Three Dimensional Optimal Spectral Extraction (TDOSE) from Integral
  Field Spectroscopy
  [Abbreviated] The amount of integral field spectrograph (IFS) data has grown
considerable over the last few decades. The demand for tools to analyze such
data is therefore bigger now than ever. We present TDOSE; a flexible Python
tool for Three Dimensional Optimal Spectral Extraction from IFS data cubes.
TDOSE works on any three-dimensional data cube and bases the spectral
extractions on morphological reference image models. In each wavelength layer
of the IFS data cube, TDOSE simultaneously optimizes all sources in the
morphological model to minimize the difference between the scaled model
components and the IFS data. The flux optimization produces individual data
cubes containing the scaled three-dimensional source models. This allows for
efficient de-blending of flux in both the spatial and spectral dimensions of
the IFS data cubes, and extraction of the corresponding one-dimensional
spectra. We present an example of how the three-dimensional source models
generated by TDOSE can be used to improve two-dimensional maps of physical
parameters. By extracting TDOSE spectra of $\sim$150 [OII] emitters from the
MUSE-Wide survey we show that the median increase in line flux is $\sim$5% when
using multi-component models as opposed to single-component models. However,
the increase in recovered line emission in individual cases can be as much as
50%. Comparing the TDOSE model-based extractions of the MUSE-Wide [OII]
emitters with aperture spectra, the TDOSE spectra provides a median flux (S/N)
increase of 9% (14%). Hence, TDOSE spectra optimizes the S/N while still being
able to recover the total emitted flux. TDOSE version 3.0 presented in this
paper is available at https://github.com/kasperschmidt/TDOSE.
