Accurate Recovery of H i Velocity Dispersion from Radio Interferometers

Naively, one would expect that the flux of a channel map is obtained by simply adding the flux of the cleaned map to that of the residual map:

$$\begin{eqnarray}&&F=C+R,\end{eqnarray} \tag{ 4 }$$

where C is the cleaned flux and R is the residual flux. However, C and R have different units (C is in Jy/clean beam and R in Jy/dirty beam) and depending on the configuration of the array, the shape of the dirty beam can be strongly non-Gaussian and more extended than the clean beam. This mismatch in shape between the dirty beam and the clean beam leads to an overestimate of the flux density if the clean beam parameters are used to determine the flux of sources containing both cleaned and residual emission. We illustrate in Figure 1 how the dirty beam and the clean beam differ from each other in typical multi-configuration observations with the VLA. To account for the difference in area between the clean beam and the dirty beam, the flux of the residual must be scaled by a scaling factor $\epsilon$:

$$\begin{eqnarray}&&F(\mathrm{true})=C+\epsilon \,R,\end{eqnarray} \tag{ 5 }$$

where F(true) is the corrected flux and $\epsilon$ is a correction factor that accounts for the ratio between the clean beam area and the dirty beam area (Jorsater & van Moorsel 1995). Theoretically, due to the absence of zero spacing in interferometric data, the dirty beam integral and the total flux density of the dirty image are zero as there are equal amounts of positive and negative flux in the dirty image. In practice, the positive flux is mostly concentrated in the main lobe of the dirty beam and, for the purposes of residual scaling, one has to choose a region (box) centered on the main lobe over which to integrate the dirty beam and ensure most of the flux in the main lobe is captured. The residual scaling method is included in the AIPS⁶ task IMAGR and more technical aspects are presented there. As described in Walter et al. (2008), the calculation of the flux in the main lobe of the THINGS dirty beam was done inside a box with a half-width of 50 pixels (75'') in R.A. and in decl. This box was chosen as it encompasses the larger part of the main lobe of the THINGS dirty beam while still well within its first negative sidelobe. This choice is appropriate for all of the THINGS galaxies as they have all been observed in the same manner and with the same set-up, meaning their dirty beams are all very similar. A quantitative analysis of the uncertainties associated with this choice of box size and with the use of a different dirty beam area has not been performed, and is beyond the scope of this paper. However, initial testing prior to the reduction of the THINGS data as presented in Walter et al. (2008) indicates that residual-scaling results do not critically depend on the box size (F. Walter 2017, private communication). Note that the residual scaling affects the noise and thus it should only be used for flux measurements in areas with genuine emission (Rich et al. 2008; Walter et al. 2008; Ott et al. 2012). For this reason, the THINGS residual-scaled cubes are blanked in areas judged to be devoid of emission. The criterion used by THINGS is that genuine emission must be present in at least three consecutive channels at or above a level of 2σ in standard cubes convolved to 30'' resolution. These cubes are then used as masks to blank areas containing only noise. While residual scaling has been widely used to get more accurate flux values (e.g., Walter et al. 2008), the effects its presence or absence has on H i velocity dispersion have not yet been well explored. Stilp et al. (2013) first noticed the difference between H i velocity dispersion derived from flux-rescaled cubes and standard cubes obtained using multi-configuration VLA observations. This paper makes a thorough comparison of H i velocity dispersions derived from residual-scaled cubes and non-residual-scaled cubes from THINGS.

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The widely used classical CLEAN algorithm iteratively deconvolves images until the peak residual flux reaches an adopted threshold value. Most major H i surveys of nearby galaxies (e.g., THINGS, Little THINGS, VLA-ANGST) have adopted a cleaning level of 2.5 times the rms noise to prevent noise spikes from being cleaned and to avoid divergence of the CLEAN process. The effects of residual emissions due to the extended wings of the dirty beam can be minimized by deeply cleaning the data, but this can be computationally expensive and could introduce additional artefacts. Again, the choice of the cleaning depth is driven by obtaining a reasonable flux estimate but its effects on the measurement of H i velocity dispersion have not been quantified previously. This analysis explores how cleaning depth affects H i velocity dispersion and investigates a number of methods that can lead to a more accurate determination of the velocity dispersion.

