simulation
 
*with the PS understood, we now have very good agreement in ECal ShowerSum spectrum between different runs (different PS, trigger setup, and threshold). All should be within 15% of the simulation
 
*the end point of electron sim has a sharp drop off but the data show slower drop off. Not sure what is causing the difference, anything missing in the simulation? The comparison used Run 4780. Points to check:
 
*the end point of electron sim has a sharp drop off but the data show slower drop off. Not sure what is causing the difference, anything missing in the simulation? The comparison used Run 4780. Points to check:
−
**how much is pileup?
+
**How much is pileup?
 
**simulation threshold should account for Shower block normalization (different scaling factor)
 
**simulation threshold should account for Shower block normalization (different scaling factor)
−
**is the shashlyk length accurate?
+
**Is the shashlyk length accurate?
 
**The MIP calibration is accurate to what level?
 
**The MIP calibration is accurate to what level?
−
**peak-finding for individual shower block may not result in a soft-ware sum the same as the peak-finding of the hardware sum directly.
+
**Peak-finding for individual shower block may not result in a soft-ware sum the same as the peak-finding of the hardware sum directly.
  
 
==update from Darren + Spencer==
 
==update from Darren + Spencer==
−
*Spencer studied TS3 from SC-C.and.SC-D.and.ShowerSum runs -- SC-C, SC-D, and SHowerSum spectra need some study
+
*Spencer studied TS3 from SC-C.and.SC-D.and.ShowerSum runs -- SC-C, SC-D, and ShowerSum spectra need some study
−
*Darren showed AI/ML on ECal and Cherenkov
+
*Darren showed AI/ML on ECal and Cherenkov: [https://solid.jlab.org/wiki/index.php/File:SULI_Week1_ML_PID.pdf SULI_Week1_Slides]
−
**for Cherenkov we need to add position cut
+
**For Cherenkov we need to add position cut
 
anything else (all)
 
anything else (all)

Revision as of 12:09, 9 June 2023

Update from Tuesday Analysis/Sim Meeting

    • looks like back/forward tracking can be added to level0 tree but then it needs to be passed onto level 1
  • Spencer update on TS3 study, suggestion:
    • for data: use individual shower calibrated by MIP, not showersum directly
    • for data, run 4680 had SC-A.and.SC-D and SC-A did not work well, should try the newer run
    • for sim, separate the 4 particle types and weigh each by the cross-section.
  • Darren/Zhiwen is working with Kishan on AI/ML
    • How does AI/ML PID compare with classical PID for beam test sim?
    • How well does the trained ML PID work for beam test data?
  • High-Level Question to be answered by our study:
    • Is AI/ML PID complimentary to classical method (thus assist classical method), or superior?
  • Update on prescale in data:
    • (Jixie, W. Gu): CODA3.0 uses power(2,n-1)+1, so n=6 means 33 and for every 33 events, 1 is recorded.

Discussion on Cooking with Updated Tracking

  • Presentation by Mike:

Discussion on Cooking with Edge-Finding

  • Ye presented the principle of edge-finding, presentation:
  • Carter is working on setting up his code on ifarm.

update on simulation (Ye)

  • with the PS understood, we now have very good agreement in ECal ShowerSum spectrum between different runs (different PS, trigger setup, and threshold). All should be within 15% of the simulation
  • the end point of electron sim has a sharp drop off but the data show slower drop off. Not sure what is causing the difference, anything missing in the simulation? The comparison used Run 4780. Points to check:
    • How much is pileup?
    • simulation threshold should account for Shower block normalization (different scaling factor)
    • Is the shashlyk length accurate?
    • The MIP calibration is accurate to what level?
    • Peak-finding for individual shower block may not result in a soft-ware sum the same as the peak-finding of the hardware sum directly.

update from Darren + Spencer

  • Spencer studied TS3 from SC-C.and.SC-D.and.ShowerSum runs -- SC-C, SC-D, and ShowerSum spectra need some study
  • Darren showed AI/ML on ECal and Cherenkov: SULI_Week1_Slides
    • For Cherenkov we need to add position cut

anything else (all)

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