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Agenda:

  1. ldmx-sim                                — Cameron/all  (Slides from Cameron)
    1. Cameron showed promising results at the collaboration meeting of his three way comparison: SLIC, hps-sim, ldmx-sim. 
    2. Update on the comparison status.
    3. Access to the ldmx-sim code for HPS.
    4. We should have a formal decision whether to move our MC production tools to ldmx-sim.
  2. Brief Updates:
    1. Kalman Filter  - Robert 
    2. Trigger Diagnostics - iss621
    3. Ecal Calibration - iss638 
    4. New detector from 2019 Survey - iss628
    5. "Provide a default steering file for 2019 production reconstruction” - iss629
    6. Tracking - Track Reconstruction Project.
    7. Updates for stdhep tools (git branch in the package hps-mc: iss143) – Tongtong Cao
  3. AOB/Questions

Notes:

  1. Cameron – ldmx-sim
    1. Some discussion on std-hep as an input format. Omar agrees that this is reasonable "It benefits ldmx-sim to have many input formats". With std-hep file reading capability, compatibility with existing files (and tools) will be OK. Many tools can be put in ldmx-sim though.
  2. Cameron - hpstr update
    1. See slides.
  3. Robert Johnson - Kalman Filter
    1. Robert found an issue with coordinate transformations. Now the results are the same as the hps-java.
    2. He will fill an interface to GBL. This is useful for Millipede, and also the GBL code more easily provides the full co-variant matrix.
  4. Tongtong  - stdhep tools
    1. He found a memory leak in the stdhep tools. This was causing issues running on large files.
    2. Issue with fie open not returning the number of events properly.
    3. Use of random_sample.cc - 
      1. Need to produce more events than N for random sampling. 
      2. random_sample.cc reads in all the input into a vector. This takes a lot of memory.
      3. random_sample.cc picks the input events at random. This means that an estimate of 37% input events are not used, and 37% are used more than once.
        1. Solution1: random_sample_usingInputEventsInOrder.cc  – Just takes the events in order, avoiding the memory issue and the usage issue.
        2. Solution2: Update of random_sample.cc  – Use the std::shuffle to randomize the order of the events. No waste of electrons, and less memory use.
  5.  Tontong - Trigger Diagnostics 2019 
    1. Confluence page detailing development: "Trigger Diagnostics 2019 "
    2. Replaces a number of the drivers that were used for the 2015/2016 data sets, instead of modifying the existing files, creates new "2019" versions.

 

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