ThreeDimensional Optimal Spectral Extraction (TDOSE) from integral field spectroscopy
Abstract
The amount of integral field spectrograph (IFS) data has grown considerably over the last few decades. The demand for tools to analyze such data is therefore bigger now than ever. We present a flexible Python tool for ThreeDimensional Optimal Spectral Extraction (TDOSE) from IFS data cubes. TDOSE works on any threedimensional data cube and bases the spectral extractions on morphological reference image models. By default, these models are generated and composed of multiple multivariate Gaussian components, but can also be constructed with independent modeling tools and be provided as input to TDOSE. 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 threedimensional source models. This allows the efficient deblending of flux in both the spatial and spectral dimensions of the IFS data cubes, and extraction of the corresponding onedimensional spectra. TDOSE implicitly requires an assumption about the twodimensional light distribution. We describe how the flexibility of TDOSE can be used to mitigate and correct for deviations from the input distribution. Furthermore, we present an example of how the threedimensional source models generated by TDOSE can be used to improve twodimensional maps of physical parameters like velocity, metallicity, or star formation rate when flux contamination is a problem. By extracting TDOSE spectra of ∼150 [OII] emitters from the MUSEWide survey we show that the median increase in line flux is ∼5% when using multicomponent models as opposed to singlecomponent models. However, the increase in recovered line emission in individual cases can be as much as 50%. Comparing the TDOSE modelbased extractions of the MUSEWide [OII] emitters with aperture spectra, the TDOSE spectra provides a median flux (S/N) increase of 9% (14%). Hence, TDOSE spectra optimize the S/N while still being able to recover the total emitted flux.
TDOSE version 3.0 presented in this paper is publicly available at https://github.com/kasperschmidt/TDOSE and <xref reftype="bibr" rid="R72">http://Schmidt (2019)</xref>http://.
 Publication:

Astronomy and Astrophysics
 Pub Date:
 August 2019
 DOI:
 10.1051/00046361/201935857
 arXiv:
 arXiv:1906.05891
 Bibcode:
 2019A&A...628A..91S
 Keywords:

 methods: data analysis;
 methods: observational;
 techniques: imaging spectroscopy;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 Accepted by A&