11institutetext: Departamento de Física de la Tierra y Astrofísica, Fac. CC.
Físicas, Universidad Complutense de Madrid, Plaza de las Ciencias, 1, Madrid,
E-28040, Spain
11email<EMAIL_ADDRESS>
Instituto de Física de Partículas y del Cosmos, IPARCOS-UCM, Fac. CC. Físicas,
Universidad Complutense de Madrid, Plaza de las Ciencias 1, Madrid, E-28040,
Spain
Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro
No.1, C.P. 72840, Tonantzintla, Puebla, Mexico
Instituto de Astrofísica de Andalucía-CSIC, Glorieta de la Astronomía s/n,
18008, Granada, Spain
FRACTAL S.L.N.E.. C/ Tulipán 2, p13, 1A. E-28231, Las Rozas de Madrid, Spain
Departamento de Física Teórica y del Cosmos, Universidad de Granada, Campus de
Fuentenueva, 18071, Granada, Spain
Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada,
18071, Granada, Spain

# MEGADES: MEGARA Galaxy Discs Evolution Survey

Data Release I: central fields

M. Chamorro-Cazorla 1122 A. Gil de Paz 1122 Á. Castillo-Morales 1122 J.
Gallego 1122 E. Carrasco 33 J. Iglesias-Páramo 44 M. L. García-Vargas 55 S.
Pascual 1122 N. Cardiel 1122 C. Catalán-Torrecilla 1122 J. Zamorano 1122 P.
Sánchez-Blázquez 1122 A. Pérez-Calpena 55 P. Gómez-Álvarez 55 J. Jiménez-
Vicente 667711223344556677

The main interest of the Science Team for the exploitation of the MEGARA
instrument at the 10.4m Gran Telescopio Canarias (GTC hereafter) is devoted to
the study of nearby galaxies, with focus on the research of the history of
star formation, and chemical and kinematical properties of disc systems. We
refer to this project as MEGADES: MEGARA Galaxy Discs Evolution Survey. The
initial goal of MEGADES is to provide a detailed study of the inner regions of
nearby disc galaxies, both in terms of their spectrophotometric and chemical
evolution, and their dynamical characterisation, by disentangling the
contribution of in-situ and ex-situ processes to the history of star formation
and effective chemical enrichment of these regions. In addition, the dynamical
analysis of these inner regions naturally includes the identification and
characterization of galactic winds potentially present in these regions. At a
later stage, we will extend this study further out in galactocentric distance.
The first stage of this project encompasses the analysis of the central
regions of a total of 43 nearby galaxies observed with the MEGARA Integral
Field Unit for $\sim$ 114 hours, including both Guaranteed Time and Open Time
observations. In this paper we provide a set of all the processed data
products available to the community and early results from the analysis of
these data regarding stellar continuum, ionized and neutral gas features.

###### Key Words.:

Galaxies: ISM – Galaxies: bulges – Galaxies: evolution – Galaxies: stellar
content – Techniques: imaging spectroscopy

##  1 Introduction

Whereas the fundamental principles of stellar evolution have been reasonably
well understood since the last century, the fundamental aspects of galaxy
evolution continue to be the subject of fierce discussion within the
astronomical community. This is due to the fact that the distance between
galaxies, relative to their own size, is considerably smaller than in the case
of stars, which means that the evolution of the vast majority of galaxies has
evolved under the influence of other galaxies, rather than evolving in
isolation, especially in early epochs when the universe was much denser than
it is in the present. Also, some processes that take place in galaxies and are
considered to be internal secular processes, might be periodic and can be
triggered by the interaction of the galaxies with their environment, such as
nuclear activity or intense star formation. However, even if such mechanisms
are classified as secular (considering the definition of secularity given by
Kormendy & Kennicutt 2004) they leave an evident and clearly distinct imprint
on the later evolution of these objects which helps to distinguish the
evolution of the different galaxies. Some of the mechanisms capable of
affecting the kinematic, chemical and photometric properties of galaxies are:
major and minor mergers (Toomre & Toomre, 1972), bars, rings, density waves
(Lin & Shu, 1964), AGN feedback (Croton et al. 2006, Fabian 2012), stellar
diffusion, gas in-fall (Sancisi et al., 2008), and, in the case of galaxies in
clusters, also ram pressure striping (Gunn & Gott, 1972) and galaxy harassment
(Moore et al., 1996). At the same time, Galactic Winds (GWs) constitute an
important mechanism for redistributing dust and metals both in galaxies and
towards the InterGalactic Medium (IGM) and have been invoked to reproduce the
scale relationships observed in galaxies, as well as to understand the
apparent discrepancies between the theoretical and observed luminosity
functions and to understand the evolution of galaxies (especially of high-
redshift objects) through the green valley. Given that the contribution of all
these mechanisms is rather difficult, determining the evolution of galaxies is
in many aspects still a puzzle.

In recent years our knowledge on these questions has progressed significantly,
mostly thanks to studies carried out with Integral Field Spectroscopy (IFS)
instruments. These instruments are particularly well suited to understand the
role played by each of those mechanisms in the evolution of galaxies, as they
provide two-dimensional spectroscopic information. This technique has already
demonstrated its potential when studying nearby galaxies (SAURON, de Zeeuw et
al. 2002; ATLAS3D, Cappellari et al. 2011; CALIFA, Sánchez et al. 2012; MaNGA,
Bundy et al. 2015; VENGA, Blanc et al. 2013; SIGNALS, Rousseau-Nepton et al.
2019). These surveys have supposed a great advance, especially in studies of
galaxies at low redshift, since up to that moment the existing techniques did
not allow to perform such diverse analyses.

At higher redshifts, we also count with surveys based on IFS observations such
as SINS (Förster Schreiber et al., 2009), MASSIVE (Contini et al., 2012),
KMOS3D (Wisnioski et al., 2015) and MAGIC (Mercier et al., 2022). The main
difference between these studies and those based on observations made on
nearby galaxies is that at high redshift these surveys basically focus on the
analysis of the brightest emission lines, leaving aside the study of the
stellar continuum since the sensitivity of the instruments prevents their
correct observation. Studies based on multiband photometric observations that
generate Spectral Energy Distributions (SED), such as J-PAS, (Benitez et al.,
2014) and SHARDS (Pérez-González et al., 2013), represent the best alternative
to these high-z surveys to study the stellar continuum. SED based surveys have
all the spatial information and are very powerful at high redshift to
determine a large number of parameters (i.e. mean ages, redshifts, stellar
masses) but they lack the spectral resolution needed to measure individual
lines or different kinematic components. However, we are now on the verge of
an unprecedented revolution in the study of distant galaxies thanks to the
start of the JWST (Gardner et al., 2006) observations, both in photometry with
NIRcam (Rieke et al., 2005) and in 2D spectroscopy with NIRSpec (Bagnasco et
al., 2007).

These surveys have also taught us that the amount of information that their
data provide and the studies that can be carried out with them must rely on
multi-wavelength information from multiple facilities, both ground-based and
from space. This is where the necessity to publicly share the observations
with the rest of the community appears, and with it the legacy surveys, in
order to be able to exploit them in the best possible way.

In this paper we present the First Data Release within MEGADES (MEGARA Galaxy
Discs Evolution Survey), the legacy survey of the MEGARA (Multi-Espectrógrafo
en GTC de Alta Resolución para Astronomía) instrument (Gil de Paz et al. 2018;
Carrasco et al. 2018). MEGARA is a high-resolution optical spectrograph
installed on the 10.4 m GTC telescope with two observing modes, the Large
Compact Bundle (LCB) mode, an Integral Field Unit (IFU) covering a field of
view of 12.5 $\times$ 11.3 arcsec2 with a spatial resolution of 0.62 arcsec,
and the Multi-Object Spectroscopy (MOS) mode, that allows the acquisition of
92 objects spectra simultaneously in an area of 3.5 $\times$ 3.5 arcmin2
around the IFU. The observations can cover a spectral interval from 3,650 to
9,700 Å in different ranges with three possible resolutions R=$\lambda$/FWHM
$\sim$ 6,000, 12,000 and 20,000.

Thanks to the unique characteristics of MEGARA and the large collecting area
of GTC, the MEGADES survey means another step forward on our way to
discovering the mechanisms that shape the evolution of galaxies given its
unprecedented combination of depth, spatial and, especially, spectral
resolutions. MEGADES aims to improve our knowledge of the secular processes
that galaxies undergo by studying the kinematic properties of stars and gas in
all the phases in which the later is present, determining the characteristics
of stellar populations, analysing emission line ratio diagnostic diagrams and
discovering the nature of the bulges of the galaxies in the sample. In this
first paper we present studies of the stellar and gas kinematics and spectral
lines flux measurements in the central regions of MEGADES galaxies. Together
with this paper we release the processed observations and the analysis
products obtained.

In $\S$2 we describe the scientific goals of the survey together with the
sample of observed galaxies. $\S$3 describes the observations in detail. In
$\S$4 we explain the process of data reduction and subsequent data
refinements. We detail both the analysis of the stellar kinematics and the
spectral line fitting performed on the data in $\S$5. In $\S$6 we detail all
the data and products that have been released together with this paper.
Finally, we summarize all the contents of the paper in $\S$8 .

Throughout this paper, we assume a standard $\Lambda$CDM universe whose
cosmological parameters are H0 = 70 km s-1 Mpc-1, $\Omega_{\Lambda}$ = 0.7 and
$\mathrm{\Omega_{m}}$ = 0.3.

##  2 Survey

###  2.1 MEGADES goals

The fundamental objective of MEGADES is to understand the impact of secular
processes on discs evolution. Our primary goal is to test whether gas infall
is one of the main mechanisms responsible for the evolution of galaxies and
whether it may be driving the inside-out formation of discs. Therefore, we
will test the predictions of models for this scenario against our MEGADES
observations, analysing the causes of the differences both secular and
external, i.e. nuclear activity, stellar migration, minor mergers, intense
star formation, etc. We will use MEGARA high spatial and spectral resolution
2D spectroscopy to perform analyses of the kinematic properties of the stellar
component in order to detect the presence of different structures such as bars
or inner discs in the central regions of MEGADES galaxies. We will also
analyse line indices sensitive to age and metallicity (Chamorro-Cazorla et
al., 2022) and, for the detected HII regions, the gas emission line spectrum
from which we will produce diagnostic diagrams and determine chemical
abundances. Finally, we will study in MEGADES sample the presence and
frequency of GWs, both in its warm and cold phases. We will examine the
kinematic properties and the shape of the H$\alpha$ emission line to
disentangle different kinematic components to study the warm phase, while we
will use the NaI D absorption doublet to analyse the cold phase.

With this first data release we will be able to perform all the above-
mentioned studies in the central regions of the galaxies in the sample and
then put them in context with the outer emission disc analyses when these
observations become available. We will study the stellar populations that make
up the bulges of the MEGADES galaxies together with their kinematical
properties. With this information we will be able to know whether classical
bulges dominate the galaxies in our sample or whether there are more galaxies
with pseudobulges (Kormendy & Kennicutt, 2004). We will also study the
possible presence of outflows associated to nuclear starbursts and AGN,
considering the existence of different kinematic components, in neutral or
warm phase, and the differences that may exist between both phases. In
addition to all of this, we will be able to generate different diagnostic
diagrams with the information contained in the different lines we will study.

###  2.2 MEGADES sample

Figure 1: RGB images of some of the galaxies in the MEGADES sample from
PanSTARRS observations (g, r and i filters). The white box in the centre of
each panel indicates MEGARA IFU FoV. The images of all the galaxies in the
MEGADES sample can be found in the Appendix A, Figure 9.

The original MEGADES sample (see Table 1 and Figures 1 and 9) was extracted
from the S4G sample (Spitzer Survey of Stellar Structure in Galaxies, Sheth et
al. 2010). The S4G was designed as a volume- (d ¡ 40 Mpc, —b— ¿ 30∘),
magnitude- (mB,corr ¡ 15.5 mag), and diameter-limited (D25 ¿ 1′) survey. The
use of MEGARA IFU to carry out all observations makes a diameter limited
sample a reasonable idea, as has been the case for the CALIFA sample (Sánchez
et al., 2012) or PHANGS-MUSE (Emsellem et al., 2022).

Besides the limitations of the S4G sample we need to impose further
constraints to make the sample fit our scientific goals. We want to avoid
dwarf systems and elliptical galaxies since, in most cases, these are not
rotationally supported. We also need to remove galaxies with inclinations
higher than 70∘ to be able to perform the analyses on metallicity and have the
possibility to derive velocity ellipsoids.

Taking into account all the previous considerations, and given that the entire
S4G sample (2331 galaxies) is unfeasible to observe given the needed telescope
time, we must consider further selection criteria in our sample: we limit the
diameter to a range where, on the lower side, we remove the smallest galaxies
and, on the upper side, we limit the diameter to approximately the field of
view of the MEGARA MOS (2.5′¡ D25 ¡ 4′)111Note that as part of the outer-disc
extension of MEGADES we plan to obtain MOS spectra of HII regions over the
whole galaxies extension., and a limit in declination (Dec(J2000) ¿
$-20^{\circ}$). Summarising all the above selection criteria we now have the
following:

  * •

Distance ¡ 40 Mpc (z $\sim$ 0.0092)

  * •

Galactic latitude —b— ¿ 30∘

  * •

Declination Dec(J2000) ¿ –20∘

  * •

Apparent magnitude mB,corr ¡ 15.5 mag.

