hgrv: Apply the Hermite-Gaussian Radial Velocity (HGRV) Estimation...
In rvmethod: Radial Velocity Method for Detecting Exoplanets

Description Usage Arguments Value Examples

This function applies the HGRV method as given in Holzer et al. (2020) to a given observed spectrum, using the estimated template from the estimate_template function and the parameters component of the output from the Gaussfit function. The result is an estimate of the relative radial velocity present in the observed spectrum in units of m/s.

1	hgrv(obs_wvl, obs_flx, tmp_wvl, tmp_flx, Features, obs_err = NULL, cntm = NULL)

`obs_wvl`	the vector of wavelengths of the observed spectrum
`obs_flx`	the vector of normalized flux of the observed spectrum
`tmp_wvl`	the vector of wavelengths of the template spectrum
`tmp_flx`	the vector of normalized flux of the template spectrum
`Features`	a dataframe with the wavelength bounds and fitted Gaussian parameters for each absorption feature. The `parameters` component of the output from the `Gaussfit` function provides this.
`obs_err`	the vector of uncertainties in the normalized flux of the observed spectrum (must be the same length as `obs_wvl` and `obs_flx`)
`cntm`	the vector of continuum values used to normalize the flux of the observed spectrum (must be the same length as `obs_wvl` and `obs_flx`)

a list with the following components

`rv`	the estimated radial velocity in units of m/s
`rv_err`	the standard error of the estimated radial velocity in units of m/s
`n`	the number of data points used in the weighted linear regression
`data`	a list with the observed wavelengths (`wvl`), the difference flux (`diff_flux`), the explanatory variable constructed as a sum of first-degree generalized Hermite-Gaussian functions (`hgvar`), and the weights (`weights`) used in the regression.

data(template)
ftrs = findabsorptionfeatures(template$Wavelength,
                              template$Flux,
                              pix_range = 8, gamma = 0.05,
                              alpha = 0.07, minlinedepth = 0.015)
gapp = Gaussfit(template$Wavelength, template$Flux, ftrs)
data(observed_spec)
hgrv_output = hgrv(observed_spec$Wavelength, observed_spec$Flux,
                   template$Wavelength, template$Flux, gapp$parameters,
                   obs_err = observed_spec$Uncertainty)
plot(hgrv_output$data$hgvar, hgrv_output$data$diff_flux)
abline(a=0, b=hgrv_output$rv)
abline(a=0, b=hgrv_output$rv - 3*hgrv_output$rv_err, lty=2)
abline(a=0, b=hgrv_output$rv + 3*hgrv_output$rv_err, lty=2)