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Jose A. Hernando edited this page Dec 3, 2019 · 2 revisions

Design of det-sim using as input track energy deposits

Pipe-lines

  • Define the pipe-lines, the input/output arguments and parameters:

read + branch(s1_wf, s2_wf) + wf + trigger + write

two implementations: MC-full, MC-energy-deposits

  • S1 pipe:

    s1_wf: S1 + s1_at_pmt + s1_pmt_wf

    output: [(add)] per sensor for PMTs

    S1: input: [(x, y, z, E)]

      output: [(x, y, t, nph)]

  parameter: w_s

    s1_at_pmt:

      input [(x, y, t, nph)]

  ouput: [(t, nph)] per sensor

  requires: pmt_psf(x, y, z, id-sensor)

    s1_pmt_wf:

      input: [(t, nph)] por sensor

  parameters: gain, noise, base-line per id-sensor
                         wf_width, wf_sampling_time
  output: (adc) por sensor

  • S2 pipe:

    s2_wf: ie-generate + ie-propagation + ie-difussion + EL + branch(s2_at_pmt + s2_pmt_wf, s2_at_sipm + s2_sipm_wf)

    output: [(add)] por sensor por PMTs and SiPMs

    ie-generate:

          input: [(x, y, z, E)] - deposits

      parameters: wi-parameter, fano-factor

      outputs: [(x, y, z, nie)] - number of ionized-electrons

    ie-progagate:

          inputs : [(x, y, z, nie)]

      output: [(x, y, t, nie)]

      parameters: lifetime or lifetime-function, v-drift

    ie-difusse:

          inputs: [(x, y, t, nie)]

      output:[(x, y, t, nie)]

      parameters: diffusion-coefficients: transverse and longitudinal
                          z-EL, v-drift

    EL:

          inputs  : [(x, y, t, nie)]

      outputs: [(x, y, t, nph)]

      parameters: EL-gain, EL-sigma,

    s2_at_pmt:

          inputs:. [(x, y, t, nph)]

      require: pmt_psf(x, y, z = z_EL, id-sensor) or geometrical_correction_function(x, y, id)

      outputs: [(t, nph)] per id-sensor

      parameters: EL-z, v-drift, pmt_wf_time_sampling

    s2_at_sipm:

          input: [x, y, t, nph]

      require: sipm_psf(x, y, id-sensor)

      output: [(t, nph)] per id-sensor 

      parameters: EL-z, v-drift, sipm_wf_time_sampling

    s2_ pmt_wf:

          input: [nph] per id-sensor

      parameters: gain, noise, base-line per id-sensor
                         wf_width, wf_sampling_time

      output: [adc] por id-sensor

    s2_sipm_wf:

       input: [nph] per id-sensor

   parameters: gain, noise, base-line por id-sensor
                       wf_width, wf_sampling_time

  output: [adc] per id-sensor

wf : s1_pmt_wf + s2_pmt_wf, s2_sipm_wf_s2

trigger:

write:

About PSFs

We can consider different PSFs:

  1. theoretical PSF (starting from Gonzalo's PSF and consider integral in the surface of sensors and reflexions in the light tube)

  2. PSF from Kr data. Consider the Kr maps to simulate electron drift and PMT-PSF. Use Ander's SiPM-PSF from SiPM.

  3. PSF from simulation. Get Kr full simulation and either obtain the Kr-maps or parametize sensor's response à la Gonzalo.

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