The choice of weighting scheme in deconvolution algorithms can also affect the measurement of H i velocity dispersion. This is because the dirty beam, which depends on the configuration of the array and the weighting function used, influences the shape of the clean restoring beam and consequently that of the individual velocity profiles in the final restored image. We analyze the effects of weighting of uv-data during image construction on H i velocity dispersion by comparing velocity dispersion from natural and robust weighted data cubes. Finally, using model data cubes, we investigate which type of data cubes should ideally be used for velocity dispersion analysis, or more broadly, analysis of profile widths and shapes. In this paper we focus on using the THINGS data, but note that the conclusions are generally applicable to any observation where the dirty beam differs substantially from a Gaussian. This also implies that for arrays where the dirty beam is close to Gaussian (e.g., the Westerbork Synthesis Radio Telescope, WSRT), the need for residual scaling will be much reduced or absent.

This analysis uses 22 galaxies from THINGS. These were selected to be minimally affected by interactions, projection effects or major bulk motions. For further discussion, we refer the reader to Ianjamasimanana et al. (2012). Their names and observational properties are shown in Table 1 (as also given in Walter et al. 2008). The data provided by THINGS include both natural and robust weighted data cubes (with a robust parameter value of 0.5). For each weighting, the non-residual scaled versions are publicly available on the THINGS website (http://www.mpia.de/THINGS/Data.html). The residual-scaled ones are available upon request. Our aim is to characterize the values of the velocity dispersions with high precision. So, as in Ianjamasimanana et al. (2012), individual line profiles with peak amplitude higher than 4 times the rms noise level are aligned to the same reference velocity and summed to derive what we call super profiles. We use a velocity field to define the amount of shift per pixel to end up at the same reference velocity. We decompose the high signal-to-noise ratio (S/N) super profiles into narrow and broad Gaussian components and compare the fitted parameters (velocity dispersion, ratio of linestrength or flux ratio between the narrow and broad components) from the different THINGS cubes to assess the effects of deconvolution and residual scaling on H i profile shapes. The narrow component represents high-brightness regions, whereas the broad component contains faint low-level emission where the residual emission contributions can become important. We also describe the results obtained from single Gaussian fits, as generally used in the literature to obtain a more global measure of the dispersion. We remove the effect of missing short spacings, which is inherent to interferometric data and manifests as negative wings in the super profiles, by (simultaneously) including a polynomial background or baseline in the super profile fits. Most of our sample galaxies require a zero or constant background, whereas in a few cases higher-order polynomial fits are needed.

Table 1.  The Sample Galaxies

Galaxy	Weighting	Bmaj	Bmin	Ch. Width
		('')	('')	$(\mathrm{km}\,{{\rm{s}}}^{-1})$
1	2	3	4	5
NGC 628	NA	11.88	9.30	2.6
	RO	6.8	5.57	⋯
NGC 925	NA	5.94	5.71	2.6
	RO	4.85	4.65	⋯
NGC 2366	NA	13.10	11.85	2.6
	RO	6.96	5.94	⋯
NGC 2403	NA	8.75	7.65	5.2
	RO	6.01	5.17	⋯
Ho ii	NA	13.74	12.57	2.6
	RO	6.95	6.05	⋯
M81 dwA	NA	15.87	14.23	1.3
	RO	7.79	6.27	⋯
DDO 53	NA	11.75	9.53	2.6
	RO	6.34	5.67	⋯
NGC 2903	NA	15.27	13.32	5.2
	RO	8.66	6.43	⋯
Ho i	NA	14.66	12.73	2.6
	RO	7.78	6.03	⋯
NGC 2976	NA	7.41	6.42	5.2
	RO	5.25	4.88	⋯
NGC 3184	NA	7.51	6.93	2.6
	RO	5.33	5.11	⋯
NGC 3198	NA	13.01	11.56	5.2
	RO	7.64	5.62	⋯
IC 2574	NA	12.81	11.90	2.6
	RO	5.93	5.48	⋯
NGC 3351	NA	9.94	7.15	5.2
	RO	6.26	5.20	⋯
NGC 3621	NA	15.95	10.24	5.2
	RO	10.50	5.68	⋯
NGC 4214	NA	14.69	13.87	1.3
	RO	7.41	6.35	⋯
NGC 4736	NA	10.22	9.07	5.2
	RO	5.96	5.55	⋯
DDO 154	NA	14.09	12.62	2.6
	RO	7.94	6.27	⋯
NGC 5055	NA	10.06	8.66	5.2
	RO	5.78	5.26	⋯
NGC 5236	NA	15.16	11.44	2.6
	RO	10.40	5.60	⋯
NGC 6946	NA	6.04	5.61	2.6
	RO	4.93	4.51	⋯
NGC 7793	NA	15.60	10.85	2.6
	RO	10.37	5.39	⋯

Note. Column 1: Name of galaxy; Column 2: NA: Natural weighting; RO: Robust weighting; Column 3/4: major and minor axis of synthesized beam in arcsec; Column 5: Channel width.

