A spatially resolved study of ionized regions in galaxies at different ...

3.4. Results 67
emission line, using a low order polynomial function to describe the continuum emission.
Instead of fitting the entire wavelength range in a row, we used shorter wavelength ranges
for each spectrum that sampled one or a few of the analyzed emission lines. For example,
an interval of 4800 to 5100 Å was used for measuring Hβ and [Oiii] λλ 4959,5007 Å. This
ensures a characterization of the continuum with the most simple polynomial function, and
a way to simplify the fitting procedure. The software also allows definition of emission line
systems. This linking method is useful to fit lines that share some properties (e.g., lines
that are kinematically coupled with the same width) or include lines whose line ratio is
known (e.g., the line ratio between [Oiii] λ 4959 Å and [Oiii] λ 5007 Å). This was essential for
accurate deblending of the lines, when necessary. Following Gonzalez-Delgado et al. (1994),
Castellanos et al. (2002) and Pérez-Montero and Díaz (2003), the statistical errors associated
with the observed emission fluxes have been calculated using the expression:
σ l = σ c
√N
(
1 + EW )
N∆
where σ l is the error in the observed line flux, σ c represents the standard deviation in a box
near the measured emission line and stands for the error in the continuum placement, N is
the number of pixels used in the measurement of the line flux, EW is the line equivalent
width of the line, and ∆ is the wavelength dispersion in Å per pixel. This expression takes
into account the error in the continuum and the photon count statistics of the emission line.
The procedure of fitting all desired emission lines has the advantage of creating flux
maps of any line with the continuum subtracted. This is not the case of narrow-band imaging,
where a careful cleaning method has to be applied to obtain free-continuum images.
Moreover, sometimes these narrow-band images include more than one line (e.g., Hα, the
[Nii] λλ 6548,6584 Å doublet or the [Sii] λλ 6717,6731 Å doublet), reducing its usability to
study the basic parameters of the ionized gas. This another advantage of using IFU data.
In order to handle the data obtained from FIT3D and analyze them, we have built two
python modules called pyfit3D and pyabund. The combination of both modules allows to
create maps from emission line measurements and clipping the data according to a certain
value. This is useful if there are data that needs masking, as in the case when errors in
the flux measurement or in the ratios from a combination of several lines are too high. The
first module also contains a general routine for automatic measurement of flux emission
lines for fits files of any dimension, in particular for RSS files. It is based on the ngaussfit
task from IRAF. We have measured the Hα flux in the blue RSS with the pyfit3D task.
The comparison of these results with the ones obtained with FIT3D gives the same flux
measurement for each spaxel, within the errors. This is not suprising since both codes uses
a similar algorithm for fitting lines. The module pyabund also contains a routine which
performs the abundance analysis and estimates the physical properties from a table which
contains the flux line measurements and their corresponding errors.