rvmethod (version 0.1.2)

findabsorptionfeatures: Find Absorption Features in a Spectrum

Description

This function applies the Absorption Feature Finder algorithm (Algorithm 1 in Holzer et. al 2020) to find absorption features in a high signal-to-noise, normalized, spectrum. For a spectrum that covers more than 100 Angstroms, it is recommended to parallelize it by setting the cores argument to be greater than 1.

Usage

findabsorptionfeatures(
  wvl,
  flux,
  pix_range = 7,
  gamma = 0.01,
  alpha = 0.05,
  minlinedepth = 0,
  cores = 1
)

Arguments

wvl

vector of wavelengths in the spectrum

flux

vector of normalized flux in the spectrum (must have the same length as wvl)

pix_range

integer that specifies the window size in units of pixels to use in the moving linear regression

gamma

significance level used in finding local minima

alpha

significance level used in estimating wavelength bounds of features (Note: this must be larger than gamma)

minlinedepth

minimum depth required for found absorption features to be returned

cores

number of cores to parallelize over (if set to 1, no parallelizing is done)

Value

a list with the following components:

wvbounds

a list of length 2 vectors that each give the lower and upper bounds of found absorption features

min_wvl

a vector of the wavelengths at which the minimum flux is achieved for each found absorption feature

min_flx

a vector of the minimum flux for each found absorption feature

max_flx

a vector of the maximum flux for each found absorption feature

Examples

# NOT RUN {
data(template)
ftrs = findabsorptionfeatures(template$Wavelength,
                              template$Flux,
                              pix_range = 8, gamma = 0.05,
                              alpha = 0.07, minlinedepth = 0.015)
plot(template$Wavelength, template$Flux,
     type='l', xlab = "Wavelength", ylab = "Flux")
for(i in 1:length(ftrs$wvbounds)){
  lines(ftrs$wvbounds[[i]],
        c(1,1) - 0.01*rep(i%%2,2), col=3)
}
# }