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Here we compare the super profile parameters of the natural and the robust THINGS data cubes. Figure 2 compares the parameters from the non-residual-scaled version of the cubes. In this case, the natural cubes tend to give higher velocity dispersion values than the robust cubes (∼7% higher for the single Gaussian component, ${\sigma }_{1{\rm{G}}}$, and for the narrow component, ${\sigma }_{{\rm{n}}}$, and ∼13% higher for the broad component, ${\sigma }_{{\rm{b}}}$). The ratio of linestrength, or ratio between the narrow and broad component fluxes, ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$, however, only scatters around the line of equality. Figure 3 presents a comparison of the super profile parameters for the residual-scaled version of the cubes. In this case, the parameters from the natural and the robust cubes are very similar. We conclude that the robust and the natural weighting schemes give similar super profile parameter values in the residual-scaled case, but values differ slightly for the non-residual-scaled cubes.

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In this section, we compare super profile parameters derived from non-residual-scaled cubes with those from residual-scaled cubes. In Figure 4, we compare super profile parameters of the natural non-residual-scaled cubes (NA) with those of the natural residual-scaled cubes (NA-res). The NA cubes give mean single Gaussian and narrow component velocity dispersions that are respectively higher by ∼18% and ∼12% than the NA-res cubes. For the broad component dispersions, the NA cubes give a mean value that is ∼27% higher than that of the NA-res cubes. As a result, the NA cubes give smaller ${\sigma }_{{\rm{n}}}/{\sigma }_{{\rm{b}}}$ values than the NA-res ones. The ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$ values from the NA and NA-res cubes are similar.

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In Figure 5, we compare parameters from robust non-residual-scaled cubes (RO) and robust residual-scaled cubes (RO-res). Here the ${\sigma }_{{\rm{n}}}$ from the RO and the RO-res cubes are similar. The mean ${\sigma }_{1{\rm{G}}}$ and ${\sigma }_{{\rm{b}}}$ values from the RO are, on average, ∼16% higher than those of the RO-res, which are much lower than the difference found for the natural-weighted cubes.

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In conclusion, the broad component is more sensitive to residual-scaling effects than the narrow and the single Gaussian components. Similarly, the effects of residual scaling are more pronounced for the natural-weighted than for the robust-weighted cubes. Natural non-residual-scaled cubes give broad component dispersion values that are, on average, 10% higher than those of robust non-residual-scaled cubes. Super profile parameters from residual-scaled cubes are similar despite the weighting scheme adopted. In general, the broad component dispersion values from non-residual-scaled cubes are higher than those from residual-scaled cubes.

In the previous section, we have shown that residual-scaled cubes tend to give lower velocity dispersion values than non-oresidual-scaled cubes. Note, however, that as already mentioned in Section 3.1, residual-scaled cubes are also blanked (Walter et al. 2008). So how much of the difference is caused by blanking and how much is due to the residual scaling? To investigate this, we blank the non-residual-scaled cubes using the same blanking mask as the residual-scaled cubes. We do this for both the robust and the natural-weighted data cubes, then derive and fit super profiles from these blanked non-residual-scaled cubes. In Figure 6, we compare the velocity dispersions from the blanked non-residual-scaled cubes with those from the non-blanked non-residual-scaled cubes. In Figure 7, we compare the velocity dispersions from the blanked non-residual-scaled cubes with those from the (already blanked) residual-scaled cubes. We conclude from these two figures that blanking tends to affect only the broad component. This can be understood by the fact that the broad component mostly represents faint emission, which manifests as wings in the super profiles. This low-level emission consists mostly of uncleaned residual emission, which leads to an overestimate of the broad component velocity dispersion when not blanked or residual-scaled. Thus, the difference in broad component velocity dispersion between the residual-scaled cubes and the non-residual-scaled cubes presented in the previous section is partly due to the effects of blanking. For the single Gaussian velocity dispersion, which is most commonly used in the literature, blanking the data cubes does not affect the velocity dispersion, whereas residual scaling the cubes does.

