PREX2AcceptanceTodoList

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Counting and Optics portal Optics Meetings

• Collimator Definition (Ryan)

1. 1. Verify collimator image in G4HRS reconstruction
   2. Check collimator image in data reconstruction
   3. Verify collimator image in HAMC

• Optics Data (Siyu)

1. 1. Demonstrate residuals in PREX reconstruction data base
   2. Pointing - precision
   3. Verify pointing over sieve holes

• Acceptance function (Ryan)

1. 1. Calculate, histogram polar scattering angle from data (Th_data)
   2. Calculate, histogram polar scattering angle from G4HRS (Th_MC) (post target, assuming beam axis and beam energy)
   3. Calculate, histogram polar scattering angle from G4HRS (Th_V)
   4. from each Th_data, Th_MC, and Th_V, calculate , <Q2>, <A>, d<A>/dR

• G4HRS compare to focal plane data (Hanjie)
• G4HRS check apertures (Cip to get this started?)
• HAMC updates and studies (Devi, Bob)
  1. Fix collimator in hamc. It appears to be rotated and maybe shifted.
  2. Collimator should agree with engineering drawing. (X,Y) shadow of accepted events versus engineering drawing.
  3. Prove that the collimator alone, and not other apertures, defines the acceptance in hamc. It is a check of the HRS model in hamc. (Need same for g4mc). We already assume this is proven to be true for the data. Bob will provide instructions.
  4. Comparisons of data to hamc to certify hamc (need same for g4mc). Compare momentum, Qsq, scattering angle, azimuthal angle, tg_th, tg_ph, focal plane variables X, Y, tantheta, tanphi. Of course, there is redundancy in these, as they are not independent. Bob will provide example macros.
  5. Comparisons of acceptance function for g4mc versus hamc. They should agree exactly ! Even a toy MC should get this right because the acceptance is defined by the angle bite (collimator) and the momentum bite (quartz detector).
  6. Sensitivity studies to evaluate the systematic error caused by imperfections in the acceptance function.
  • Warm up exercise (Devi, Bob)
    1. Use the acceptance function to compute Qsq and scattering angle, and compare these to the data. Bob will provide example macros.
    2. The next step uses the asymmetry values tabulated by Horowitz.
    3. Look at the asymmetry integrated over the acceptance function and see how it shifts (dA/A) with changes in the MC assumptions, e.g. target thickness or detector cut. Ideally dA/A << 1%. If the shifts are small, we are done. This study will require running hamc several times with changes in the parameters. Each run will produce a new acceptance function, and then we run a macro that integrates the asymmetries. Bob will provide an example macro.