  * •

Apparent diameter 2.5′¡ D25 ¡ 4′

  * •

Inclination i ¡ 70∘

This paper includes a random subsample of 30 out of the 215 galaxies that meet
the selection criteria of the MEGADES-S4G sample (see Table 1). However, to
enrich our sample we have added 13 galaxies from the CALIFA sample (Calar Alto
Legacy Integral Field Area Survey, marked with † in Table 1) which show signs
of containing interstellar NaI D and are possible candidates for hosting
galactic winds in their neutral phase by having an EWISM-NaID ¿ 1.5 $\AA$.
CALIFA is a survey, with SDSS DR7 (Abazajian et al., 2009) as the parent
sample, limited in diameter (45″¡ D25 ¡ 80″), redshift (0.005 ¡ z ¡ 0.03),
galactic latitude above 20∘ and declination above +7∘.

Table 1: Global properties of the galaxy sample.

Name | Morphology(1) | t(2) | z(1) | R.A.(1) | Dec.(1) | Scale(3) | DL (3) | Spectral class(1)
---|---|---|---|---|---|---|---|---
|  |  |  | (J2000) | (J2000) | [kpc arcsec-1] | [Mpc] |
IC 1683† | S? | 2.7 | 0.01624 | 01${}^{\textup{h}}$22${}^{\textup{m}}$39${}^{\textup{s}}$.000 | +34${}^{\textup{d}}$26${}^{\textup{m}}$13${}^{\textup{s}}$.000 | 0.331 | 70.4 | -
NGC 0023† | SB(s)a | 1.2 | 0.01523 | 00${}^{\textup{h}}$09${}^{\textup{m}}$53${}^{\textup{s}}$.410 | +25${}^{\textup{d}}$55${}^{\textup{m}}$25${}^{\textup{s}}$.600 | 0.310 | 66.0 | HII LIRG
NGC 0600 | (R’)SB(rs)d | 7.0 | 0.00614 | 01${}^{\textup{h}}$33${}^{\textup{m}}$05${}^{\textup{s}}$.300 | -07${}^{\textup{d}}$18${}^{\textup{m}}$41${}^{\textup{s}}$.100 | 0.127 | 26.4 | HII
NGC 0716† | SBa: | 1.1 | 0.01521 | 01${}^{\textup{h}}$52${}^{\textup{m}}$59${}^{\textup{s}}$.681 | +12${}^{\textup{d}}$42${}^{\textup{m}}$30${}^{\textup{s}}$.490 | 0.310 | 65.9 | HII
NGC 0718 | SAB(s)a | 1.0 | 0.00578 | 01${}^{\textup{h}}$53${}^{\textup{m}}$13${}^{\textup{s}}$.300 | +04${}^{\textup{d}}$11${}^{\textup{m}}$45${}^{\textup{s}}$.000 | 0.119 | 24.9 | -
NGC 1042 | SAB(rs)cd | 6.0 | 0.00457 | 02${}^{\textup{h}}$40${}^{\textup{m}}$23${}^{\textup{s}}$.967 | -08${}^{\textup{d}}$26${}^{\textup{m}}$00${}^{\textup{s}}$.760 | 0.094 | 19.6 | NLAGN
NGC 1087 | SAB(rs)c | 5.2 | 0.00508 | 02${}^{\textup{h}}$46${}^{\textup{m}}$25${}^{\textup{s}}$.164 | -00${}^{\textup{d}}$29${}^{\textup{m}}$55${}^{\textup{s}}$.140 | 0.105 | 21.8 | WR HII
NGC 2500 | SB(rs)d | 7.0 | 0.00171 | 08${}^{\textup{h}}$01${}^{\textup{m}}$53${}^{\textup{s}}$.210 | +50${}^{\textup{d}}$44${}^{\textup{m}}$13${}^{\textup{s}}$.600 | 0.035 | 7.3 | HII
NGC 2537 | SB(s)m pec | 8.8 | 0.00148 | 08${}^{\textup{h}}$13${}^{\textup{m}}$14${}^{\textup{s}}$.640 | +45${}^{\textup{d}}$59${}^{\textup{m}}$23${}^{\textup{s}}$.300 | 0.031 | 6.3 | HII
NGC 2543† | SB(s)b | 3.0 | 0.00824 | 08${}^{\textup{h}}$12${}^{\textup{m}}$57${}^{\textup{s}}$.922 | +36${}^{\textup{d}}$15${}^{\textup{m}}$16${}^{\textup{s}}$.660 | 0.169 | 35.5 | -
NGC 2552 | SA(s)m? | 9.0 | 0.00175 | 08${}^{\textup{h}}$19${}^{\textup{m}}$20${}^{\textup{s}}$.532 | +50${}^{\textup{d}}$00${}^{\textup{m}}$34${}^{\textup{s}}$.660 | 0.036 | 7.5 | -
NGC 2967 | SA(s)c | 5.2 | 0.00626 | 09${}^{\textup{h}}$42${}^{\textup{m}}$03${}^{\textup{s}}$.295 | +00${}^{\textup{d}}$20${}^{\textup{m}}$11${}^{\textup{s}}$.180 | 0.169 | 26.9 | -
NGC 3104 | IAB(s)m | 9.9 | 0.00201 | 10${}^{\textup{h}}$03${}^{\textup{m}}$57${}^{\textup{s}}$.350 | +40${}^{\textup{d}}$45${}^{\textup{m}}$24${}^{\textup{s}}$.900 | 0.042 | 8.6 | HII
NGC 3485 | SB(r)b: | 3.2 | 0.00478 | 11${}^{\textup{h}}$00${}^{\textup{m}}$02${}^{\textup{s}}$.380 | +14${}^{\textup{d}}$50${}^{\textup{m}}$29${}^{\textup{s}}$.700 | 0.099 | 20.5 | AGN
NGC 3507 | SB(s)b | 3.1 | 0.00327 | 11${}^{\textup{h}}$03${}^{\textup{m}}$25${}^{\textup{s}}$.357 | +18${}^{\textup{d}}$08${}^{\textup{m}}$07${}^{\textup{s}}$.620 | 0.068 | 14.0 | LINER
NGC 3780 | SA(s)c: | 5.2 | 0.00798 | 11${}^{\textup{h}}$39${}^{\textup{m}}$22${}^{\textup{s}}$.360 | +56${}^{\textup{d}}$16${}^{\textup{m}}$14${}^{\textup{s}}$.400 | 0.164 | 34.4 | -
NGC 3893 | SAB(rs)c: | 5.1 | 0.00323 | 11${}^{\textup{h}}$48${}^{\textup{m}}$38${}^{\textup{s}}$.190 | +48${}^{\textup{d}}$42${}^{\textup{m}}$39${}^{\textup{s}}$.000 | 0.067 | 13.9 | HII
NGC 3982 | SAB(r)b: | 3.2 | 0.00371 | 11${}^{\textup{h}}$56${}^{\textup{m}}$28${}^{\textup{s}}$.129 | +55${}^{\textup{d}}$07${}^{\textup{m}}$30${}^{\textup{s}}$.860 | 0.077 | 15.9 | HII Sy2
NGC 3998* | SA0^0(r)? | -2.2 | 0.00350 | 11${}^{\textup{h}}$57${}^{\textup{m}}$56${}^{\textup{s}}$.133 | +55${}^{\textup{d}}$27${}^{\textup{m}}$12${}^{\textup{s}}$.922 | 0.072 | 15.0 | Sy1 LINER
NGC 4037 | SB(rs)b: | 3.3 | 0.00310 | 12${}^{\textup{h}}$01${}^{\textup{m}}$23${}^{\textup{s}}$.670 | +13${}^{\textup{d}}$24${}^{\textup{m}}$03${}^{\textup{s}}$.700 | 0.064 | 13.3 | -
NGC 4041 | SA(rs)bc: | 4.0 | 0.00405 | 12${}^{\textup{h}}$02${}^{\textup{m}}$12${}^{\textup{s}}$.202 | +62${}^{\textup{d}}$08${}^{\textup{m}}$14${}^{\textup{s}}$.000 | 0.084 | 17.4 | HII
NGC 4189 | SAB(rs)cd? | 5.9 | 0.00700 | 12${}^{\textup{h}}$13${}^{\textup{m}}$47${}^{\textup{s}}$.270 | +13${}^{\textup{d}}$25${}^{\textup{m}}$29${}^{\textup{s}}$.300 | 0.144 | 30.1 | HII
NGC 4278* | E1-2 | -4.8 | 0.00207 | 12${}^{\textup{h}}$20${}^{\textup{m}}$06${}^{\textup{s}}$.824 | +29${}^{\textup{d}}$16${}^{\textup{m}}$50${}^{\textup{s}}$.722 | 0.043 | 8.9 | Sy1 LINER
NGC 4593* | (R)SB(rs)b | 3.0 | 0.00831 | 12${}^{\textup{h}}$39${}^{\textup{m}}$39${}^{\textup{s}}$.425 | -05${}^{\textup{d}}$20${}^{\textup{m}}$39${}^{\textup{s}}$.340 | 0.171 | 35.8 | Sy1
NGC 4750* | (R)SA(rs)ab | 2.4 | 0.00540 | 12${}^{\textup{h}}$50${}^{\textup{m}}$07${}^{\textup{s}}$.271 | +72${}^{\textup{d}}$52${}^{\textup{m}}$28${}^{\textup{s}}$.720 | 0.111 | 23.2 | LINER
NGC 5218† | SB(s)b? pec | 3.1 | 0.00949 | 13${}^{\textup{h}}$32${}^{\textup{m}}$10${}^{\textup{s}}$.379 | +62${}^{\textup{d}}$46${}^{\textup{m}}$03${}^{\textup{s}}$.910 | 0.195 | 40.9 | HII LINER
NGC 5394† | SB(s)b pec | 3.1 | 0.01150 | 13${}^{\textup{h}}$58${}^{\textup{m}}$33${}^{\textup{s}}$.652 | +37${}^{\textup{d}}$27${}^{\textup{m}}$12${}^{\textup{s}}$.550 | 0.235 | 49.7 | HII LIRG
NGC 5616† | SBc | 4.0 | 0.02810 | 14${}^{\textup{h}}$24${}^{\textup{m}}$20${}^{\textup{s}}$.699 | +36${}^{\textup{d}}$27${}^{\textup{m}}$41${}^{\textup{s}}$.120 | 0.564 | 122.9 | -
NGC 5953† | SAa: pec | 0.1 | 0.00656 | 15${}^{\textup{h}}$34${}^{\textup{m}}$32${}^{\textup{s}}$.383 | +15${}^{\textup{d}}$11${}^{\textup{m}}$37${}^{\textup{s}}$.590 | 0.135 | 28.2 | Sy2 LINER
NGC 5957* | (R’)SAB(r)b | 2.9 | 0.00605 | 15${}^{\textup{h}}$35${}^{\textup{m}}$23${}^{\textup{s}}$.214 | +12${}^{\textup{d}}$02${}^{\textup{m}}$51${}^{\textup{s}}$.360 | 0.125 | 26.0 | LINER
NGC 5963 | S pec | 4.1 | 0.00219 | 15${}^{\textup{h}}$33${}^{\textup{m}}$27${}^{\textup{s}}$.860 | +56${}^{\textup{d}}$33${}^{\textup{m}}$34${}^{\textup{s}}$.900 | 0.045 | 9.4 | -
NGC 6027† | S0 pec | -1.5 | 0.01466 | 15${}^{\textup{h}}$59${}^{\textup{m}}$12${}^{\textup{s}}$.537 | +20${}^{\textup{d}}$45${}^{\textup{m}}$48${}^{\textup{s}}$.090 | 0.299 | 63.5 | HII
NGC 6140 | SB(s)cd pec | 5.6 | 0.00303 | 16${}^{\textup{h}}$20${}^{\textup{m}}$58${}^{\textup{s}}$.160 | +65${}^{\textup{d}}$23${}^{\textup{m}}$26${}^{\textup{s}}$.000 | 0.063 | 13.0 | -
NGC 6217 | (R)SB(rs)bc | 4.0 | 0.00454 | 16${}^{\textup{h}}$32${}^{\textup{m}}$39${}^{\textup{s}}$.200 | +78${}^{\textup{d}}$11${}^{\textup{m}}$53${}^{\textup{s}}$.400 | 0.094 | 19.5 | HII Sy2
NGC 6339 | SBd | 6.3 | 0.00703 | 17${}^{\textup{h}}$17${}^{\textup{m}}$06${}^{\textup{s}}$.500 | +40${}^{\textup{d}}$50${}^{\textup{m}}$41${}^{\textup{s}}$.900 | 0.145 | 30.3 | -
NGC 6412 | SA(s)c | 5.2 | 0.00438 | 17${}^{\textup{h}}$29${}^{\textup{m}}$37${}^{\textup{s}}$.510 | +75${}^{\textup{d}}$42${}^{\textup{m}}$15${}^{\textup{s}}$.900 | 0.090 | 18.8 | -
NGC 7025† | Sa | 1.0 | 0.01657 | 21${}^{\textup{h}}$07${}^{\textup{m}}$47${}^{\textup{s}}$.340 | +16${}^{\textup{d}}$20${}^{\textup{m}}$09${}^{\textup{s}}$.100 | 0.350 | 74.6 | -
NGC 7437 | SAB(rs)d | 6.7 | 0.00707 | 22${}^{\textup{h}}$58${}^{\textup{m}}$10${}^{\textup{s}}$.060 | +14${}^{\textup{d}}$18${}^{\textup{m}}$30${}^{\textup{s}}$.590 | 0.146 | 30.4 | -
NGC 7479* | SB(s)c | 4.3 | 0.00792 | 23${}^{\textup{h}}$04${}^{\textup{m}}$56${}^{\textup{s}}$.650 | +12${}^{\textup{d}}$19${}^{\textup{m}}$22${}^{\textup{s}}$.400 | 0.163 | 34.1 | Sy2 LINER
NGC 7591† | SBbc | 3.6 | 0.01654 | 23${}^{\textup{h}}$18${}^{\textup{m}}$16${}^{\textup{s}}$.280 | +06${}^{\textup{d}}$35${}^{\textup{m}}$08${}^{\textup{s}}$.900 | 0.337 | 71.7 | Sy LIRG LINER
NGC 7738† | SB(rs)b | 3.0 | 0.02251 | 23${}^{\textup{h}}$44${}^{\textup{m}}$02${}^{\textup{s}}$.059 | +00${}^{\textup{d}}$30${}^{\textup{m}}$59${}^{\textup{s}}$.860 | 0.455 | 98.1 | -
NGC 7787† | (R’)SB(rs)0/a: | 0.4 | 0.02221 | 23${}^{\textup{h}}$56${}^{\textup{m}}$07${}^{\textup{s}}$.823 | +00${}^{\textup{d}}$32${}^{\textup{m}}$58${}^{\textup{s}}$.140 | 0.449 | 96.7 | -
PGC 066559 | SB(s)dm pec | 8.1 | 0.00900 | 21${}^{\textup{h}}$19${}^{\textup{m}}$43${}^{\textup{s}}$.040 | -07${}^{\textup{d}}$33${}^{\textup{m}}$12${}^{\textup{s}}$.500 | 0.185 | 38.8 | -