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In the previous sections, we analyzed the effects of weighting and residual scaling on the global measurement of the H i velocity dispersion (i.e., from the entire disk of the galaxies) from data cubes cleaned to 2.5 times the rms noise. However, accurate measurement of the H i velocity dispersion as a function of radius is also important (e.g., for the analysis of disk (in)stability and/or the studies of the shape of the dark matter halo, Petric & Rupen 2007). Here we explore the effects of the choice of CLEAN depth on the measurement of H i velocity dispersion as a function of radius.

We use the dirty image of NGC 3184, a face-on spiral galaxy, and clean it down to 3.5, 2.5, and 1.5 times the rms noise using natural and robust weightings. The 2.5 times rms cube is identical to the standard THINGS cube. We also create blanked non-residual-scaled and (blanked) residual-scaled versions of these cubes. We divide the image of NGC 3184 into series of elliptical annuli with 0.1 r₂₅ width (we adopt r₂₅ = 222'', Walter et al. 2008), with identical orientation parameters (inclination, position angle, and center position) as taken from Walter et al. (2008). We stack individual profiles within each of these annuli and fit the resulting super profiles of the (3.5, 2.5, 1.5) times rms cubes with a single Gaussian function to derive the radial velocity dispersion profiles shown in Figure 8. We show the shapes of the super profiles at a radius of 0.5 r₂₅ in Figure 9. We choose NGC 3184 because of its face-on orientation, so  projection and beam smearing effects are minimal. For the natural non-residual-scaled cubes, there is a noticeable difference between the velocity dispersion derived from the (3.5, 2.5, 1.5) times rms cubes. This difference is smaller for the robust non-residual-scaled cubes. For residual-scaled data cubes, cleaning level has little to no effect on the derived velocity dispersion. When cleaned down to 1.5 times rms, non-residual-scaled cubes give identical velocity dispersion values to residual-scaled cubes. Blanking seems to affect only the velocity dispersion of the natural non-residual-scaled cubes cleaned to 3.5 times rms. The effects of cleaning level, residual-scaling and blanking on the shapes of the super profiles can be seen in Figure 9.

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In this section, we quantify the effects of low-level uncleaned emission on the derivation of H i velocity dispersion using second moment calculation and single Gaussian fits of H i spectra. Here, the model cubes created using the above procedure all have Gaussian input spectra with a velocity dispersion of 12 $\mathrm{km}\,{{\rm{s}}}^{-1}$, but different peak flux values.

Figures 10 and 11 show histograms of the single Gaussian dispersion and the mean second moment values derived from the model data cubes. We use the GIPSY task MOMENTS to calculate the second moment values. We do not apply any restrictions, such as clipping of spectral channels, to derive the second moment values. The model cubes do not contain any double profiles or large noise spikes, so the values shown in Figures 10 and 11 are not affected by this. For the non-residual-scaled cubes, the velocity dispersions from both the single Gaussian fits and the second moment maps depend on the input peak flux. The derived mean velocity dispersions tend to increase from higher to lower S/N. For the lowest input peak flux, the mean single Gaussian dispersion and second moment values differ from the input dispersion by ∼18% and ∼34%, respectively. For the residual-scaled model data cubes, the mean single Gaussian velocity dispersions are very similar to the input dispersion. We do, however, observe a steady increase in the mean second moment values as we decrease the input peak flux values. The derived value differs from the input dispersion by ∼14% at the lowest input peak flux. The histograms of the second moment values have a broader distribution compared to those of the single Gaussian dispersions, especially for the non-residual-scaled cubes at low S/N. Thus, second moment values are more sensitive to S/N than single Gaussian dispersions. The effects of S/N are more pronounced for the non-residual-scaled than for the residual-scaled cubes. The results from the models show that for the standard THINGS cubes (i.e., cleaned to 2.5 time the rms noise), residual-scaled cubes give more accurate velocity dispersion as opposed to non-residual-scaled cubes. The latter tend to overestimate the velocity dispersions, especially in the low S/N regime.

