11institutetext: Department of Astrophysics, University of Vienna,
Türkenschanzstrasse 17, A-1180 Vienna
11email<EMAIL_ADDRESS>22institutetext: European Southern
Observatory, Karl-Schwarzschild-Strasse 2, D-85748 Garching, Germany
33institutetext: Observatorio Astronómico Nacional (IGN), Alfonso XII 3,
E-28014 Madrid, Spain 44institutetext: Institute of Astronomy, Department of
Physics, National Tsing Hua University, Hsinchu, Taiwan 55institutetext:
University of Hertfordshire, Centre for Astrophysics Research, College Lane,
Hatfield AL10 9AB, UK 66institutetext: Instituto de Física Fundamental (CSIC).
c/ Serrano 121-123, 28006, Madrid, Spain 77institutetext: IRAM, 300 rue de la
Piscine, 38406 Saint Martin d’Hères, France 88institutetext: LERMA,
Observatoire de Paris, PSL Research University, CNRS, Sornonne Universités,
75014 Paris, France 99institutetext: Universitäts-Sternwarte, Ludwig-
Maximilians-Universität München, Scheinerstrasse 1, D-81679 Munich, Germany
1010institutetext: Excellence Cluster ORIGINS, Boltzmannstrasse 2, D-85748
Garching, Germany 1111institutetext: Max-Planck Institute for Extraterrestrial
Physics, Giessenbachstrasse 1, D-85748 Garching, Germany 1212institutetext:
South-Western Institute for Astronomy Research, Yunnan University, Kunming,
650500 Yunnan, P.R. China

# Emergence of high-mass stars in complex fiber networks (EMERGE)

I. Early ALMA Survey: observations and massive data reduction

A. Hacar 11 A. Socci 11 F. Bonanomi 11 D. Petry 22 M. Tafalla 33 D. Harsono 44
J. Forbrich 55 J. Alves 11 J. Grossschedl 11 J. R. Goicoechea 66 J. Pety 7788
A. Burkert 9910101111 G.X. Li 1212

(Received XXX ; accepted 06/03/2024)

###### Abstract

Context. Recent molecular surveys have revealed a rich gas organization of
sonic-like filaments at small-scales (aka fibers) in all kind of environments
prior to the formation of low- and high-mass stars. These fibers form at the
end of the turbulent cascade and are identified as the fine sub-structure
within the hierarchical nature of the gas in the Interstellar Medium (ISM).

Aims. Isolated fibers provide the sub-sonic conditions for the formation of
low-mass stars. This paper introduces the EMERGE project aiming to investigate
whether complex fiber arrangements (networks) could also explain the origin of
high-mass stars and clusters.

Methods. We analyzed the EMERGE Early ALMA Survey including 7 star-forming
regions in Orion (OMC-1/2/3/4 South, LDN 1641N, NGC 2023, and Flame Nebula)
homogeneously surveyed in both molecular lines (N2H+ J=1-0, HNC J=1-0, plus
HC3N J=10-9) and 3mm-continuum using a combination of interferometric ALMA
mosaics and IRAM-30m single-dish (SD) maps together with a series of Herschel,
Spitzer, and WISE archival data. We also developed a systematic data reduction
framework allowing the massive data processing of ALMA observations.

Results. We obtained independent continuum maps and spectral cubes for all our
targets and molecular lines at different (SD and interferometric) resolutions
and exploring multiple data combination techniques. Based on our low-
resolution (SD) observations (30″or $\sim$ 12 000 au), we describe the global
properties of our sample covering a wide range of physical conditions
including low- (OMC-4 South, NGC 2023), intermediate (OMC-2, OMC-3, LDN
1641N), and high-mass (OMC-1, Flame Nebula) star-forming regions in different
evolutionary stages. The comparison between our SD maps and ancillary YSO
catalogs denotes N2H+ (1-0) as the best proxy for the dense, star-forming gas
in our targets showing a constant star formation efficiency and a fast time
evolution of $\lesssim$ 1 Myr. While apparently clumpy and filamentary in our
SD data, all targets show a much more complex fibrous sub-structure at the
enhanced resolution of our combined ALMA+IRAM-30m maps
(4$\aas@@fstack{\prime\prime}$5 or $\sim$ 2 000 au). A large number of
filamentary features at sub-parsec scales are clearly recognized in the high-
density gas ($\gtrsim 10^{5}$ cm-3) traced by N2H+ (1-0) directly connected to
the formation of individual protostars. Surprisingly, this complex gas
organization appears to extend further into the more diffuse gas ($\sim
10^{3}-10^{4}$ cm-3) traced by HNC (1-0).

Conclusions.  This paper presents the EMERGE Early ALMA survey including a
first data release of continuum maps and spectral products for this project to
be analysed in future papers of this series. A first look at these results
illustrates the need of advanced data combination techniques between high-
resolution interferometric (ALMA) and high-sensitivity, low-resolution single-
dish (IRAM-30m) datasets to investigate the intrinsic multi-scale, gas
structure of the ISM.

###### Key Words.:

ISM: clouds – ISM: kinematics and dynamics – ISM: structure – stars: formation
– submillimeter: ISM

##  1 Introduction

A classical observational dichotomy separates the origin of solar-like stars
from their high-mass counterparts. Low-mass stars (few M⊙; e.g., HL Tau) can
be found in isolation or small groups in quiescent, low-mass environments such
as Taurus. In contrast, high-mass stars ($>$10 M⊙; e.g., $\theta^{1}$ Ori C in
the ONC) are typically found within clusters (which also form low-mass stars)
and under the influence of intense stellar feedback (i.e. UV radiation, winds,
outflows) in giant molecular clouds such as Orion (e.g., Pabst et al., 2019).
These findings led to the proposal of independent formation scenarios for low-
(Shu et al., 1987) and high-mass (Bonnell et al., 2001; McKee & Tan, 2003)
stars. Based on similar (spherical) initial conditions (either a low-mass,
dense core or a massive clump, respectively) these models have dominated the
star-formation field during the last decades.