*Data products from these galaxies are under temporary embargo as they constitute the core of Laura Hermosa Muñoz’s Ph. D. thesis. † CALIFA Survey data. References. (1) NASA/IPAC Extragalactic Database. (2) Morphological type code from Hyperleda. (3) Wright (2006).

##  3 MEGARA observations

All MEGADES observations (43 galaxies) of this first release, DR1, have been
taken with the MEGARA IFU at GTC. The MEGARA IFU, or LCB, covers a field of
view of 12.5 $\times$ 11.3 arcsec2 using 567 fibres in a hexagonal
tessellation with a spaxel size of 0.62 arcsec. In addition to these fibres,
it also uses 56 extra fibres to measure sky background spectra simultaneously
with the observation of the scientific target in order to subtract it from the
observations afterwards. These sky fibres are distributed in 8 distinct
regions in the outermost parts of the MOS field of view, located between 1.75
and 2 arcmin from the IFU. The fundamental feature that makes MEGARA ideal for
performing the analyses required in MEGADES is its spectral resolution. MEGARA
has 18 different Volume Phase Holographic (VPH) transmission gratings that
allow to cover a range from 3650 to 9700 Å with low (LR), medium (MR) and high
(HR) resolutions.

Table 2: MEGARA VPHs specifications.

MEGARA VPH | Spectral Coverage | R.L.D.* | $\mathrm{\Delta\lambda_{FWHM}}$ | R
---|---|---|---|---
| [$\AA$] | [$\AA$ px-1] | [$\AA$] |
LR-B | 4350.6 - 5250.8 | 0.23 | 0.79 | 6061
LR-V | 5165.6 - 6176.2 | 0.27 | 0.94 | 6078
LR-R | 6158.3 - 7287.7 | 0.31 | 1.11 | 6100

*Reciprocal linear dispersion.

We have observed the central regions of the MEGADES galaxies using three
different VPHs, VPH480-LR (LR-B), VPH570-LR (LR-V) and VPH675-LR (LR-R) whose
specifications can be found in Table 2. This produces a combined spectrum
covering the spectral range from 4350 to 7288 Å (see Figure 2 for the combined
MEGARA spectrum of NGC 0718). This range includes spectral features such as
H$\beta$, $[\mathrm{OIII}]\lambda 5007$, NaI D and H$\alpha$, as well as
absorption features needed for the stellar populations analysis (Chamorro-
Cazorla et al., 2022). For the 13 CALIFA galaxies we have LR-V and LR-R
observations only, enough to study the possible presence of outflows in both
the neutral (NaI D) and the warm (H$\alpha$) components.

Figure 2: Concatenated spectra of the galaxy NGC 0718 observed with the VPHs
LR-B, LR-V and LR-R in blue, green and red, respectively. The spectrum of each
VPH is the result of coadding the 567 spectra measured simultaneously in one
single exposure with the MEGARA IFU.

Table 6 shows the observing dates, airmass and exposure times for all the
galaxies in the sample. Table 7 provides the observing conditions for each
observing night with information on the seeing and atmospheric transparency.
Figure 3 shows a histogram with the seeing values provided by the monitors of
the observatory during the nights of MEGADES observations.

Figure 3: Seeing values, provided by the monitors of observatory, histogram
for the nights of the MEGADES sample observations. The black dashed line
indicates the size of the hexagonal spaxels that comprise the MEGARA IFU.

##  4 Data processing

###  4.1 Basic reduction: MEGARA DRP

We have used the MEGARA data reduction pipeline (DRP) v0.12.0 (Pascual et al.,
2022) to process all the data provided by the telescope. This data reduction
has been performed according to the instructions provided in Castillo-Morales
et al. (2020)222DOI: 10.5281/zenodo.1974953.We have followed the same routines
that we used to reduce the data for the galaxy NGC 7025 in Chamorro-Cazorla et
al. (2022), considering the particularities of each individual observation.

We start by removing the level of Bias present in the image. This Bias is an
electronic pedestal that is added to all images taken with MEGARA before the
Analog-to-Digital Converter (ADC) to minimize errors associated with the
conversion of voltages that would otherwise be very close to zero. For the
purpose of removing this pedestal from the counts in the images we use the
bias calibration images and the task MegaraBiasImage. It is worth mentioning
that these images are taken with zero exposure time and that the bias level is
slightly different at the top and bottom of the image ($\sim$ 100 counts)
since the MEGARA CCD (a CCD231-84 from E2V) is read through two diagonally-
opposed amplifiers. The use of two (instead of the four available) amplifiers
minimizes electronic cross-talk during the reading-out process. The images are
corrected from overscan and trimmed to match the physical size of the
detector. Bad pixels are automatically masked in this step with the file
master_bpm.fits, available in the pipeline.

Then we need to properly trace the trajectory of each one of our fibre spectra
on the detector. To do this, we use the TraceMap calibration images obtained
by illuminating the focal plane with a halogen lamp providing homogeneous
illumination. When performing this step, we have to be careful with the
calibration images we use since the position of the fibres on the detector
changes depending on the temperature of the telescope. It is recommended to
use calibration images as close as possible to the observation of the
scientific target to minimise the temperature difference between them as much
as possible. The DRP tasks we use for this purpose is MegaraTraceMap and
MegaraModelMap. The first task is in charge of tracking the path of the light
projected by the fibres on the CCD while the second task makes a more
exhaustive model of it in order to do the extraction assuming each fibre has a
gaussian light profile across dispersion. In some cases, 11 observations taken
with LR-B and 3 with LR-R, we had problems tracing the last fibre box (from
fibre 603 to 623) because the instrument was not properly focused. In these
cases the spectra from these fibres have been ignored. An example of the
impact caused by doing this on our data can be seen in Figure 23. In subfigure
(t) and (v), continuum images taken with LR-B and LR-R, respectively, can be
seen and all the spaxels corresponding to the out-of-focus fibres are shown in
white.

We are now ready to perform the wavelength calibration. Here we use images
that are acquired using ThAr or ThNe lamps depending generally on the VPH we
are using for our observation. As a general rule, for LR-B, LR-V and LR-R
observations we use ThAr lamps. For this task, it is necessary to check that
the traces obtained in the previous tasks match the path followed by the
projection of the fibres on the detector in the images of the arc lamps. If
there is a small offset between the two, this can be corrected by indicating
it in the recipe we are going to use, MegaraArcCalibration. In this paper we
have only calibrated three observations with ThNe lamps: NGC 4189, NGC 2552
and NGC 3104 observed with LR-R, being these calibrations even more precise,
with RMS ¡ 0.5 km s-1(see Figure 4). The median RMS values in $\mathrm{\AA}$
for LR-B, LR-V and LR-R are 0.019, 0.030 and 0.020, respectively, and 0.020
for the whole sample. We find that the RMS represent only 2.4% (LR-B), 3.2%
(LR-V) and 1.8% (LR-R) of the reciprocal linear dispersion of each VPH (see
Table 2). In Figure 4 we show the average of the wavelength calibration RMS
(in km s-1) for each observation, separated by VPH, of the fittings once all
observations are calibrated in wavelength.

Figure 4: Wavelength calibration RMS of all observations by VPH. Blue, green
and red histograms represent LR-B, LR-V and LR-R observations, respectively.

MEGARA transmits the light from the focal plane of the telescope to the
spectrograph via optical fibres. This means that each fibre transmits the
light in a different way and with different wavelength dependencies. The fibre
specific calibration of the variation in transmission as a function of
wavelength is done with the MegaraFiberFlatImage task using the same images as
those used for the MegaraTraceMap recipe. The median of the standard deviation
values of the fiber-to-fiber sensitivity variation for each VPH in our sample
exposures are 0.090, 0.086 and 0.096 for LR-B, LR-V and LR-R, respectively.

The last correction to fully reduce our images is the flux calibration. For
this purpose we use spectra of standard stars available in the ESO
spectrophotometric standards database (Oke 1990; Hamuy et al. 1992, 1994):
BD+33 26 42, HD192281, HR153, HR718, HR1544, HR1996, HR3454, HR4468, HR4554,
HR4963, HR5501, HR7596, HR7950 and HR8634. The exposures of standard stars
also need to be examined to check if the previously computed traces are still
valid for these observations.

The task MegaraLcbAcquisition is in charge of finding the region of the MEGARA
IFU field-of-view where the standard star is located in order to know in which
fibres the spectra to be considered are placed when comparing them with those
of CALSPEC. To ensure that we do not miss flux when measuring in the image of
the star we compute the position of the centroid using the spectra of 3
concentric rings around the brightest spaxel (37 spaxels in total). In this
step an atmospheric extinction correction is also applied using a reference
file provided by GTC (King, 1985). With this information we can run the
MegaraLcbStdStar task that produces the sensitivity curve needed to flux
calibrate our data. This curve is saved in the master_sensitivity.fits file.
Figure 5 shows the sensitivity curves for the flux calibration obtained during
the reduction of the whole sample. It should be mentioned that absolute flux
calibration is difficult to guarantee, however, relative flux calibration is
much more reliable. This will be very important when measuring and comparing
lines that have been observed with the same VPH.

Figure 5: Sensitivity curves for the flux calibration of all MEGADES
observations. The subfigures (a), (b) and (c) show the sensitivity curves
derived from all the standard stars observed with the LR-B, LR-V and LR-R
gratings, respectively. Cyan lines mark the spectral coverage available, after
flux calibration, in Angstroms, for all fibres. Brown dashed lines indicate
the spectral coverage available, after flux calibration, in Angstroms, with a
proper flux calibration.

It is worth mentioning that the file master_sensitivity.fits includes in its
headers very valuable information when later analysing the MEGARA
observations. This information tells us the spectral coverage available, after
flux calibration, in pixels and in Angstroms for at least one fibre
(PIXLIMR1-2 and WAVLIMR1-2), common to all fibres (PIXLIMM1-2 and WAVLIMM1-2)
and with a proper flux calibration (PIXLIMF1-2 and WAVLIMF1-2). The WAVLIMM1-2
and WAVELIMF1-2 ranges are shown in Figure 5 using cyan and dashed brown
lines, respectively.

Now we have all the calibration files that an observation of this kind
requires. However, before applying all these corrections we must verify two
important details. The first one is to check if the traces we are using are
correct for the scientific target image or if we need to apply some offset on
these traces, as in previous steps. The second one is to check if we have any
diffuse light in these images that could spoil the observation, since in some
exposures there was moonlight that managed to sneak into the detector.
Fortunately, this light only adds a few counts to the image and can be easily
removed with one of the megaratools, megaratools-diffuse_light. This tool
makes a model of the diffuse light using information from the regions of the
CCD that are not illuminated by the fibres. This model allows us to eliminate
all traces of diffuse light from our observations. The observations affected
by this diffuse light are identified in Table 6 with the † symbol.

We can now finally apply all the corrections calculated in the data reduction
process, including diffuse light correction if necessary. For this purpose we
need to run the MegaraLcbImage task with the science images. This task, in
addition to applying all the corrections described in this section, also
performs a subtraction of an average spectrum of the sky background measured
simultaneously on the outermost 56 fibres of the MEGARA MOS. As final products
we have several images in Row-Stacked Spectra (RSS) format. This format
consists in a two-dimensional image with 623 rows, one for each fibre (567
target and 56 sky background fibres) and 4300 columns of spectral information.
The most relevant products we have at the end of the data reduction process
with the MEGARA DRP are: final_rss.fits which is the reduced science image
with sky subtraction applied, reduced_rss.fits which is the reduced science
image without removing the sky background and sky_rss.fits contains the sky
background information. The images we use for our analysis are the
final_rss.fits.

