Manual: Electron Sampling and Trajectory Simulation

The ElectronSamplers module provides means of generating initial electron samples using different initial condition methods. The ElectronSampler is an abstract supertype, with ADKSampler, SPANESampler, SPASampler and WFATSampler being its subtypes. When the user starts a trajectory simulation job by invoking eTraj.perform_traj_simulation, the method would further call the init_sampler method, which would assign the corresponding type of ElectronSampler in the background. These invocations execute in the background thus the ElectronSamplers module is kept internal (i.e., not exported for public invocation).

The eTraj.perform_traj_simulation method serves as a public entrance to performing a trajectory simulation. The method would automatically detect number of available threads (specified by passing command-line arguments -t <thread_num> when starting julia) and run the trajectory simulation in parallel.

Here we brief on the working procedure of the method eTraj.perform_traj_simulation:

• First, the eTraj.perform_traj_simulation method initializes an eTraj.TrajectorySimulationJob, which stores the essential parameters. The electron sampler is assigned according to init_cond_method.
• Then, it repeatedly invokes the eTraj.launch_and_collect! method, where in each invocation a batch of electrons which tunnel at the same time $\tr$ but have different transversal momenta $\kkt$ is launched and collected using the corresponding simulation scheme.
  • The sampling behavior is controlled by the sample_monte_carlo parameter, if sample_monte_carlo is true, the $\tr$ of the electron batches and the $\kkt$ in each batch would be randomly sampled inside the given intervals specified by sample_t_intv, mc_kd_max and mc_kz_max,
  otherwise, the initial conditions of the electrons would be sampled in an equidistant manner, with the intervals controlled by sample_t_intv, ss_kd_max and ss_kz_max.
• After generating a batch of initial electrons, the eTraj.launch_and_collect! method simulates the electrons' classical trajectories (together with the phase), and then collects the electrons' final momenta on the grid. Trajectory simulations are carried out using the OrdinaryDiffEq.jl package in the DifferentialEquations.jl ecosystem.
  • The grid's size and spacing depends on the final_p_max and final_p_num parameters.
• Finally, the eTraj.perform_traj_simulation method generates the output file which contains the photoelectron momentum distribution (PMD) and other necessary information in a Julia Data Format (JLD2) file or an HDF5 file.

The output file is a Julia JLD2 file, which is compatible with the HDF5 data format, and can be opened by the JLD2.jl package:

julia> using JLD2

julia> file = jldopen("ADK-CTMC_4e14_800nm_cos4_2cyc_CP.jld2")
JLDFile .../ADK-CTMC_4e14_800nm_cos4_2cyc_CP.jld2 (read-only)
    ├─ info
    ├─ params_text              # parameters stored in YAML format
    ├─ params                   # parameters stored in a Julia `Dict`
    ├─ px                       # coordinates of the `momentum_spec` on x-axis
    ├─ py                       # coordinates of the `momentum_spec` on y-axis
    ├─ momentum_spec            # PMD data stored in a Julia `Array`
    ├─ ion_prob                 # total ionization probability
    ├─ ion_prob_uncollected     # ionization probability of discarded electrons
    └─ num_effective_traj       # total number of effective trajectories

julia> file["params_text"] |> print  # equivalent to print(file["params_text"])
init_cond_method: ADK
laser:
    type: Cos4Laser
    peak_int: 4.0e14
    wave_len: 800.0
    ⋮
target:
    type: HydrogenLikeAtom
    Ip: 0.5
    nucl_charge: 1
    ⋮
sample_t_intv: (-100, 100)
sample_t_num: 20000
sample_monte_carlo: false
⋮

The detailed documentation of the eTraj.perform_traj_simulation is shown below: