Skip to contents

Run Monte-Carlo Simulation for CW-IRSL (tunnelling transitions)

Source: R/run_MC_CW_IRSL_TUN.R
run_MC_CW_IRSL_TUN.Rd

Runs a Monte-Carlo (MC) simulation of continuous wave infrared stimulated luminescence (CW-IRSL) using the model for tunnelling transitions. Tunnelling refers to quantum mechanical tunnelling processes from the excited state of the trap, into a recombination centre.

Usage

run_MC_CW_IRSL_TUN(
  A,
  rho,
  times,
  clusters = 10,
  r_c = 0,
  delta.r = 0.1,
  N_e = 200,
  method = "seq",
  output = "signal",
  ...
)

Arguments

A

numeric (required): The effective optical excitation rate for the tunnelling process (s^-1).

rho

numeric (required): The density of recombination centres (defined as \(\rho\)' in Huntley 2006) (dimensionless).

times

numeric (required): The sequence of time steps within the simulation (s).

clusters

numeric (with default): The number of created clusters for the MC runs. The input can be the output of create_ClusterSystem. In that case n_filled indicate absolute numbers of a system.

r_c

numeric (with default): Critical distance (>0) that must be provided if the sample has been thermally and/or optically pretreated. This parameter expresses the fact that electron-hole pairs within a critical radius r_c have already recombined.

delta.r

numeric (with default): Increments of the dimensionless distance parameter r'

N_e

numeric (width default): The total number of electron traps available (dimensionless). Can be a vector of length(clusters), shorter values are recycled.

method

character (with default): Sequential 'seq' or parallel 'par'processing. In the parallel mode the function tries to run the simulation on multiple CPU cores (if available) with a positive effect on the computation time.

output

character (with default): output is either the 'signal' (the default) or 'remaining_e' (the remaining charges/electrons in the trap)

...

further arguments, such as cores to control the number of used CPU cores or verbose to silence the terminal

Value

This function returns an object of class RLumCarlo_Model_Output which is a list consisting of an array with dimension length(times) x length(r) x clusters and a numeric time vector.

Details

The model

$$ I_{TUN}(r',t) = -dn/dt = A * exp(-(\rho')^{-1/3} * r')* n (r',t) $$

Where in the function:
A := effective optical excitation rate for the tunnelling process (s^-1)
r' := the dimensionless tunnelling radius
\(\rho\)' := rho' the dimensionless density of recombination centres (see Huntley (2006))
t := time (s)
n := the instantaneous number of electrons corresponding to the radius r' at time t

Function version

0.2.0

How to cite

Friedrich, J., Kreutzer, S., 2025. run_MC_CW_IRSL_TUN(): Run Monte-Carlo Simulation for CW-IRSL (tunnelling transitions). Function version 0.2.0. In: Friedrich, J., Kreutzer, S., Pagonis, V., Schmidt, C., 2025. RLumCarlo: Monte-Carlo Methods for Simulating Luminescence Phenomena. R package version 0.1.10. https://r-lum.github.io/RLumCarlo/

References

Huntley, D.J., 2006. An explanation of the power-law decay of luminescence. Journal of Physics: Condensed Matter, 18(4), 1359.

Pagonis, V., Friedrich, J., Discher, M., Müller-Kirschbaum, A., Schlosser, V., Kreutzer, S., Chen, R. and Schmidt, C., 2019. Excited state luminescence signals from a random distribution of defects: A new Monte Carlo simulation approach for feldspar. Journal of Luminescence 207, 266–272. doi:10.1016/j.jlumin.2018.11.024

Further reading

Aitken, M.J., 1985. Thermoluminescence dating. Academic Press.

Jain, M., Guralnik, B., Andersen, M.T., 2012. Stimulated luminescence emission from localized recombination in randomly distributed defects. Journal of Physics: Condensed Matter 24, 385402.

Chen, R., McKeever, S.W.S., 1997. Theory of Thermoluminescence and Related Phenomena. WORLD SCIENTIFIC. doi:10.1142/2781

Author

Johannes Friedrich, University of Bayreuth (Germany), Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)

Examples

run_MC_CW_IRSL_TUN(
 A = 0.8,
 rho = 1e-4,
 times = 0:50,
 r_c = 0.05,
 delta.r = 0.1,
 method = "seq",
 clusters = 10,
  output = "signal") %>%
 plot_RLumCarlo(norm = TRUE, legend = TRUE)