There is one final caveat before we start to do the pertinent analysis. In the
final_rss.fits, if there is too much contrast between the brightest spaxel and
the adjacent fibres, there may be crosstalk in these fibres that the ModelMap
could not account for. This usually happens when the galaxy is not well
centred in the IFU. We must take this into account in order not to draw the
wrong conclusions in cases where this effect is noticeable.

###  4.2 Post-processing

####  4.2.1 Astrometry correction

We may wish to compare our observations with observations from other studies,
either photometric or spectroscopic, so we are interested in keeping the
coordinates of our pointings as precise as possible. If we look on the GTC
website we find that the pointing accuracy of the telescope is around 1 to 2
arcsec. For this reason we have checked whether the coordinates that appear in
the headers of our images correspond to the real coordinates of the objects.
To do so, we compared our observations with the images available in the Pan-
STARRS survey (Chambers et al., 2016) and measured the offsets between the
peak of the brightest contours in the Pan-STARRS images and the brightest
spaxel in the MEGARA observations. This procedure allows us to ensure that our
pointing is correct with an error $\leq$ 1 spaxel (0.62 ″). Figure 6 shows the
offsets we have applied to our observations, both in Right Ascension and
Declination, to match the Pan-STARRS pointing. It can be clearly seen that in
most cases the deviation of the targets follow the same trend.

Figure 6: Offsets applied to all MEGADES sample images for pointing
correction. The blue, green and red dots represent the offsets in the LR-B,
LR-V and LR-R observations, respectively.

The information of the new pointing has been included in the header of the
final_rss.fits images by updating the following keywords: RADEG, DECDEG, RA
and DEC from extension 0 and CRVAL1 and CRVAL2 from extension 1 (FIBERS
extension). Note that the FIBERS extension is the one used by the ”cube”
megaratool.

####  4.2.2 Voronoi binning

A systematic procedure has been followed to analyse all the MEGADES data,
using the final RSS as a departure point. First we must separate the stellar
signal from the interstellar contribution in our spectra. However, if we want
to make sure that our data are sufficiently robust to make this distinction,
we must perform a Voronoi binning first. We use the Voronoi binning method by
Cappellari & Copin (2003) to reach, at least, a signal-to-noise ratio (S/N)
per angstrom of 10. We calculate this signal-to-noise ratio in the continuum
near the spectral lines of interest in each spectral range and taking into
account the redshift of each galaxy. For LR-B we measure it between 4800
$\mathrm{\AA}$ and 4850 $\mathrm{\AA}$, for LR-V between 5920 $\mathrm{\AA}$
and 5970 $\mathrm{\AA}$ and for LR-R between 6604 $\mathrm{\AA}$ and 6654
$\mathrm{\AA}$ (all rest-frame). It is worth bearing in mind that each
observation will have a different Voronoi binning independent from the rest of
the observations, given that it only depends on the signal-to-noise ratio we
measure in the spaxels of each exposure.

Figure 7 (bottom panel) shows the S/N as a function of the distance to the
brightest spaxel in each observation for all spaxels in the MEGADES sample
separated by VPH. The solid lines represent the median S/N value for each
distance and the dashed lines represent the corresponding 5th and 95th
percentile bin. We see that the S/N values decrease as we move away from the
centre of the galaxies, as expected. Also, the small differences that may
exist between the different VPHs become smaller with distance from the
brightest spaxel. In the top panel of Figure 7 we show the surface AB
magnitude per spaxel as a function of distance to the brightest spaxel, in a
similar way as in the S/N figure. In this case we see that the behaviour of
the curves is similar to that of the S/N since the noise level along the whole
detector is rather homogeneous.

Figure 7: Top panel: surface AB magnitude per spaxel for all the spaxels in
the sample as a function of the distance. In both cases, the solid lines
represent the median S/N value for each distance and VPH and the dashed lines
encompass the 5th and 95th percentiles for each of them. The blue, green and
red colors correspond to the measurements for the LR-B, LR-V and LR-R
observations, respectively. Bottom panel: signal-to-noise ratio per Angstrom
of all the spaxels in the sample as a function of the distance. The dashed
horizontal line identifies the S/N 10 value.

Out of the 65905 spaxels in our sample, 32493 ($\sim$ 49%) have a S/N below
10. This means that all these spaxels will be combined within a Voronoi region
in their corresponding observation and the rest of them will be analysed
individually. After performing the Voronoi binning of the data, we find that
91.6 % of all Voronoi regions in the sample consist of 3 or less spaxels.

If we look at the median S/N values plotted in Figure 7 and observe where they
intersect with the horizontal dashed line, which represents the S/N cut we
have made to create the Voronoi regions, we see that we reach in single
spaxels a distance of 2 arcsec, 3.5 arcsec and 4 arcsec for the LR-B, LR-V and
LR-R observations, respectively. Being the average sample distance 34.8 Mpc,
it means that we can reach 0.33 kpc (LR-B), 0.58 kpc (LR-V) and 0.66 kpc
(LR-R) without binning.

##  5 Analysis

For the analysis of the stellar continuum (and its subtraction to do the line
analysis) the same software used for the pilot study of the MEGADES sample
performed in Chamorro-Cazorla et al. 2022 was employed; the Penalized Pixel-
Fitting (pPXF) by Cappellari & Emsellem 2004 (see also Cappellari 2017).
Thanks to the use of pPXF, besides being able to discern which features have a
stellar origin and which features do not, we will manage to know the kinematic
characteristics of the stellar component of galaxies and their stellar
populations (which will be discussed in future papers). In section 5.1 we
describe in detail how we have applied this software to our data.

One of the products we obtain from the pPXF analysis are the residuals that
remain after subtracting the best fit to the original data. This means that
all features found in these residual spectra, both in emission and absorption,
do not have a stellar origin. Such information will allow us to study the
interstellar gas in both ionized and neutral phases present in these galaxies.

However, in contrast to the stellar information, the interstellar spectral
lines will not be analysed from a Voronoi binned region, but rather they will
be analysed spaxel by spaxel. This means that we have to use the best-fitting
spectra of the underlying stellar populations in Voronoi regions to subtract
the stellar continuum from individual spaxels. Naturally, the continuum level
differs between the Voronoi regions and the induvidual spaxels that comprise
these regions, so we need to normalise each best-fitting Voronoi spectrum to
the continuum level of the original spaxel in order to correctly subtract the
stellar information. Moreover, since we have the continuum level information
in the spectral range in which we have measured the signal, close to the lines
of interest, we can add to the residuals the same continuum level as in the
original data in order to measure the equivalent widths (EW) of the lines of
interest in our spectra. In section 5.2 we discuss in detail which lines we
have studied and the method applied for that purpose.

###  5.1 Stellar continuum fitting

To extract all the information of the stellar continuum present in our spectra
we have used, as mentioned above, the full spectral fitting analysis software
pPXF. This programme is based on the analysis of the absorption lines in order
to estimate the stellar populations present in the spectra. This makes
essential to avoid the emission lines we may have in the spectra from
affecting these fits. For this purpose, we mask the spectral regions where we
have this type of feature, and the regions where the subtraction of the sky
lines has not been perfect. These masked regions may be different depending on
the galaxy we are analysing at the moment, as not all of them have the same
spectral features. These characteristics can even change from one spaxel to
another within the same observation. It is also important, in the case of LR-V
observations, to mask, in addition to these areas, the wavelength range
corresponding to the sodium doublet (NaI D), since pPXF is in charge of
distinguishing, using the rest of the absorption lines, which part of this
absorption comes from the stars and which from the interstellar medium. In
Figure 8 we show some examples of these fittings and the different masked
regions depending on the analysed galaxy.

Figure 8: Examples of stellar continuum fitting using pPXF for 2 galaxies of
the sample. Top left panel: NGC 0718 spectrum in the LR-B spectral range. Top
right panel: NGC 0718 spectrum in the LR-V spectral range. Bottom left panel:
NGC 3982 spectrum in the LR-B spectral range. Bottom right panel: NGC 3982
spectrum in the LR-V spectral range. The black lines show the original
spectrum observed with MEGARA for these spaxels. The red lines are the best
fit performed by pPXF on the data. The green lines are the residuals resulting
from subtracting the best model from the original data. The blue vertical
regions are the areas that have been masked during the fitting.

The process we have followed to perform our analysis with pPXF is similar to
the one followed in the study by Kacharov et al. (2018). We first analyse the
properties of the stellar kinematics using additive and multiplicative
polynomials of degree 10. After this step we already know the velocity,
velocity dispersion, skewness and kurtosis in our data. This allows us to
analyse the stellar populations by fixing their kinematic parameters. In this
step, where we analyse the stellar populations, we set the degree of the
additive polynomials to 0 and keep the degree of the multiplicative
polynomials at 10. These latter fittings are the ones we use to separate the
stellar signal from the interstellar component. Throughout all of our
analyses, we use a first-order regularisation with a low factor (R = 5; see
Kacharov et al. 2018).

It is necessary to use stellar population synthesis models in order to be able
to fit our spectra. In the case of this survey we have used the stellar
population synthesis model predictions (SSP) by Vazdekis et al. (2010) based
on MILES stellar library and Padova+00 isochrones (Sánchez-Blázquez et al.
2006 and Falcón-Barroso et al. 2011). These models (350 in total) cover a very
wide age range, from 0.063 Gyr to 17.78 Gyr, and have seven different levels
of metallicity ([M/H]): $-2.32$, $-1.71$, $-1.31$, $-0.71$, $-0.40$, 0.00 y
+0.22. Its spectral coverage, ranging from 3525 $\mathrm{\AA}$ to 7500
$\mathrm{\AA}$, perfectly spans the spectral range of the MEGARA VPHs used in
this survey. Although the FWHM of the MILES models (2.5 Å) is worse than our
MEGARA observations (R$\sim$6000), in the central regions of the galaxies the
broadening of the lines along the line of sight allows the use of these
models. Of all the possible initial mass functions (IMF) available we have
used a unimodal IMF with a logarithmic slope of 1.3, i.e. Salpeter (Salpeter,
1955) and have not taken into account any kind of $\alpha$-enhancement as they
are not available in these models.

###  5.2 Emission and absorption lines

We followed different strategies for the analysis of the lines depending on
the VPH used for the observation. For the lines observed with LR-B, H$\beta$
and $[\mathrm{OIII}]\lambda 5007$, as well as for the lines observed within
LR-V, i. e. NaI D, we have employed the spectra obtained from the residuals of
the stellar populations with a constant flux bias added to match the level of
the original (reduced) spectra for EW measurement purposes. However, for the
lines observed within LR-R, H$\alpha$, $[\mathrm{NII}]\lambda 6584$ and the
two [SII] lines, $[\mathrm{SII}]\lambda 6717$ and $[\mathrm{SII}]\lambda
6731$, we have used a different procedure. This is due to the fact that in the
wavelength range covered by this VPH there are not many spectral features
available to fit the stellar populations in a robust way. Therefore, we
measured the lines in this spectral range directly on the reduced observations
of the galaxy.

There is only one of the four lines we measured in this setup that can be
affected by absorption features of variable intensity depending on the
different underlying stellar populations present, this is H$\alpha$. To solve
this problem we combined the LR-V and LR-R observations so that we could take
advantage of the information present in the LR-V spectra to estimate the
H$\alpha$ component in absorption. We then corrected our flux and EW
measurements for H$\alpha$ in emission in the following way:

${\rm EW}_{\rm total}={\rm EW}_{\rm emission}+|{\rm EW}_{\rm absorption}|$

${\rm Flux}_{\rm total}={\rm EW}_{\rm total}\times{\rm Flux}_{\rm continuum}$

Given that sometimes the pointing of the observations varies from one VPH to
another in the same galaxy we could think that the spaxel in which we measure
the emission does not have the same underlying stellar population as the
spaxel in which we measure the absorption. However, since the offsets between
observations are rather small (typically 1 spaxel) and the variations in the
absorption of the H$\alpha$ line are not affected at these scales, we find
that this correction is not affected by this effect, as will be shown in the
MEGADES II paper.

All lines with a signal to noise ratio, measured at the peak of the line,
higher than 3 have been analysed individually using Gauss-Hermite models,
except for NaI D where we have used an anchored double Gaussian model. We have
also fitted the continuum emission around the lines using the information
available on both sides of the line. For this task we made use of one of the
tools developed for the analysis of MEGARA observations, the analyze_rss
megaratool. The spectral ranges used for each line can be seen in Table 3.

Table 3: Line fitting windows definitions in restframe. Ion | $\lambda_{0}$ | Line window | Continuum windows
---|---|---|---
| [Å] | [Å] | [Å]
H$\beta$ | 4861.333 | 4848 - 4877 | 4828 - 4848 & 4877 - 4892
$[\mathrm{OIII}]\lambda 5007$ | 5006.843 | 4997 - 5017 | 4977 - 4997 & 5017 - 5037
NaI D | 5889.950 | 5883 - 5905 | 5850 - 5870 & 5910 - 5930
H$\alpha$ | 6562.819 | 6555 - 6573 | 6513 - 6533 & 6598 - 6618
$[\mathrm{NII}]\lambda 6584$ | 6583.460 | 6575 - 6594 | 6513 - 6533 & 6598 - 6618
$[\mathrm{SII}]\lambda 6717$ | 6716.440 | 6707 - 6724 | 6680 - 6700 & 6751 - 6771
$[\mathrm{SII}]\lambda 6731$ | 6730.810 | 6724 - 6741 | 6680 - 6700 & 6751 - 6771

##  6 Data Release

MEGADES is a legacy survey and therefore we make all data public to the
community in order to exploit its full potential. We deliver all our
observations reduced and with their pointings corrected, ready to do science
with them. In addition to the reduced observations we also deliver the
analyses of stellar kinematics performed with pPXF and the measurements of
some spectral lines carried out with analyze_rss. The study of the stellar
populations have been performed on the observations with a Voronoi binning so
that all analysed regions reach a signal-to-noise ratio of 10. On the other
hand, the study of the spectral lines have been done spaxel by spaxel, without
any kind of binning, in order to take full advantage of the spatial resolution
of the instrument. In future papers we will publish our analysis for the
stellar populations and diagnostic diagrams using all measured lines.