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Here, the model data cubes are constructed using narrow and broad Gaussian input spectra, each with different input profile parameter values (${\sigma }_{{\rm{n}}}$, ${\sigma }_{{\rm{b}}}$, ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$, and the ratio of the narrow and broad component amplitudes, ${a}_{{\rm{n}}}/{a}_{{\rm{b}}}$). Table 2 illustrates the results from the various models, where the input parameters are shown in bold. The conclusions can be summarized as follows. The residual and non-residual-scaled model data cubes give similar ${\sigma }_{{\rm{n}}}$ values, approaching the input values for a range of S/N. However, the non-residual-scaled cubes give ${\sigma }_{{\rm{b}}}$ values that are overestimated by up to ∼16%, whereas the residual-scaled ones result in ${\sigma }_{{\rm{b}}}$ values similar to the input values. In addition, the non-residual-scaled cubes tend to underestimate the ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$ ratios, especially for lower input peak flux values (i.e., lower S/N). The derived ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$ values from the residual-scaled cubes, however, are similar to the input values and do not depend on the input peak flux. For the non-residual scaled cubes, when we go from higher to lower input peak flux, the ${\sigma }_{{\rm{n}}}$ and ${\sigma }_{{\rm{b}}}$ values stay basically the same, whereas the broad component amplitude becomes higher and therefore its area also gets larger. This results in a decrease in the ${a}_{{\rm{n}}}/{a}_{{\rm{b}}}$ and ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$ ratios. Thus in the low S/N regime, the ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$ ratio from non-residual-scaled cubes can be underestimated. For example, in our model, where the S/N is ∼3.6, ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$ is underestimated by up to ∼48%. For the residual-scaled cubes, the derived ${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$ ratios and the input values agree within ∼18% for the same S/N. To illustrate these, we show the shapes of the (overall) super profiles, the individual narrow and broad components in Figure 12 for a range of S/N for both the residual and the non-residual-scaled cubes. Figure 12 clearly shows that the amplitudes of the fitted narrow and broad components from the non-residual-scaled cubes are sensitive to the input S/N. However, the shapes of the fitted narrow/broad components from the residual-scaled cubes are not dependent on the input S/N.

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Table 2.  Parameters from Different Set of Model Data Cubes I

