1 Overview

gamma is intended to process in-situ gamma-ray spectrometry measurements for luminescence dating. This package allows to import, inspect and (automatically) correct the energy scale of the spectrum. It provides methods for estimating the gamma dose rate by the use of a calibration curve. This package only supports Canberra CNF and TKA files.

Typical workflow:

1. Import spectrum data with read(),
2. Inspect spectrum with plot(),
3. Calibrate the energy scale with energy_calibrate(),
4. Estimate the gamma dose rate with dose_predict().

The estimation of the gamma dose rate requires a calibration curve. This package contains several built-in curves, but as these are instrument-specific you may need to build your own (see help(dose_fit)). These built-in curves are in use in several labs and can be used to reproduce published results. Check out the dedicated vignette for an overview of the fitting process: vignette("doserate")

Note that gamma uses the International System of Units: energy values are assumed to be in keV and dose rates in µGy/y.

2 Import file

Both Canberra CNF and TKA files can be imported.

# Automatically skip the first channels
# Import a CNF file
cnf_test <- system.file("extdata/LaBr.CNF", package = "gamma")
(spc_cnf <- read(cnf_test))
#> Gamma spectrum:
#> *  name: LaBr
#> *  date: 2019-02-07 11:48:18
#> *  live_time: 3385.54
#> *  real_time: 3403.67
#> *  channels: 1024
#> *  energy_min: -7
#> *  energy_max: 3124.91

# Import a TKA file
tka_test <- system.file("extdata/LaBr.TKA", package = "gamma")
(spc_tka <- read(tka_test))
#> Gamma spectrum:
#> *  name: LaBr
#> *  date: 2024-09-23 21:26:10.596201
#> *  live_time: 3385.54
#> *  real_time: 3403.67
#> *  channels: 1024
#> *  energy_min: NA
#> *  energy_max: NA

# Import all files in a given directory
files_test <- system.file("extdata", package = "gamma")
(spc <- read(files_test))
#> A collection of 4 gamma spectra: BEGe, Kromek_CZT, LaBr, LaBr

3 Inspect spectrum

# Plot CNF spectrum
plot(spc_cnf) +
  ggplot2::theme_bw()

4 Calibrate the energy scale

The energy calibration of a spectrum is the most tricky part. To do this, you must specify the position of at least three observed peaks and the corresponding energy value (in keV).

The package allows you to provide the channel-energy pairs you want to use. However, the spectrum can be noisy so it is difficult to properly determine the peak channel. In this case, a better approach may be to pre-process the spectrum (variance-stabilization, smoothing and baseline correction) and perform a peak detection. Once the identified peaks are satisfactory, you can set the corresponding energy values (in keV) and use these lines to calibrate the energy scale of the spectrum.

Regardless of the approach you choose, it is strongly recommended to check the result before proceeding.

# Use a square root transformation
sliced <- signal_slice(spc_tka)

# Use a square root transformation
transformed <- signal_stabilize(sliced, f = sqrt)

# Use a 21 point Savitzky-Golay-Filter to smooth the spectrum
smoothed <- signal_smooth(transformed, method = "savitzky", m = 21)

# Estimate the baseline of a single file
baseline <- signal_baseline(smoothed, method = "SNIP", decreasing = TRUE)

# Plot spectrum + baseline
plot(smoothed, baseline) +
  ggplot2::labs(title = "Spectrum + baseline") +
  ggplot2::theme_bw()

# Substract the estimated baseline
corrected <- smoothed - baseline
# Or, remove the baseline in one go
# corrected <- removeBaseline(smoothed)

# Plot the corrected spectrum
plot(corrected) +
  ggplot2::labs(title = "Baseline-corrected spectrum") +
  ggplot2::theme_bw()

# You can remove the baseline of multiple spectra in one go
# Note that the same parameters will be used for all spectra
clean <- signal_correct(spc)

# Detect peaks in a single file
peaks <- peaks_find(corrected)

# Plot spectrum + peaks
plot(corrected, peaks) +
  ggplot2::labs(title = "Peaks") +
  ggplot2::theme_bw()

# Set the energy values (in keV)
set_energy(peaks) <- c(238, NA, NA, NA, 1461, NA, NA, 2615)
peaks
#> 3 peaks were detected.

# Calibrate the spectrum using the peak positions
scaled <- energy_calibrate(spc_tka, peaks)

# Plot the spectrum
plot(scaled, xaxis = "energy") +
  ggplot2::labs(title = "Calibrated spectrum") +
  ggplot2::theme_bw()

# Create a list of channel-enegy pairs
calib_lines <- list(
  channel = c(84, 492, 865),
  energy = c(238, 1461, 2615)
)

# Calibrate the spectrum using these fixed lines
scaled2 <- energy_calibrate(spc_tka, lines = calib_lines)

# Spectrum pre-processing and peak detection
pks <- spc_tka |>
  signal_slice() |>
  signal_stabilize(f = sqrt) |>
  signal_smooth(method = "savitzky", m = 21) |>
  signal_correct(method = "SNIP", decreasing = TRUE, n = 100) |>
  peaks_find()

# Set the energy values (in keV)
set_energy(pks) <- c(238, NA, NA, NA, 1461, NA, NA, 2615)

# Calibrate the energy scale
cal <- energy_calibrate(spc_tka, pks)

# Plot spectrum
plot(cal, pks) +
  ggplot2::theme_bw()

5 Estimate the gamma dose rate

To estimate the gamma dose rate, you can either use one of the calibration curves distributed with this package or build your own.

# Load one of the built-in curves
data(BDX_LaBr_1) # IRAMAT-CRP2A (Bordeaux)
data(AIX_NaI_1) # CEREGE (Aix-en-Provence)

The construction of a calibration curve requires a set of reference spectra for which the gamma dose rate is known and a background noise measurement. First, each reference spectrum is integrated over a given interval, then normalized to active time and corrected for background noise. The dose rate is finally modelled by the integrated signal value used as a linear predictor.

See vignette(doserate) for a reproducible example.