Some of the data included in this data release have already demonstrated their
scientific potential in the analysis of stellar kinematics (Dullo et al.,
2019) and stellar populations (Chamorro-Cazorla et al., 2022). Other MEGARA
observations have also been successfully exploited to study spatially resolved
outflows in cold and warm phases (Catalán-Torrecilla et al., 2020). This kind
of studies have also been carried out on AGN and LINER galaxies (Cazzoli et
al., 2020, 2022). Some other studies that have taken advantage of the MEGARA
observational features include Mayya et al. (2020) and Tendulkar et al.
(2021).

###  6.1 Data products for Data Release I

Both the observations and the products we deliver with this paper are in RSS
format. This format has already been explained in section 4.1. For the case of
the data products, each row in the two-dimensional data corresponds to a fibre
of the MEGARA IFU, as in the case of the observations, but each column instead
of being a different wavelength is a different property, depending on the
analysis we have performed. Information regarding which property is in which
column of each product type (stellar kinematic analysis or spectral line
analysis) is included within the headers of each file in extension #2. Tables
4 and 5 show the information in each column of the files for the products of
pPXF and line analysis, respectively.

All the file products are available on the MEGADES website (see $\S$6.3) and
the list of files with the information they contain is as follows:

  * •

$[\textit{OBJECT}]\\_[\textit{VPH}]\\_\textit{final\\_rss.fits}$: these are
the final products of the MEGARA DRP. They are “reduced” Row-Stacked Spectra
(RSS) 2D images including the 623 fiber spectra for the LCB mode, all using a
common flux calibration and wavelength solution with constant reciprocal
dispersion for all fibers and 4300 resolution elements. Based on the averaged
spectrum of all fibers to be used for sky subtraction (by default all 56 sky
fibers in the LCB) the DRP also performs the sky substraction correction to
generate this product. There is one of these files per observation.

  * •

$[\textit{OBJECT}]\\_[\textit{VPH}]\\_\textit{voronoi\\_rss\\_snr\\_10.fits}$:
these files contain the same information as the ”final_rss.fits” files with a
Voronoi binning performed on them to achieve a minimum signal to noise ratio
of 10. The format of this file is an RSS with the same dimensions as
”final_rss.fits” (623 rows and 4300 columns). Note that spaxels belonging to
the same Voronoi region share the same spectrum. There is one of these files
per observation.

  * •

$[\textit{OBJECT}]\\_[\textit{VPH}]\\_\textit{emission\\_final\\_rss.fits}$:
These files contain the result of subtracting the best fit performed with pPXF
on the Voronoi regions of the observations renormalised at the continuum level
of each individual spaxel to the observed spectra of each individual spaxel.
The format of this file is again an RSS with the same dimensions as
”final_rss.fits”. There is one of these files per observation.

  * •

$[\textit{OBJECT}]\textit{\\_LR-V\\_stellar\\_kin.fits}$: These files contain
the stellar kinematic information for each galaxy. It is in RSS format with
623 rows but this time only 12 columns, instead of the 4300 columns of the
previous files. In these 12 columns we have the information given in Table 4,
which is stored in extension #2 of these files (there is one per galaxy).
Table 5 shows the information contained in extension #2 of the header of these
files with each of the characteristics described here and the channel to which
they correspond.

  * •

$[\textit{OBJECT}]\\_[\textit{VPH}]\\_[\textit{SPECTRAL\\_LINE}]\textit{.fits}$:
These files contain the information of the different spectral lines that we
have analysed in this paper. The format of these files is an RSS with 623 rows
and 33 columns, one for each measured feature, and it has the same dimensions
for all the lines we have studied. The information available in each of the
columns is provided in Table 5. For the specific nomenclature of the files
containing the analysis of each line provided in this paper, depending on the
line, replace $[\textit{VPH}]\\_[\textit{SPECTRAL\\_LINE}]$ with: LR-B_Hbeta,
LR-B_O5007, LR-V_NaD, LR-R_Halpha, LR-R_N6584, LR-R_S6717 and LR-R_S6731.
There is one of these files per analysed line and galaxy. Table 5 shows the
information contained in extension #2 of the header of these files with each
of the characteristics described here and the file channel to which they
correspond.

Table 4: Information included in each channel of the products obtained with pPXF. This information is included in extension #2 of the header of the corresponding products. Channel (CH) | Description
---|---
01 | Velocity with heliocentric (if applied) in km/s
02 | Error of CH 01 in km/s
03 | Sigma in km/s corrected for instrumental sigma
04 | Error of CH 03 in km/s
05 | h3
06 | Error of CH 05
07 | h4
08 | Error of CH 06
09 | Velocity in km/s
10 | Error of CH 09 in km/s
11 | Sigma in km/s
12 | Error of CH 11 in km/s
Table 5: Information included in each channel of the products obtained with
analyze_rss.py. This information is included in extension #2 of the header of
the corresponding products.

Channel (CH) | Description
---|---
01 | Fitting method (0=gauss-hermite, 1=1gauss, 2=2gauss, 3=gaussian doublet)
02 | Continuum level in cgs†
03 | RMS in cgs
04 | S/N at the peak of the line
05 | Flux from window_data - window_continuum in cgs
06 | Flux from (window_data - window_continuum) / mean_continuum (EW) in $\mathrm{\AA}$
07 | Flux from best-fitting function(s) in cgs
08 | EW from best-fitting function(s) in $\mathrm{\AA}$
09 | Amplitude for methods 0 & 1 & 2 (first gaussian) & 3 (first gaussian) in cgs
10 | Central lambda (in $\mathrm{\AA}$) for methods 0 & 1 & 2 (first gaussian) & 3 (first gaussian)
11 | Sigma (in $\mathrm{\AA}$) for methods 0 & 1 & 2 (first gaussian) & 3 (first gaussian)
12 | h3 for method 0
13 | h4 for method 0
14 | Amplitude for method 2 (second gaussian) & 3 (first gaussian) in cgs
15 | Central lambda (in $\mathrm{\AA}$) for method 2 (second gaussian) & 3 (second gaussian)
16 | Sigma (in $\mathrm{\AA}$) for method 2 (second gaussian) & 3 (second gaussian)
17 | Velocity in km/s from CH 10 (1st gaussian)
18 | Velocity with heliocentric correction in km/s from CH 10 (1st gaussian)
19 | Sigma in km/s from CH 11 (1st gaussian)
20 | Sigma in km/s from CH 11 corrected for instrumental sigma (1st gaussian)
21 | Velocity in km/s from CH 15 (2nd gaussian)
22 | Velocity with heliocentric correction in km/s from CH 15 (2nd gaussian)
23 | Sigma in km/s from CH 16 (2nd gaussian)
24 | Sigma in km/s from CH 16 corrected for instrumental sigma (2nd gaussian)
25 | Flux from best-fitting 1st gaussian in cgs
26 | Flux from best-fitting 2nd gaussian in cgs
27 | Error of CH 05 (Flux from window_data - window_continuum) in cgs
28 | Error of CH 06 ((Flux from window_data - window_continuum) / mean_continuum) in $\mathrm{\AA}$
29 | Error of CH 07 (Flux from best-fitting function(s)) in cgs
30 | Error of CH 08 (EW from best-fitting function(s)) in $\mathrm{\AA}$
31 | Error of CH 17 (Velocity in km/s from CH 10 (1st gaussian)) in km/s
32 | Error of CH 21 (Velocity in km/s from CH 15 (2nd gaussian) in km/s
33 | Best-fitting $\mathrm{\chi}^{2}$ in cgs

†erg s-1 cm-2 Å-1

###  6.2 Maps

In addition to the data release, this work also presents some of the results
obtained. In the Appendix of this paper we show some figures derived from the
analyses described in section 5. A continuum image obtained using the LR-V VPH
(5165 Å- 6150 Å) with the PanSTARRS r-band image isophotes overplotted in
white can be seen in subfigure (a). Subfigure (b) shows the integrated
spectrum of the different observations. We do this by first summing the sky-
corrected spectra of all the IFU spaxels, and combine the information in the
different setups to cover the whole spectral range spanned by all the VPHs. We
also present the results of the study of stellar kinematics in the central
regions of the sample galaxies. In subfigures (c), (d), (e) and (f) we show
the stellar velocity, velocity dispersion, skewness and kurtosis maps,
respectively, for each galaxy. All these results have been computed on a
Voronoi binning to reach a signal-to-noise ratio per $\AA$ of 10. The data
corresponding to these maps are all included in this first data release.
Subfigure (s) shows a continuum RGB image obtained from the MEGARA
observations. To make such maps we aligned the continuum images of LR-B, LR-V
and LR-R (shown in subfigures (t), (u) and (v), respectively) based on their
corrected astrometry solution. In the case of observations with two out of the
three observations, the LR-V contribution to the image has been doubled.

For the galaxies in the S4G subsample we also show the flux, EW, velocity and
velocity dispersion data for all the lines we have studied in this paper,
H$\beta$ (subfigures (g), (h), (i) and (j), respectively),
$[\mathrm{OIII}]\lambda 5007$ (subfigures (k), (l), (m) and (n),
respectively), H$\alpha$ (subfigures (w), (x), (y) and (z), respectively),
$[\mathrm{NII}]\lambda 6584$ (subfigures ($\alpha$), ($\beta$), ($\gamma$) and
($\delta$), respectively) and the two [SII] lines, $[\mathrm{SII}]\lambda
6717$ (subfigures ($\epsilon$), ($\zeta$), ($\eta$) and ($\theta$),
respectively) and $[\mathrm{SII}]\lambda 6731$ (subfigures ($\iota$),
($\kappa$), ($\lambda$) and ($\mu$), respectively). For the NaI D line we
present the same results except for the case of velocity dispersion
(subfigures (o), (p), (q) and (r), respectively). Instead of that, we show the
map of the ratio between the flux of the doublet lines. For the 13 CALIFA
galaxies of the sample we only show the analysis for NaI D, H$\alpha$,
$[\mathrm{NII}]\lambda 6584$, $[\mathrm{SII}]\lambda 6717$ and
$[\mathrm{SII}]\lambda 6731$ lines, since we have no observations in LR-B. The
spaxels plotted on these maps are the ones with lines with a signal to noise
ratio at the peak of the line above 3, the rest of them remain blank.

In the galaxies marked as embargoed in Table 1 we have masked out the central
spaxel and two rings of spaxels around it (19 spaxels in total) due to the
presence of AGNs that make the spectral features in the central regions more
complex. In these areas we would need more kinematic components to analyse
these features. The studies related to these areas will be conducted in Laura
Hermosa Muñoz’s thesis to be presented at the end of this year.

###  6.3 Web interface

MEGADES Library Database is a software tool to manage all the observations and
data products obtained from the analyses performed on the entire MEGADES
sample. The database has been developed in MySQL with all the data available
to date and a web-based tool that allows handling them. The technologies
involved in the development of this software included: HTML, JavaScript and
Java Servlets with a Tomcat server and a MySQL database.

Security reasons require a public user name and password to access the MEGADES
database333https://www.megades.es. This username and password are,
respectively, ”public” and ”6BRLukU55E”.

##  7 Notes on some individual galaxies

IC 1683: The stellar component of this galaxy reveals a rotation pattern that
appears to be misaligned with respect to the photometric semi-major axis of
the galaxy. We detect ionised gas in most of the field. A star-forming ring is
found surrounding the position of the brightest region in the continuum. The
compactness of this structure (3 arcsec in diameter) possibly prevented its
detection in molecular gas emission by Bolatto et al. (2017). If we look at
the kinematics of this ionised gas component we discover a rather distorted
rotation pattern in the central region that can be explained using two
kinematic components: one component follows the galaxy general rotation field
traced by the stellar kinematics and the other component rotates with respect
to the ring minor axis. We also detect extended neutral gas corresponding to
the brightest areas in the stellar continuum.

NGC 0023: This galaxy shows stellar kinematics dominated by a rotation pattern
in the NS direction with and a clear rotation-h3 anticorrelation. The line
emission extends to the entire MEGARA IFU field of view in the shape of a
circumnuclear star-forming ring that also coincides with a minimum in the
width of the emission lines detected, with its East side partly obscured by an
intervening dust lane (see Figure 9). The ionised-gas velocity field is more
distorted than the stellar one. These distortions may be caused by the bar
hosted in its central region (de Vaucouleurs et al. 1976; see however Afanasev
et al. 1991). This galaxy has a companion, NGC 0026, but Epinat et al. (2008)
found no signs of any interaction in their H$\alpha$ velocity maps. It shares
several characteristics with IC 1683, including the presence of extended NaD
absorption, although its velocity field is misaligned by as much as $\sim$40∘.