Non-residual Scaled					Residual-scaled
${\sigma }_{{\rm{n}}}$	${\sigma }_{{\rm{b}}}$	Peak Flux	${a}_{{\rm{n}}}/{a}_{{\rm{b}}}$	${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$	${\sigma }_{{\rm{n}}}$	${\sigma }_{{\rm{b}}}$	Peak Flux	${a}_{{\rm{n}}}/{a}_{{\rm{b}}}$	${A}_{{\rm{n}}}/{A}_{{\rm{b}}}$
$(\mathrm{km}\,{{\rm{s}}}^{-1})$	$(\mathrm{km}\,{{\rm{s}}}^{-1})$	(mJy)			$(\mathrm{km}\,{{\rm{s}}}^{-1})$	$(\mathrm{km}\,{{\rm{s}}}^{-1})$	(mJy)
1	2	3	4	5	6	7	8	9	10
8.00	20.00	10.00	1.50	0.60	8.00	20.00	10.00	1.50	0.60
8.17 ± 0.06	22.19 ± 0.28	11.45	1.53 ± 0.03	0.56 ± 0.03	7.98 ± 0.04	19.63 ± 0.16	11.12	1.51 ± 0.02	0.61 ± 0.03
8.00	20.00	5.00	1.50	0.60	8.00	20.00	5.00	1.50	0.60
8.32 ± 0.12	23.35 ± 0.57	5.99	1.44 ± 0.04	0.51 ± 0.05	7.97 ± 0.09	19.33 ± 0.30	5.54	1.50 ± 0.05	0.62 ± 0.06
8.00	20.00	2.50	1.50	0.60	8.00	20.00	2.50	1.50	0.60
8.24 ± 0.25	23.48 ± 0.99	3.25	1.21 ± 0.07	0.42 ± 0.08	7.94 ± 0.18	18.92 ± 0.60	2.75	1.48 ± 0.10	0.62 ± 0.12
8.00	20.00	10.00	2.33	0.93	8.00	20.00	10.00	2.33	0.93
8.15 ± 0.05	22.65 ± 0.35	11.45	2.29 ± 0.04	0.82 ± 0.05	7.98 ± 0.04	19.50 ± 0.21	11.12	2.35 ± 0.04	0.96 ± 0.05
8.00	20.00	5.00	2.33	0.93	8.00	20.00	5.00	2.33	0.93
8.23 ± 0.10	23.77 ± 0.73	5.99	2.08 ± 0.06	0.72 ± 0.08	7.98 ± 0.08	19.20 ± 0.41	5.54	2.35 ± 0.09	0.98 ± 0.11
8.00	20.00	2.50	2.33	0.93	8.00	20.00	2.50	2.33	0.93
8.19 ± 0.19	23.39 ± 1.10	3.25	1.69 ± 0.09	0.59 ± 0.11	7.96 ± 0.15	18.80 ± 0.79	2.75	2.34 ± 0.18	0.99 ± 0.22
Parameters from Different Set of Model Data Cubes II
6.00	16.00	10.00	1.49	0.56	6.00	16.00	10.00	1.49	0.56
6.16 ± 0.04	17.89 ± 0.14	11.45	1.56 ± 0.02	0.54 ± 0.02	6.00 ± 0.03	15.76 ± 0.10	11.06	1.56 ± 0.02	0.59 ± 0.02
6.00	16.00	5.00	1.49	0.56	6.00	16.00	5.00	1.49	0.56
6.20 ± 0.09	18.39 ± 0.33	5.99	1.42 ± 0.04	0.48 ± 0.04	6.01 ± 0.06	15.61 ± 0.20	5.51	1.56 ± 0.04	0.60 ± 0.05
6.00	16.00	2.50	1.49	0.56	6.00	16.00	2.50	1.49	0.56
6.15 ± 0.22	18.45 ± 0.64	3.25	1.19 ± 0.07	0.40 ± 0.08	6.04 ± 0.12	15.52 ± 0.38	2.73	1.60 ± 0.08	0.62 ± 0.09
6.00	16.00	1.25	1.49	0.56	6.00	16.00	1.25	1.49	0.56
5.97 ± 0.47	17.67 ± 1.01	1.84	0.92 ± 0.11	0.31 ± 0.12	6.13 ± 0.23	15.66 ± 0.77	1.34	1.71 ± 0.17	0.67 ± 0.20
6.00	16.00	10.00	2.32	0.87	6.00	16.00	10.00	2.32	0.87
6.12 ± 0.04	18.15 ± 0.21	11.45	2.33 ± 0.04	0.78 ± 0.04	6.00 ± 0.03	15.70 ± 0.13	11.06	2.43 ± 0.04	0.93 ± 0.04
6.00	16.00	5.00	2.32	0.87	6.00	16.00	5.00	2.32	0.87
6.16 ± 0.08	18.62 ± 0.42	5.99	2.06 ± 0.06	0.68 ± 0.07	6.02 ± 0.05	15.59 ± 0.26	5.51	2.47 ± 0.08	0.95 ± 0.09
6.00	16.00	2.50	2.32	0.87	6.00	16.00	2.50	2.32	0.87
6.13 ± 0.19	18.29 ± 0.79	3.25	1.68 ± 0.11	0.56 ± 0.12	6.06 ± 0.10	15.54 ± 0.50	2.73	2.56 ± 0.15	1.00 ± 0.17
6.00	16.00	1.25	2.32	0.87	6.00	16.00	1.25	2.32	0.87
6.01 ± 0.43	17.08 ± 1.23	1.84	1.28 ± 0.17	0.45 ± 0.19	6.10 ± 0.19	15.67 ± 1.00	1.34	2.73 ± 0.32	1.06 ± 0.36
6.00	16.00	10.00	0.80	0.30	6.00	16.00	10.00	0.80	0.30
6.25 ± 0.07	17.59 ± 0.13	11.45	0.88 ± 0.01	0.31 ± 0.02	5.99 ± 0.04	15.80 ± 0.08	11.06	0.82 ± 0.01	0.31 ± 0.01
6.00	16.00	5.00	0.80	0.30	6.00	16.00	5.00	0.80	0.30
6.33 ± 0.12	18.24 ± 0.24	5.99	0.83 ± 0.02	0.29 ± 0.03	5.99 ± 0.09	15.67 ± 0.14	5.51	0.82 ± 0.02	0.31 ± 0.02
6.00	16.00	2.50	0.80	0.30	6.00	16.00	2.50	0.80	0.30
6.21 ± 0.26	18.55 ± 0.46	3.25	0.72 ± 0.04	0.24 ± 0.05	6.02 ± 0.17	15.52 ± 0.27	2.73	0.82 ± 0.04	0.32 ± 0.05
6.00	16.00	1.25	0.80	0.30	6.00	16.00	1.25	0.80	0.30
5.91 ± 0.57	18.14 ± 0.76	1.84	0.55 ± 0.06	0.18 ± 0.07	6.13 ± 0.32	15.52 ± 0.54	1.34	0.86 ± 0.08	0.34 ± 0.10

Note. The input parameters for each model are represented in bold, whereas the derived parameters from the resulting cubes are shown in regular font. Columns 1 and 6: velocity dispersion of the narrow component; Columns 2 and 7: velocity dispersion of the broad component; Columns 3 and 8: input peak flux values; Columns 4 and 9: ratio between the input amplitude of the narrow and the broad components. Columns 5 and 10: flux ratio of the narrow component and the broad component.

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