DQEMCalTests

Installation on MIT's sqtestbench

First install miniconda and create an environment in which to install the latest version of ROOT and python3. Installation instructions can be found here but are given below:

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh

After installing, initialize the newly-installed Miniconda for either bash or zsh shell:

~/miniconda3/bin/conda init bash
~/miniconda3/bin/conda init zsh

Now install ROOT inside the miniconda environment:

conda config --set channel_priority strict
conda create -c conda-forge --name <my-environment> root
conda activate <my-environment>

You are now ready to run the DarkQuest EMCal test scripts below.

Running analyses

Steps:

• make ROOT makeROOT.py

• Optional Step: If doing linear or CNN regression, the selections need to be applied first such that the bulk of data used in the training are the electron events with most of the energy deposit in the calorimeter

  python makeSelections.py
  # linear regression for runs without attenuator or neutral density filter
  python linearRegression.py --start 496 --end 507
  # linear regression for runs with attenuator
  python linearRegression.py --start 506 --end 507
  # linear regression for runs with neutral density filter
  python linearRegression.py --start 585 --end 589
  

• Optional Step: Run the same for CNN regression if needed.

  # CNN regression for runs without attenuator or neutral density filter
  python CNNRegression.py --start 496 --end 507
  # CNN regression for runs with attenuator
  python CNNRegression.py --start 506 --end 507
  # CNN regression for runs with neutral density filter
  python CNNRegression.py --start 585 --end 589
  

• run MIP inter-channel calibrations MIPInterCalibration.py

  # runs with attenuator
  python MIPInterCalibration.py --start 521 --end 526
  # runs with neutral density filter
  python MIPInterCalibration.py --start 614 --end 618
  # runs without attenuator or neutral density filter
  python MIPInterCalibration.py --start 655 --end 655
  

• apply MIP inter-channel calibrations applyCorrection.py

  # runs with attenuator
  python applyCorrection.py -m results/MIPCalib_Run521_Run526.json --start 493 --end 544
  # runs with neutral density filter
  python applyCorrection.py -m results/MIPCalib_Run614_Run618.json --start 563 --end 612
  # runs without attenuator or neutral density filter
  python applyCorrection.py -m results/MIPCalib_Run655_Run655.json --start 642 --end 654
  

• run the signal fit to extract the energy resolution and response runSignalFits.py

  # runs with attenuator
  python runSignalFits.py --start 493 --end 544
  # runs with neutral density filter
  python runSignalFits.py --start 563 --end 612
  # runs without attenuator or neutral density filter
  python runSignalFits.py --start 642 --end 654
  

• plot the energy resolution and response plotResol.py

  python plotResol.py
  

Side script:

• make plots for 1D and 2D energy deposits per channel, regressed per-channel coefficients makePlots.py