NGC 0718: Early-type (SAB(s)a) spiral galaxy NGC 0718 shows stellar kinematics
dominated by a rotation pattern in the NS direction (quite different from the
RC3 photometric position angle of the disk) and a clear rotation-h3
anticorrelation. The line emission extends to the entire MEGARA IFU field of
view but its velocity field is more distorted than the stellar one.
Laurikainen et al. (2005) suggested the existence of a (secondary) nuclear bar
extending up to the 5 arcsec, which could be responsible for its ionised-gas
distorted kinematics as traced by the $[\mathrm{NII}]\lambda 6584$ emission,
since the H$\alpha$ emission barely detectable above its corresponding stellar
absorption. Díaz-García et al. (2020) identified the star formation in the
inner regions of this barred galaxy as limited to the circumnuclear region and
therefore having a passive bar.

NGC 1087: The central region of this galaxy shows two bright regions in the
continuum that do not resemble a bona-fide bulge (Eskridge et al., 2002), not
even a boxy, pseudo-bulge. This might be due in part to the presence of a dust
lane crossing in between them (see Figure 9). We measured a global stellar
rotation pattern with isovelocity curves running parallel from N to S. The
ionized gas shows a velocity pattern with a clear NE to SW gradient, similar
to that derived from CO (see Leroy et al. (2021)), but highly distorted and
showing the largest velocity gradient along the position and orientation of
the nuclear dust lane. The misalignment found between the stellar and ionised
gas velocity fields was already reported by Martin & Friedli (1997). The
irregular distribution of the ionized-gas velocity dispersion in the nuclear
region of NGC 1087 makes this galaxy a good candidate to perform a multi-
component analysis.

NGC 2537: The Bear Paw galaxy is a Blue Compact Dwarf (BCD) galaxy with most
of its star formation distributed in a ring with diameter 1.2 kpc, bright both
in H$\alpha$ and CO (Gil de Paz et al., 2002a) around a massive post-starburst
($\sim$30 Myr) region (Gil de Paz et al., 2000), that corresponds to our
MEGARA pointing. This results in a lack of bright line emission associated
with the nuclear region but faint widespread diffuse emission instead.
Interestingly, the variations found in radial velocity within the field are of
the order ($\pm$10 km s-1) of the expected differences in velocity between the
galaxy main (underlying) disk and the molecular ring formed as a consequence
of the evolution of the massive nuclear starburst (Gil de Paz et al., 2002b).

NGC 2543: The line emission extends over most of the MEGARA field of view,
although we identify a ring-like feature showing the highest equivalent
widths, which could indicate the presence of a young star-forming
circumnuclear ring of $\sim$3 arcsec (0.5 kpc) in radius. The stellar and
ionised-gas velocity fields are rather similar, although the stellar
kinematics become noisy beyond the innermost 4-5 arcsec. We also detected
neutral gas absorption coinciding with the inner edge (of $\sim$2 arcsec in
radius) of the ring showing high EW in emission that is also slightly
misaligned with respect to the ionised component. While Tang et al. (2008)
detected a tidal bridge between this galaxy and PGC 80408 that produces a
distortion in the H I disc of NGC 2543, it is unclear whether this interaction
could have any effect on the properties of these central regions. The
detection of a high-EW ring and the misaligned NaD nuclear absorption points
out towards a post-nuclear starburst scenario.

NGC 3893: In this galaxy we find a stellar velocity pattern from SE to NW with
a minimum in the stellar velocity dispersion coinciding with the galaxy
nuclear regions. Morphologically classified as a SAB(rs)c: galaxy with a
Sérsic index for its bulge component , as measured by Weinzirl et al. (2009),
of $n_{\mathrm{bulge}}$=2.05, close to the separation limit proposed by Fisher
& Drory (2008), we cannot conclude that this is secularly-formed pseudo-bulge,
although its low velocity dispersion ($\leq$60 km s-1) points in that
direction. We also find neutral gas absorption in the brightest areas in the
continuum of the galaxy with a small rotation pattern within the spaxels
detected. The most remarkable feature is the presence of widespread ionised-
gas emission, especially in H$\beta$, H$\alpha$, $[\mathrm{NII}]\lambda 6584$
and $[\mathrm{SII}]\lambda 6731$ lines, but not so much in
$[\mathrm{OIII}]\lambda 5007$. The corresponding velocity field seems to be
aligned with the stellar velocity field albeit some distortions in all lines
detected.

NGC 3982: This Seyfert 2 spiral galaxy has a cylindrical rotation pattern in
its stellar component. We have measured emission lines of ionised gas
throughout the entire field of the MEGARA IFU and in all the lines presented
in Figure 19. It appears that the velocity pattern of the ionised gas
component is formed by two distinct components, one of them rotates aligned
with the stellar component (PA$\sim$$-$165∘) and another with a rotation
pattern almost perpendicular to it. This could be caused by the 4 arcsec-long
bar in the bulge of this galaxy reported by Erwin (2005). The nuclear region
shows a maximum in velocity dispersion both in stars and the ionised gas
($\geq$70 km s-1).

NGC 3998: This LINER galaxy shows a clear rotation pattern for its stellar
component with a minor axis orientation of PA$\sim$45∘ and with the radial
velocity clearly anti-correlated with the skewness (h3), as sign of a strong
rotational support for the line-of-sight radial velocities measured. The
ionised gas also shows a well-ordered rotation although the minor axis PA is
close to 0∘. Neutral gas detected in the form of NaI D absorption is also
detected in most of the IFU field of view with a velocity pattern aligned with
that of the stellar component and very high equivalent widths of up to 2 Å.
This galaxy lacks LR-R observations.

NGC 4041: The faintness of the stellar continuum in this object does not allow
us to properly measure stellar kinematics even after a Voronoi binning is
applied. However, we do detect a large amount of ionised gas in all of the
spaxels of the field with a rather cylindrical rotation aligned with the
photometric semi-major axis of the galaxy and some small-scale (of the order
of a one to two spaxels or $\sim$100 pc) distortions superimposed. For this
object our LR-V observations are significantly less deep than for LR-B and
LR-R.

NGC 4189: In this barred spiral (SAB(rs)cd) galaxy we see that the rotation of
the stellar component is very different from that seen in the ionized gas.
Thus, while the line-of-sight stellar rotation is oriented from SW
(redshifted) to the NE (blueshifted), $\sim$60∘ away from the bar major axis,
the ionised-gas radial velocity field is well reproduced by the superposition
of two rotation patterns, one aligned with the bar with high amplitude and
another one, with lower amplitude, running roughly NS. The distorted velocity
field and the kinematic and photometric misalignment in the H$\alpha$
component was observed by Chemin et al. (2006) although with our data much
more detail can be discerned making of this object (and our data) prime
targets for the dynamical modeling of stars and gas in the presence of the
non-axisymmetric potential of a stellar bar.

NGC 4278: This LINER galaxy shows a clear anti-correlation between line-of-
sight radial velocity and skewness (h3). We also measure ionised gas emission
throughout the entire field of view, possibly associated with the halo
component assigned to this object by Masegosa et al. (2011). The line emission
detected shows a radial velocity field with a rotation pattern slightly
misaligned compared to the stellar one (PA$\sim$45∘ vs. 30∘ for the
corresponding receding semi-major axes, respectively). We also find a large
amount of absorbing NaI D throughout the field. This neutral gas shows radial
velocities that are at odds with those measured from either the stars or the
ionised gas. Such a peculiar velocity structure is compatible with the inflow
reported in this object by Cazzoli et al. (2018). This galaxy lacks LR-R
observations.

NGC 4593: This Seyfert 1 galaxy has a very clear rotation pattern in its
stellar component anti-correlated with h3 and a ring wide oriented along the
major axis with low velocity dispersion and relatively high kurtosis
(h4$\sim$+0.15). This region coincides with a lens in the continuum images
from PanSTARRS (see Figure 9) that is outlined by high-EW line-emitting
regions, some of them possibly embedded in the dust lanes that, according to
the HST images, seem to spiral in towards the position of the AGN. The
velocity pattern of the ionised gas is aligned with the velocity pattern of
the stars in spite of some small scale distorsions. We also detect neutral gas
absorption over a large fraction of the circumnuclear ring.

NGC 4750: The stellar component of this LINER galaxy shows a clear rotation
that, again, it is clearly anti-correlated with h3. The analysis of the
ionised-gas kinematics, as detected in all emission lines studied in this
paper, shows a rotation pattern that is composed of a circumnuclear component
that is clearly misaligned by almost 90∘ (clockwise) relative to the stellar
velocity field and a more extended component whose receding side is at
PA$\sim$0∘ (i.e. $\sim$45∘ counter-clockwise relative to the stars). The
complex behaviour of the line emission found in NGC 4750 is in agreement with
the finding of non-rotational motions in the narrow component by Cazzoli et
al. (2018). We do not detect NaD in absorption although such an interstellar
residual absorption has been previously reported by these same authors.

NGC 5218: This interacting spiral galaxy shows a stellar rotation pattern
misaligned with respect to its photometric semi-major axis. We detect gas in
both its neutral and ionised phases over a large part of the field of view.
The radial velocity and velocity dispersion maps in the case of the ionised
gas component appear highly distorted, likely due to the interaction with its
companion galaxy (NGC 5216) (Elfhag et al., 1996) but also to the presence of
a strong bar. NaD interstellar absorption is detected with equivalent widths
as high as 8 Å in the nucleus showing a velocity field roughly aligned with
the stellar velocity field but with two deep velocity minima (blueshifted by
$\sim$100 km s-1 relative to the systemic velocity) found at both edges of the
rotation axis.

NGC 5394: This object shows morphological and kinematic features similar to
those described for galaxy NGC 5218 object above. This is likely due to the
fact that also in this case the galaxy is in close interaction with a
(massive) companion, NGC 5395 at 27 kpc (Epinat et al., 2008) in the case of
NGC 5394, that have lead, among other things, to the development to two
opposite stellar tidal tails (see Figure 9). Thus, NGC 5394 shows a regular
stellar rotation pattern but highly distorted ionised gas velocity and
velocity dispersion maps. The NaD interstellar absorption is also very deep
(with EW as high as 6 Å) and spatially extended. Its velocity field appears to
have a rotation pattern perpendicular to that of the stellar component.

NGC 5616: Nearly edge-on spiral galaxy with a clear stellar rotation pattern
along with major axis and regions with stellar radial velocities close to the
galaxy systemic velocity along with minor axis. These correspond to regions
located at larger galactocentric distances with only a minor fraction of their
motion taking place along the line of sight. On the East side of the galaxy
center we identify, both in the PanSTARRS images and in our MEGARA false-color
images, a dust lane crossing the entire field of view that is embedded by
bright knots of star formation. The properties of this object as seen with
MEGARA resemble those of UGC 10205 (Catalán-Torrecilla et al., 2020),
including the presence of some NaI D absorption possibly associated with the
large gas column density and bright background continuum provided by the
galaxy nucleus. This absorption shows a slight rotation pattern aligned with
the stellar component.

NGC 5953: This Seyfert 2 galaxy shows stellar kinematics dominated by a
rotation pattern anti-correlated with h3. The line-of-sight kurtosis (h4)
values show a positive correlation with the radial velocities. We have been
able to detect and measure NaI D in absorption over a large part of the field
of view. Its velocity pattern is aligned perpendicularly to the stellar
rotation and it is highly distorted. As for the ionised gas component, we
identify an emission ring around the brightest continuum region (the continuum
know located 2 arcsec W of the nucleus appears to be a foreground star given
as it shows diffraction spikes in the HST images) that we relate to the 4
arcsec-radius star-forming ring reported by Riffel et al. (2006). The
innermost part of the ionised-gas radial velocity field shows a distortion
that could be explained as due to an overimposed rotation pattern aligned NS
of $\sim$2 arcsec ($\sim$300 pc) in length. It should be noted that, according
to Arp (1966), this galaxy is in interaction with NGC 5954.

NGC 6027: This lenticular galaxy shows a very clear rotation pattern in its
stellar component that is anti-correlated with h3. We detect neutral gas in
the form of blueshifted NaI D in absorption that appears to show a S-shaped
morphology in radial velocity. The most striking feature of this object, that
it is part of the Seyfert Sextet (Arp 1966; also known as HCG92), is the
presence of ionised-gas emission in H$\alpha$ and $[\mathrm{NII}]\lambda 6584$
that is counter-rotating relative to the stars. This was first reported by
Durbala et al. (2008) and it was interpreted as being a dwarf spiral accrete
during the assembly process of this compact group. The radial velocity
gradient in the ionised gas seems to be stepper (and of different sign) than
that of the stars, possibly due to a larger contribution of the axisymmetric-
drift correction to the latter.

NGC 6217: The stellar component of this Seyfert 2 galaxy shows radial
velocities that appear to be the superposition of two rotation patterns, both
with low amplitudes, one aligned from E to W and another one with the receding
side oriented towards a PA of $-$45∘. Both patterns are clearly visible when
analysing the ionised-gas radial velocity field, with the latter being
associated with a (young) circumnuclear star-forming ring of high equivalent
width in emission and 3 arcsec (300 pc) in diameter. In the paper by Hernandez
et al. (2005) they reported the existence of another H$\alpha$ ring of about
43 arcsec in diameter. Besides, we detect some neutral gas in the central part
of the galaxy, especially in its NE region.

