GlueX Analysis Workfest 2018

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Purpose

  1. To discuss analysis tools, techniques, and results required as input for amplitude analyses and absolute cross section measurements.
  2. To actively work in groups on solving these problems, with well defined deliverables to come from the Workfest.

Workfest Tasks

The workfest tasks are split into two teams below. Team mascots and logos are up for debate... suggestions welcome.

R&D Team Tasks (Justin)

Comparison of track and shower efficiencies between data and MC (Simon, Alex A., Jon, Will M., Ahmed)

  • Suggested studies: Missing particle in well-defined final states and measure efficiency of reconstruction
    • Possible reactions for tracking effic.: Missing π or proton in γp → π+π-p, γp → π+π-π0p, γp → π+π-π+π-p
    • Possible reactions for shower effic.: Missing γ in γp → π+π-π0p (FCAL), γp → π+π-π0π0p (BCAL?)
      • Previous studies: Jon Zarling [4], Cristiano Fanelli [5]
  • Deliverable: Initial evaluation of efficiency vs kinematic variables (p, θ, φ) for data and MC
  • Bonus points: Initial evaluation of resolution (measured - missing particle KinFit) for kinematic variables (p, θ, φ)

Comparison of kinematic fit χ2 between data and MC (Daniel, Alex B, Stuart, Mike M.)

  • Suggested studies: Analysis of multiple final states with different constrains (P4, P4+vertex, P4+vertex with mass constraints)
    • Possible reactions: γp → π+π-p (ρ and Δ++), γp → K+K-p (φ and Λ(1520)), γp → π+π-π0p (ω and η), γp → π0γp (ω), γp → π+π-ηp (η'), γp → π0π0ηp (η')
    • Previous studies: Alex Barnes [6]
  • Deliverable: background subtracted χ2 and pull distributions for data and MC for charged, neutral, and mixed final states
  • Bonus points: Prepare for tuning of MC parameters and/or covariance matrices to improve data/MC agreement

Validation Team Tasks (Sean)

Comparison of branching ratios between data and MC (Mark D., Tegan, Mahmoud)

  • Suggested studies: Analysis of multiple decay modes for ω, η, and η' to measure ratio of branching ratios (with MC efficiency correction)
    • Possible reactions: γp → γγp (η), γp → π+π-π0p (ω and η), γp → π0π0π0p (η), γp → π0γp (ω), γp → π+π-ηp (η'), γp → π0π0ηp (η')
  • Deliverable: background subtracted and efficiency corrected ω, η and η' yields
  • Bonus points: investigate dependence on kinematic variables: Eγ, -t, etc.

Cross section evaluation for benchmark channel (Thomas, Simon)

  • Suggested studies: Evaluate all ingredients of cross section: yield, efficiency, flux for benchmark channel
    • Possible reactions: γp → π+π-p
  • Deliverable: scripts to evaluate cross section for benchmark channel with a given version of software/calibrations
  • Bonus points: implement in monitoring database

Event-by-event studies of benchmark channels (Sean, Ashley)

  • Suggested studies: determine yields for pre-skimmed datasets of rare channels
    • Possible reactions: J/ψ, Ξ, Δ++
  • Deliverable: scripts to monitor event yield and/or other observables with a given version of software/calibrations
  • Bonus points: implement in monitoring database

Quantitative comparison of data and weighted phasespace MC for angular dependent analyses (Alex B., Elton, Matt, Carlos)

  • Suggested studies: Fit vector meson SDME using current MC efficiency to compare weighted MC with data
    • Possible reactions: γp → π+π-p (ρ), γp → K+K-p (φ)
  • Deliverable: quantitative comparison (χ2, Kolmogorov-Smirnov, etc.) of weighted phasespace MC angular distribution with data
  • Bonus points: account for background (accidentals, etc.) in AmpTools, and statistical tests of fit parameters (toy MC, bootstrap, etc.)

Workfest Software and Conditions

  • version.xml: /group/halld/www/halldweb/html/dist/version_workfest2018.xml (in progress)
  • sim-recon branch: workfest2018
  • hdds branch: workfest2018
  • gluex_root_analysis branch: workfest2018
  • calibration time: calibtime=2018-05-08-08-00-00
  • calibration variation: mc_workfest2018

Analysis scripts developed for tasks should be stored in the gluex_workshops/workfest_2018 GitHub repository.

