Simulate Ideal Pulses

You can also download this tutorial as a Jupyter notebook and a plain Julia source file.

Here we will use an example pet file csv format from LegendTestData corresponding to the Public Inverted Coax.

using LegendGeSim
using Plots

Get inputs from legend-test-data

using LegendTestData

Detector metadata

ldsim_path = joinpath(legend_test_data_path(), "data", "ldsim")

detector_name = "invcoax-metadata"
detector_metadata_filename = joinpath(ldsim_path, detector_name*".json");

Alternatively, enter your own path to a real LEGEND detector JSON

detector_metadata_filename = "path/to/V04545A.json"

PET input file

path_to_pet_file = joinpath(ldsim_path, "single-invcoax-th228-geant4.csv");

Settings

See manual on Field Simulation and Ideal Pulse Simulation for a detailed explanation of environment and simulation settings, as well as the noise model settings

environment_settings = Dict(
    "crystal_temperature_in_K" => 77,
    "medium" => "vacuum",
);

Simple settings for point charge simulation with dummy constant impurity

simulation_settings = Dict(
    "method" => "SSD",
    "cached_name" => "", # a non-empty string will cache the simulation results
);

noise_model = Dict(
    "type" => "sim"
);

# Simulate ideal pulses

Dict{String, String} with 1 entry:
  "type" => "sim"

Provide an optional argument n_waveforms to define the number of pulses to be simulated. Default: all pulses based on the input file

pss_table, pss_truth = LegendGeSim.simulate_pulses(detector_metadata_filename, path_to_pet_file, environment_settings, simulation_settings; n_waveforms=5);

[ Info: ---------------------- pet -> stp (stepping info)
Processing file: /home/runner/.julia/artifacts/11b2a7ab47d6f84b449b8f16112d0f3489ec697d/legend-exp-legend-testdata-cd70a8d/data/ldsim/single-invcoax-th228-geant4.csv for detector Public Inverted Coax
[ Info: ...clustering
1411 hits before clustering
  0.810286 seconds (582.07 k allocations: 29.900 MiB, 99.79% compilation time)
244 hits after clustering
[ Info: ...removing hits with no energy deposits
[ Info: ...removing events outside of the detector
[ Info: Simulation method: SSD
┌ Warning: No crystal metadata path given. Simulation with dummy constant impurity density.
└ @ LegendGeSim ~/work/LegendGeSim.jl/LegendGeSim.jl/src/pss.jl:74
[ Info: ---------------------- stp -> pss (ideal pulses)
[ Info: _||_||_||_ Simulate detector
[ Info: Simulating with SSD from scratch for given settings
[ Info: DL = 0mm
┌ Warning: You did not provide operating voltage in environment settings -> taking recommended voltage from metadata
└ @ LegendGeSim ~/work/LegendGeSim.jl/LegendGeSim.jl/src/legend_detector_to_ssd.jl:45
[ Info: Simulating at 3800.0V
...electric potential
...electric field
...weighting potential 1
...weighting potential 2
No fano noise (no noise model given)
[ Info: ~.~.~.~.~ Simulate charge pulses
[ Info: ~.~.~ SolidStateDetectors
[ Info: Detector has 2 contacts
[ Info: Table has 5 physics events (11 single charge depositions).

Progress:  40%|████████████████▍                        |  ETA: 0:00:07
Progress: 100%|█████████████████████████████████████████| Time: 0:00:04
[ Info: Generating waveforms...

plot(pss_table.waveform, legend=false, linewidth=1.5)

The "negative" waveforms are the ones coming from the n+ contact, so they are symmetric. In fact, the length of our table is twice the amount of waveforms we simulated.

length(pss_table)

10

plot(pss_table.waveform[1:5], legend=false, title="only p+ contact pulses")

Save output to hdf5 file

You can save simulated pulses in a file that can be used later.

using LegendHDF5IO

pss_name = "cache/test_100wfs_pss.hdf5"
lh5open(pss_name, "w") do f
    LegendHDF5IO.writedata(f.data_store, "pss/pss", pss_table[1:5])
    LegendHDF5IO.writedata(f.data_store, "pss/truth", pss_truth[1:5])
end

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