NGC 6339: This barred spiral galaxy shows stellar kinematics with a rotation
pattern that is perpendicular with respect to the photometric semi-major axis
of the bar. Ionised-gas emission is found along the brightest continuum
regions, with the brightest emission-line region located at the easternmost
end of the field of view. The velocity distribution of the ionised gas is
distorted by showing an S-shape along its central part that could be explained
by two distinct kinematic components: one component that follows the stellar
kinematics and another that is slightly deflected in the direction of the
bright region to the east. This distortion could be due to the effects of the
bar or to the presence of star formation embedded in a dust lane that crosses
the galaxy nuclear regions from N to S. Note that this galaxy was classified
as having both circumnuclear star formation and hosting star formation along
the bar by Díaz-García et al. (2020).

NGC 7025: The stellar kinematics of this galaxy was studied in detail in the
paper by Dullo et al. (2019). It shows a rotation pattern anti-correlated with
h3. Its stellar kinematics along with its photometric properties led these
authors to suggest that NGC 7025 hosts a pseudo-bulge. Using MEGARA data,
Chamorro-Cazorla et al. (2022) proposed that NGC 7025 suffered a minor merger
$\sim$3.5-4.5 Gyr ago that shaped its spatially-resolved stellar populations.
Here, we detect NaI D in absorption in the central regions of the galaxy and
report a rotation pattern that follows the rotation of the stellar component.
We also detect ionised gas, especially in the $[\mathrm{NII}]\lambda 6584$ and
$[\mathrm{OIII}]\lambda 5007$ lines, whose velocity fields also seem to follow
the stellar velocity pattern although with a highly distorted morphology that
extends much further away to the NE than to the SW relative to the galaxy
nucleus. It is worth noting that in the areas where we detect H$\alpha$
emission their velocities seem to be much higher than in the other (forbidden)
emission lines detected.

NGC 7479: This strongly barred Seyfert 2 galaxy (Lumsden et al., 2001) shows
continuum emission in the nuclear regions that is severely affected by a dust
lane that crosses from N to S that is visible even in the MEGARA false-color
images (see also Fig.9). Such NS orientation also corresponds to the PA of the
bar at larger spatial scales. The stellar velocity field shows a clear
rotation pattern with its receding side having a PA of $-$135∘. The ionised-
gas emission is particularly bright in the emission lines located in the LR-R
setup, possibly due to high dust attenuations present in the region. Its
radial velocity maps indicate a complex behaviour, with a twist in the
rotation axis relative to that of the stellar velocity field and two regions
(along with the major axis of the stellar radial velocity field) with high
(gas) velocity dispersions ($\geq$100 km s-1).

NGC 7591: This LINER galaxy shows a stellar velocity field with a clear
rotation pattern. We detect ionised gas throughout the entire field of view of
MEGARA. This galaxy was not observed in the LR-B setup. The velocity field of
the ionised gas follows the stellar kinematics in almost all regions, although
it is distorted with a twist in the central region that might be explained by
the onset of several kinematic components along the line of sight. The large
velocity dispersion values found ($\geq$100 km s-1), both in recombination and
forbidden emission lines, also point out the need of using multiple kinematic
components in emission. We also detect neutral gas in the central regions of
the galaxy with a radial velocity field that is distinct from that of the
stars or the ionised gas.

NGC 7738: This is a strongly-barred (SB(rs)b) spiral galaxy with its bar
aligned in the NE to SW direction. Associated with this bar, a dust lane
crosses the MEGARA field of view in the same direction but slightly SE of the
galaxy nucleus. The stellar kinematics of the galaxy show a rotation pattern
that is anti-correlated with h3 and aligned with the photometric semi-major
axis of the galaxy. We detect neutral gas in the form of NaI D in absorption
in the brightest continuum regions. The velocity pattern of this intervening
neutral gas approximately follows stellar rotation, although the absorption
detected on top of the dust lane seems to be close to the galaxy systemic
velocity. We also measure ionised gas emission coming from H$\alpha$ and
$[\mathrm{NII}]\lambda 6584$ lines (LR-B observations were not acquired for
this object) whose velocity fields follow stellar rotation although with a
remarkably high velocity dispersion (almost 150 km s-1), possibly connected to
the effects of the bar and the dust lane associated with it. The velocity
field of the $[\mathrm{SII}]\lambda 6717$ line is affected by atmospheric
telluric absorptions.

NGC 7787: In this early-type barred spiral galaxy ((R’)SB(rs)0/a) the stellar
component follows a rotation pattern that is anti-correlated with h3. We
detect intervening neutral gas in the form of NaI D in absorption in the
central regions of the galaxy. Its kinematics approximately follow the
rotation pattern of the stars. We detect line emission arising from two
distinct regions at both sides of the nucleus, about 4 arcsec apart (or 2 kpc
at the distance NGC 7787). These regions might be part of a (nuclear; see Buta
& Crocker 1993) ring of 1 kpc in radius or, alternatively, indicate the
presence of star formation at the extremes of a nuclear bar. The fact that the
two regions appear closer in the emission line flux maps than in those showing
the equivalent widths indicate that the continuum emission drops faster than
the emission and that the emission is relatively extended in these regions.
The velocity field of the $[\mathrm{SII}]\lambda 6717$ line is affected by
atmospheric telluric absorptions.

##  8 Summary

MEGARA Galaxy Discs Evolution Survey (MEGADES) is the scientific legacy
project of MEGARA, the integral field and multi-object spectrograph installed
on the 10.4 m GTC telescope. With this project we aim to deepen our
understanding of the diverse secular processes that take place in galaxies and
their influence on the evolution of disc galaxies (e.g., stellar migration,
AGN feedback and starbursts). For this purpose, we will conduct several
studies on the characteristics and kinematics of the stellar populations that
form our galaxies and we will also study all possible aspects related to the
ionised and neutral gas (through NaI D absorption) present in the galaxies of
the sample. Being this a legacy project, we expect the scientific community to
be able to take advantage of all the data and results we make public in this
data release to ensure that this survey is as fruitful as possible and serves
beyond the scientific objectives for which it was conceived.

We currently count with high spatial (0.62 ″) and spectral resolution (R
$\sim$ 6000) observations made on the central regions of a total of 43
galaxies, 30 of these galaxies are a sub-sample of the S4G survey and the
remaining 13 were part of the CALIFA survey. The details of the sample are
described in $\S$2. All these observations have been made using the MEGARA
IFU. For the galaxies belonging to the S4G sample we have observations taken
with 3 different VPHs obtaining spectra in a spectral range between 4350.6
$\AA$ and 7287.7 $\AA$ at a resolution of R $\sim$ 6000\. For the galaxies
belonging to the CALIFA sample we have observations taken with 2 VPHs covering
a spectral range from 5165.6 $\AA$ to 7287.7 $\AA$ at the same resolution. All
the technical details of the instrument are presented in $\S$3.

We also carefully detail the whole process of data reduction and processing
from observation to conversion into data ready for scientific use in $\S$4. In
this same section we explain the post-processing performed on the reduced data
to improve the quality of its pointings and correct them. We also performed a
Voronoi binning on the observations to achieve a minimum signal-to-noise ratio
of 10.

In addition to the observations ready for scientific use, in this paper we
carry out the first studies on the data from the inner regions of the galaxies
in the MEGADES sample. We have performed an analysis of the stellar kinematics
in the galaxies of the entire sample using the pPXF software based on
observations made with the VPH LR-V (5165.6 $\AA$ \- 6176.2 $\AA$). We present
all maps of the kinematic components, i.e., radial velocity, velocity
dispersion, skewness, and kurtosis. We have also carried out measurements on
the emission lines of the ionised gas such as H$\beta$,
$[\mathrm{OIII}]\lambda 5007$, H$\alpha$, $[\mathrm{NII}]\lambda 6584$,
$[\mathrm{SII}]\lambda 6717$ and $[\mathrm{SII}]\lambda 6731$, and
measurements on the neutral gas using the sodium doublet lines NaI D. We also
present some of the features measured on these lines in maps. All these maps
have been gathered as individual galaxy cards in the Appendix to facilitate
their inspection by the reader.

All the observations together with the results of the analyses carried out in
this paper are available to the community on the MEGADES
website444https://www.megades.es. To access the public data, please log in
with the username ”public” and the password ”6BRLukU55E”.

###### Acknowledgements.

This publication is based on data obtained with the MEGARA instrument at the
Gran Telescopio CANARIAS. MEGARA has been built by a Consortium led by the
Universidad Complutense de Madrid (Spain) and that also includes the Instituto
de Astrofísica, Óptica y Electrónica (Mexico), Instituto de Astrofísica de
Andalucía (CSIC, Spain) and the Univesidad Politécnica de Madrid (Spain).
MEGARA is funded by the Consortium institutions and by GRANTECAN S.A. Based on
observations made with the Gran Telescopio Canarias (GTC), installed in the
Spanish Observatorio del Roque de los Muchachos of the Instituto de
Astrofísica de Canarias, in the island of La Palma. This work is based on data
obtained with MEGARA instrument, funded by European Regional Development Funds
(ERDF), through Programa Operativo Canarias FEDER 2014-2020. This work has
been supported by grant RTI2018-096188-B-I00 and AYA2016-75808-R funded by
program MCIN/AEI/10.13039/501100011033 of the Spanish Ministry of Science and
Innovation. This research has made use of the NASA/IPAC Extragalactic
Database, which is funded by the National Aeronautics and Space Administration
and operated by the California Institute of Technology. The Pan-STARRS1
Surveys (PS1) and the PS1 public science archive have been made possible
through contributions by the Institute for Astronomy, the University of
Hawaii, the Pan-STARRS Project Office, the Max-Planck Society and its
participating institutes, the Max Planck Institute for Astronomy, Heidelberg
and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns
Hopkins University, Durham University, the University of Edinburgh, the
Queen’s University Belfast, the Harvard-Smithsonian Center for Astrophysics,
the Las Cumbres Observatory Global Telescope Network Incorporated, the
National Central University of Taiwan, the Space Telescope Science Institute,
the National Aeronautics and Space Administration under Grant No. NNX08AR22G
issued through the Planetary Science Division of the NASA Science Mission
Directorate, the National Science Foundation Grant No. AST-1238877, the
University of Maryland, Eotvos Lorand University (ELTE), the Los Alamos
National Laboratory, and the Gordon and Betty Moore Foundation.

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##  Appendix A Additional material

Figure 9: MEGADES sample: RGB images from PanSTARRS observations (g, r and i
filters). The white box in the centre of each panel indicates MEGARA IFU FoV.

Figure 1: (cont.) MEGADES sample: RGB images from PanSTARRS observations (g, r
and i filters). The white box in the centre of each panel indicates MEGARA IFU
FoV. Table 6: Observing log.