Workfest Datasets

2017 Low Intensity Data

  • 3 days at the end of Spring 2017 Low Intensity totaling ~45M events (~10% of the Spring 2017 data)
  • Run numbers: 30730-30788
  • EVIO location:
  • REST location:
  • Analysis TTree location:

MC Simulation Samples

Conditions

  • Run number: 30730
  • Background: random trigger
  • Beam energy range: Eγ = 3 - 11.6 GeV, with coherent edge set at 8.8 GeV

Samples

Generator Number of events (106) Settings Planned usage
bggen 100 General inclusive sample for many studies
gen_etaRegge 10 η → γγ Branching Ratios, KinFit χ2
gen_etaRegge 10 η → π+π-π0 Branching Ratios, KinFit χ2
gen_etaRegge 10 η → π0π0π0 Branching Ratios, KinFit χ2
gen_etaRegge 10 η' → π+π-η Branching Ratios, KinFit χ2
gen_etaRegge 10 η' → π0π0η Branching Ratios, KinFit χ2
gen_2pi_amp 10 Physical distribution for ρ → π+π- and Phasespace KinFit χ2, Efficiencies, Cross section, ρ SDME
gen_omega_3pi 10 ω → π+π+π0 Branching Ratios, KinFit χ2, Efficiencies
gen_omega_radiative 10 ω → π0γ Branching Ratios, KinFit χ2
gen_2k_amp 10 Physical distribution for φ → K+K- and Phasespace KinFit χ2, Efficiencies, Cross section, φ SDME
gen_amp 10 b1 → ωπ0, ω → π+π+π0 Efficiencies
gen_etaRegge 10 η' → π+π-γ Branching Ratios, KinFit χ2

Location and Time

The "Workfest" will take place at:

DATES: May 15 - 17, 2018

LOCATION: CEBAF Center F326

Registration

Please add your name to the list of attendees below and indicate which task(s) you're interested in contributing to. No formal registration or registration fee is required.

Name Participate at JLab (Yes/No) Task you're interested in contributing to
Justin Stevens Yes Track/shower efficiency and accidental subtraction/flux normalization
Simon Taylor Yes Track/shower efficiency and tuning covariance matrices
Sean Dobbs Yes Track/shower efficiency and general MC tuning, statistical tools
Alex Austregesilo Yes Track/shower efficiency and accidental subtraction/flux normalization
Daniel Lersch Yes Tuning the covariance matrix for kinematic fits
Thomas Britton Yes MC tuning and accidental subtraction/flux normalization
Alex Barnes Yes Tuning covariance matrices and statistical tools
Jon Zarling Yes Track/shower efficiency with ω mesons. Tuning of inclusive MC?
Mike McCracken Prob. remote Tuning the covariance matrix for kinematic fits
Mahmoud Kamel Yes validating the track/shower efficiency determination from MC using the η/η' branching ratios
Mark Dalton Yes Neutral particle covariance matrices, neutral shower efficiency, Comparison of branching ratios, Cross section evaluation for ω→3π
Elton Smith Yes Non-linearity in BCAL due to SiPMs, accidental subtraction in AmpTools
Tegan Beattie Yes
Ahmed Foda Yes
Matt Shepherd Yes
Stuart Fegan Yes
Sebastian Cole Yes

Remote Participation

Remote participation will be possible, but the intention is to have a group of people actively working together at JLab. Thus remote participation will not be optimal.

  1. To join via a Web Browser, go to the page [7] https://bluejeans.com/115815824.
  2. More information on connecting to bluejeans is available.

Agenda

Tuesday May 15, 2018

  • 9:00 Introduction of workfest tasks and logistics (60)
    • 9:00 Workfest introduction (10)
    • 9:10 Overview of R&D team plans (20+5)
    • 9:35 Overview of Validation team plans (20+5)
  • 10:00 Begin working session (coffee provided)
  • Lunch on your own
  • 15:00 Informal update on tasks and discussion (coffee provided)

Wednesday May 16, 2018

  • 9:00 Informal update on tasks and discussion (coffee provided) (60)
  • 10:00 Begin working session
  • Lunch on your own
  • 15:00 Informal update on tasks and discussion (coffee provided)

Thursday May 17, 2018

  • 9:00 Informal update on tasks and discussion (coffee provided) (60)
  • 10:00 Begin working session
  • Lunch on your own
  • 15:00 Summary of deliverables achieved and discussion of future work (coffee provided)
    • 15:00 Summary of R&D team deliverables and future plans (60)
    • 16:00 Summary of Validation team deliverables and future plans (60)

Brainstorming on topics for future Workfests

  • Particle Identification (PID) cuts: definition in terms of CL, combine between different detectors, efficiency of cuts in data/MC, etc.
  • Implementation of tuning mcsmear parameters and covariance matrices
  • Statistical tools for validating fit parameter uncertainties in AmpTools