| LR-B | LR-V | LR-R
---|---|---|---
Galaxy | Date | secz* | texp | Date | secz* | texp | Date | secz* | texp
IC 1683 | – | – | – | 19 Sep. 2018 | 1.2 | 5$\times$720 | 19 Sep. 2018 | 1.01 | 5$\times$720
NGC 0023 | – | – | – | 22 Sep. 2019 | 1.03 | 3$\times$1200 | 22 Sep. 2019 | 1.14 | 3$\times$1200
NGC 0600 | 27 Sep. 2019 | 1.24 | 3$\times$1200 | 27 Sep. 2019 | 1.34 | 3$\times$1200 | 27 Sep. 2019 | 1.48 | 3$\times$1200
NGC 0716 | – | – | – | 31 Dec. 2018 | 1.04 | 5$\times$720 | 31 Dec. 2018 | 1.06 | 5$\times$720
NGC 0718 | 12 Oct. 2018† | 1.45 | 3$\times$1200 | 02 Oct. 2018† | 1.1 | 3$\times$1200 | 02 Oct. 2018† | 1.13 | 3$\times$1200
NGC 1042 | 24 Oct. 2019 | 1.34 | 3$\times$1200 | 24 Oct. 2019 | 1.26 | 3$\times$1200 | 24 Oct. 2019 | 1.29 | 3$\times$1200
NGC 1087 | 06 Nov. 2018† | 1.17 | 3$\times$1200 | 30 Nov. 2018† | 1.21 | 3$\times$1200 | 06 Nov. 2018 | 1.36 | 3$\times$1200
NGC 2500 | 02 Dec. 2018† | 1.13 | 3$\times$1200 | 02 Dec. 2018† | 1.09 | 3$\times$600 | 02 Dec. 2018† | 1.08 | 3$\times$1200
NGC 2537 | 08 May 2019 | 1.28 | 3$\times$1200 | 04 Dec. 2019 | 1.31 | 3$\times$1200 | 03 Dec. 2018† | 1.05 | 3$\times$1200
NGC 2543 | – | – | – | 30 Nov. 2018 | 1.06 | 5$\times$720 | 30 Nov. 2018 | 1.01 | 5$\times$720
NGC 2552 | 01 Dec. 2019 | 1.15 | 3$\times$1200 | 01 Dec. 2019 | 1.08 | 3$\times$1200 | 01 Dec. 2019 | 1.07 | 3$\times$1200
NGC 2967 | 30 Jan. 2019† | 1.26 | 3$\times$1200 | 30 Jan. 2019† | 1.15 | 3$\times$1200 | 30 Jan. 2019† | 1.15 | 3$\times$1200
NGC 3104 | 02 Dec. 2019 | 1.11 | 3$\times$1200 | 02 Dec. 2019 | 1.26 | 3$\times$1200 | 02 Dec. 2019 | 1.33 | 3$\times$1200
NGC 3485 | 02 Jan. 2019† | 1.12 | 3$\times$1200 | 02 Jan. 2019† | 1.12 | 3$\times$1200 | 11 Jan. 2019† | 1.12 | 3$\times$1200
NGC 3507 | 03 Jan. 2019† | 1.02 | 3$\times$1200 | 03 Jan. 2019† | 1.04 | 3$\times$1200 | 03 Jan. 2019† | 1.1 | 3$\times$1200
NGC 3780 | 31 Dec. 2019 | 1.29 | 3$\times$1200 | 31 Dec. 2019 | 1.18 | 3$\times$1200 | 01 Feb. 2019† | 1.24 | 3$\times$1200
NGC 3893 | 12 Jan. 2019† | 1.06 | 3$\times$1200 | 11 Jan. 2019† | 1.06 | 3$\times$1200 | 11 Jan. 2019† | 1.08 | 3$\times$1200
NGC 3982 | 20 Mar. 2021 | 1.14 | 3$\times$1200 | 19 Mar. 2021 | 1.21 | 3$\times$1200 | 19 Mar. 2021 | 1.25 | 3$\times$1200
NGC 3998 | 15 Mar. 2021† | 1.24 | 3$\times$1200 | 15 Mar. 2021 | 1.4 | 3$\times$1200 | – | – | –
NGC 4037 | 14 Mar. 2021† | 1.05 | 3$\times$1200 | 14 Mar. 2021 | 1.13 | 3$\times$1200 | 15 Mar. 2021 | 1.07 | 3$\times$1200
NGC 4041 | 14 Jan. 2019† | 1.39 | 3$\times$1200 | 14 Jan. 2019† | 1.26 | 3$\times$1200 | 01 Feb. 2019† | 1.22 | 3$\times$1200
NGC 4189 | 07 May 2019 | 1.07 | 3$\times$1200 | 08 May 2019 | 1.18 | 3$\times$1200 | 08 May 2019 | 1.43 | 3$\times$1200
NGC 4278 | 08 May 2019 | 1.01 | 3$\times$1200 | 09 May 2019 | 1.07 | 3$\times$1200 | – | – | –
NGC 4593 | 08 Feb. 2019 | 1.22 | 3$\times$1200 | 06 May 2019 | 1.32 | 3$\times$1200 | 08 Feb. 2019 | 1.25 | 3$\times$1200
NGC 4750 | 09 Feb. 2019 | 1.4 | 3$\times$1200 | 07 May 2019 | 1.4 | 3$\times$1200 | 07 May 2019 | 1.55 | 3$\times$1200
NGC 5218 | – | – | – | 19 Mar. 2021 | 1.21 | 3$\times$1200 | 19 Mar. 2021 | 1.24 | 3$\times$1200
NGC 5394 | – | – | – | 09 May 2019 | 1.14 | 3$\times$1200 | 09 May 2019 | 1.44 | 3$\times$1200
NGC 5616 | – | – | – | 08 May 2019 | 1.49 | 3$\times$1200 | 02 Aug. 2019 | 1.45 | 3$\times$1200
NGC 5953 | – | – | – | 19 Mar. 2021 | 1.03 | 3$\times$1200 | 19 Mar. 2021 | 1.04 | 3$\times$1200
NGC 5957 | 25 May 2019 | 1.07 | 3$\times$1200 | 29 May 2019 | 1.16 | 3$\times$1200 | 29 May 2019 | 1.67 | 3$\times$1200
NGC 5963 | 04 May 2021 | 1.17 | 3$\times$1200 | 10 Jun. 2019 | 1.29 | 3$\times$1200 | 10 Jun. 2019 | 1.21 | 3$\times$1200
NGC 6027 | – | – | – | 06 May 2019 | 1.07 | 3$\times$1200 | 06 May 2019 | 1.21 | 3$\times$1200
NGC 6140 | 27 May 2019† | 1.27 | 3$\times$1200 | 29 May 2019 | 1.26 | 3$\times$1200 | 30 May 2019 | 1.27 | 3$\times$1200
NGC 6217 | 20 Aug. 2018 | 1.6 | 3$\times$1200 | 20 Aug. 2018 | 1.7 | 3$\times$1200 | 21 Aug. 2018† | 1.84 | 3$\times$1200
NGC 6339 | 10 Jul. 2019 | 1.02 | 3$\times$1200 | 11 Jul. 2019 | 1.07 | 3$\times$1200 | 11 Jul. 2019 | 1.31 | 3$\times$1200
NGC 6412 | 30 May 2019 | 1.48 | 3$\times$1200 | 30 May 2019 | 1.53 | 3$\times$1200 | 11 Jun. 2019 | 1.6 | 3$\times$1200
NGC 7025 | 01 Aug. 2017 | 1.09 | 3$\times$900 | 01 Aug. 2017 | 1.23 | 3$\times$900 | 01 Aug. 2017 | 1.37 | 3$\times$600
NGC 7437 | 28 Jul. 2019 | 1.2 | 3$\times$1200 | 28 Jul. 2019 | 1.03 | 3$\times$1200 | 27 Dec. 2019 | 1.25 | 3$\times$1200
NGC 7479 | 30 Jul. 2019 | 1.24 | 3$\times$1200 | 30 Jul. 2019 | 1.09 | 3$\times$1200 | 30 Jul. 2019 | 1.08 | 3$\times$1200
NGC 7591 | – | – | – | 29 Sep. 2018 | 1.48 | 5$\times$720 | 29 Sep. 2018 | 1.2 | 5$\times$720
NGC 7738 | – | – | – | 23 Sep. 2019 | 1.18 | 3$\times$1200 | 23 Oct. 2019 | 1.13 | 3$\times$1000
NGC 7787 | – | – | – | 31 Jul. 2019 | 1.2 | 3$\times$1200 | 31 Jul. 2019 | 1.13 | 3$\times$1200
PGC 066559 | 11 Jul. 2019 | 1.25 | 3$\times$1200 | 11 Jul. 2019 | 1.26 | 3$\times$1200 | 01 Aug. 2019 | 1.24 | 3$\times$1200

†Observations affected by diffuse light. *Airmass measured at the beginning of
the observation.

Table 7: Observing nights. Date | Seeing (”) | Atm. Conditions
---|---|---
01 Aug. 2017 | 0.7 | –
02 Aug. 2017 | 1.1 | –
20 Aug. 2018 | 0.6 - 0.7 | Clear
21 Aug. 2018 | 0.8 | Clear
19 Sep. 2018 | 0.9 | Clear
29 Sep. 2018 | 0.8 | Clear
02 Oct. 2018 | 0.7 | Clear
12 Oct. 2018 | 0.9 | Clear
06 Nov. 2018 | 1.0 | Clear
30 Nov. 2018 | 0.8 | Clear
02 Dec. 2018 | 1.0 - 1.1 | Clear
03 Dec. 2018 | 0.8 | Clear
31 Dec. 2018 | 0.8 - 1.0 | Clear
02 Jan. 2019 | 0.9 | Clear
03 Jan. 2019 | 0.8 | Clear
11 Jan. 2019 | 0.9 | Clear
12 Jan. 2019 | 0.9 | Clear
14 Jan. 2019 | 1.1 - 1.2 | Clear
30 Jan. 2019 | 1.2 | Clear
01 Feb. 2019 | 1.2 | Clear
08 Feb. 2019 | 0.7 | Photometric
09 Feb. 2019 | 0.8 | Photometric
06 May 2019 | 0.7 - 0.8 | Photometric
07 May 2019 | 0.6 - 0.8 | Photometric
08 May 2019 | 0.6 - 0.8 | Photometric
09 May 2019 | 0.5 - 0.6 | Photometric
25 May 2019 | 0.8 | Clear
27 May 2019 | 1.0 | Clear
29 May 2019 | 1.0 - 1.1 | Clear
30 May 2019 | 0.9 | Photometric
10 Jun. 2019 | 1.1 - 1.2 | Clear
11 Jun. 2019 | 1.0 | Clear
10 Jul. 2019 | 1.3 | Clear
11 Jul. 2019 | 1.0 - 1.2 | Clear
28 Jul. 2019 | 1.0 | Clear
30 Jul. 2019 | 1.0 | Photometric
31 Jul. 2019 | 0.9 | Photometric
01 Aug. 2019 | 1.0 | Photometric
02 Aug. 2019 | 0.8 | Photometric
22 Sep. 2019 | 0.6 | Clear
23 Sep. 2019 | 1.1 | Clear
27 Sep. 2019 | 0.9 - 1.0 | Photometric
23 Oct. 2019 | 1.2 | Clear
24 Oct. 2019 | 1.1 - 1.2 | Clear
01 Dec. 2019 | 0.6 - 1.1 | Clear
02 Dec. 2019 | 0.6 - 0.7 | Clear
04 Dec. 2019 | 0.9 | Clear
27 Dec. 2019 | 0.9 | Clear
31 Dec. 2019 | 1.1 - 1.2 | Spectroscopic
26 Jan. 2020 | 1.2 | Clear
14 Mar. 2021 | 1.0 | Clear
15 Mar. 2021 | 1.0 | Clear
19 Mar. 2021 | 0.9 - 1.1 | Clear
20 Mar. 2021 | 1.0 | Clear
04 May 2021 | 1.1 | Clear

##  Appendix B Galaxy cards

Figure 2: IC 1683 card.

Figure 2: (cont.) IC 1683 card.

Figure 3: NGC 0023 card.

Figure 3: (cont.) NGC 0023 card.

Figure 4: NGC 0600 card.

Figure 4: (cont.) NGC 0600 card.

Figure 5: NGC 0716 card.

Figure 5: (cont.) NGC 0716 card.

Figure 6: NGC 0718 card.

Figure 6: (cont.) NGC 0718 card.

Figure 7: NGC 1042 card.

Figure 7: (cont.) NGC 1042 card.

Figure 8: NGC 1087 card.

Figure 8: (cont.) NGC 1087 card.

Figure 9: NGC 2500 card.

Figure 9: (cont.) NGC 2500 card.

Figure 10: NGC 2537 card.

Figure 10: (cont.) NGC 2537 card.

Figure 11: NGC 2543 card.

Figure 11: (cont.) NGC 2543 card.

Figure 12: NGC 2552 card.

Figure 12: (cont.) NGC 2552 card.

Figure 13: NGC 2967 card.

Figure 13: (cont.) NGC 2967 card.

Figure 14: NGC 3104 card.

Figure 14: (cont.) NGC 3104 card.

Figure 15: NGC 3485 card.

Figure 15: (cont.) NGC 3485 card.

Figure 16: NGC 3507 card.

Figure 16: (cont.) NGC 3507 card.

Figure 17: NGC 3780 card.

Figure 17: (cont.) NGC 3780 card.

Figure 18: NGC 3893 card.

Figure 18: (cont.) NGC 3893 card.

Figure 19: NGC 3982 card.

Figure 19: (cont.) NGC 3982 card.

Figure 20: NGC 3998 card.

Figure 20: (cont.) NGC 3998 card.

Figure 21: NGC 4037 card.

Figure 21: (cont.) NGC 4037 card.

Figure 22: NGC 4041 card.

Figure 22: (cont.) NGC 4041 card.

Figure 23: NGC 4189 card.

Figure 23: (cont.) NGC 4189 card.

Figure 24: NGC 4278 card.

Figure 24: (cont.) NGC 4278 card.

Figure 25: NGC 4593 card.

Figure 25: (cont.) NGC 4593 card.

Figure 26: NGC 4750 card.

Figure 26: (cont.) NGC 4750 card.

Figure 27: NGC 5218 card.

Figure 27: (cont.) NGC 5218 card.

Figure 28: NGC 5394 card.

Figure 28: (cont.) NGC 5394 card.

Figure 29: NGC 5616 card.

Figure 29: (cont.) NGC 5616 card.

Figure 30: NGC 5953 card.

Figure 30: (cont.) NGC 5953 card.

Figure 31: NGC 5957 card.

Figure 31: (cont.) NGC 5957 card.

Figure 32: NGC 5963 card.

Figure 32: (cont.) NGC 5963 card.

Figure 33: NGC 6027 card.

Figure 33: (cont.) NGC 6027 card.

Figure 34: NGC 6140 card.

Figure 34: (cont.) NGC 6140 card.

Figure 35: NGC 6217 card.

Figure 35: (cont.) NGC 6217 card.

Figure 36: NGC 6339 card.

Figure 36: (cont.) NGC 6339 card.

Figure 37: NGC 6412 card.

Figure 37: (cont.) NGC 6412 card.

Figure 38: NGC 7025 card.

Figure 38: (cont.) NGC 7025 card.

Figure 39: NGC 7437 card.

Figure 39: (cont.) NGC 7437 card.

Figure 40: NGC 7479 card.

Figure 40: (cont.) NGC 7479 card.

Figure 41: NGC 7591 card.

Figure 41: (cont.) NGC 7591 card.

Figure 42: NGC 7738 card.

Figure 42: (cont.) NGC 7738 card. The velocity field of the
$[\mathrm{SII}]\lambda 6717$ line (subfigure $\eta$) is affected by
atmospheric telluric absorptions.

Figure 43: NGC 7787 card.

Figure 43: (cont.) NGC 7787 card.The velocity field of the
$[\mathrm{SII}]\lambda 6717$ line (subfigure $\eta$) is affected by
atmospheric telluric absorptions.

Figure 44: PGC 066559 card.

Figure 44: (cont.) PGC 066559 card.