A series of observational studies have highlighted a direct link between the
filamentary structure of the ISM and the initial conditions for star-formation
(see André et al., 2014; Hacar et al., 2022; Pineda et al., 2023, for recent
reviews). According to large-scale Herschel FIR dust continuum emission
surveys, most dense cores and young stellar objects (YSO) are preferentially
formed in association to dense filaments in both nearby (André et al., 2010)
as well as in more distant Galactic Plane (Molinari et al., 2010) molecular
clouds. Low-mass cores are found along parsec-scale filaments (Hartmann et
al., 2001) and likely form out of their internal gravitational fragmentation
(Schneider & Elmegreen, 1979; Inutsuka & Miyama, 1997). On the other hand,
most of the dense clumps harbouring high-mass stars and clusters are found at
the junction of massive filaments, forming the so-called Hub-Filament Systems
(HFS; Myers, 2009; Kumar et al., 2020). Filaments provide a preferential
orientation with additional mass reservoir and could funnel large amounts of
material towards cores and clumps (e.g., Peretto et al., 2013). Yet, the
connection between the previous star formation models and the new filamentary
conditions of the ISM remains under debate, particularly in the case of high-
mass stars (e.g., Motte et al., 2018).

The analysis of the gas kinematics of many the above filamentary clouds has
revealed a high level of gas organization prior to the formation of stars.
Single-dish (low spatial resolution) observations demonstrate how many of the
parsec-size filaments detected by Herschel in nearby low-mass clouds (e.g.,
B213; Palmeirim et al., 2013) are actually collections of velocity-coherent
filaments at smaller scales identified by their internal velocity dispersion
close to the sound speed, usually referred to as fibers (Hacar et al., 2013;
André et al., 2014; Pineda et al., 2023). Sharing similar kinematic properties
although smaller in size, further ALMA (high spatial resolution) observations
resolved a similar fiber structure at sub-parsec scales in massive star-
forming regions (Hacar et al., 2018). Since their discovery, filamentary
structures of different lengths (observed to have between $\sim$0.1 and
$\sim$7 pc) but similar sonic internal velocity dispersion than those
identified in fibers have been systematically identified in all types of low-
mass clouds (Arzoumanian et al., 2013; Fehér et al., 2016; Kainulainen et al.,
2016; Hacar et al., 2016), clusters (Fernández-López et al., 2014; Hacar et
al., 2017b), high-mass star-forming regions (Treviño-Morales et al., 2019; Cao
et al., 2021), and Infrared Dark Clouds (IRDCs; Henshaw et al., 2014; Chen et
al., 2019), to cite a few examples. While first identified from the gas
velocity of low-density tracers and potentially affected by line-of-sight
confusion (see Zamora-Avilés et al., 2017; Clarke et al., 2018), the
subsequent detection of fibers in high density tracers such as N2H+ (e.g.,
Chen et al., 2019; Shimajiri et al., 2019), NH3 (Monsch et al., 2018; Sokolov
et al., 2019), H13CO+ and NH2D+ (e.g., Cao et al., 2021) guarantees a good
correspondence with the true dense gas structure within clouds in large
statistical samples. Systematically produced in hydro- and turbulent
simulations (Smith et al., 2014; Moeckel & Burkert, 2015; Clarke et al., 2017;
Li & Klein, 2019), these “filaments-within-filaments” appear as the first sub-
sonic structures created at the end of the turbulent cascade as part of
intrinsic hierarchical structure of gas in the ISM (see Hacar et al., 2022,
for a discussion).

Fibers could potentially connect the formation of both low- and high-mass
stars (see Hacar et al., 2018, for a discussion). Individual fibers promote
the formation of small chains of low-mass, Jeans-like cores via quasi-static
fragmentation (Schneider & Elmegreen, 1979; Hacar & Tafalla, 2011; Tafalla &
Hacar, 2015) which associated in large groups could become self-gravitating
and agglomerate a high number of cores and stars (Hacar et al., 2017a, b). In
parallel, interactions between fibers could also form super-Jeans cores via
merging or collision (Clarke et al., 2017; Hoemann et al., 2021) while, at the
same time, provide large mass reservoirs favouring the enhanced accretion
rates needed to form high-mass stars (Bernasconi & Maeder, 1996; Behrend &
Maeder, 2001). Massive fiber arrangements could therefore simultaneously
generate stellar clusters and high-mass stars (Hacar et al., 2018). A similar
scenario has been proposed from the merging of parsec-scale filaments (Kumar
et al., 2020, 2022). Given these promising results, this new fiber scenario
for star formation deserves further exploration.

This paper introduces the Emergence of high-mass stars in complex fiber
networks (EMERGE) project111EMERGE Project website:
https://emerge.univie.ac.at/. As working hypothesis, EMERGE aims to
investigate whether both high-mass stars and clusters could be created as
emergent phenomena in densely populated fiber systems. Rather than by distinct
processes, EMERGE will explore whether the formation of low- and high-mass
stars together with clusters arises as a natural consequence of the global
properties of these fiber systems showing a continuum spectrum of masses as
function of the network density.

EMERGE will systematically analyze the substructure, internal interactions,
and dynamical evolution of these filamentary systems across the Milky Way.
Typically located at kpc distances, resolution and sensitivity constraints
have so far limited the analysis of these massive regions to individual
targets or small samples. EMERGE will survey a large, homogeneous ALMA sample
of massive fiber networks extracted from its public archive using a novel
combination of data mining techniques (Ahmadi & Hacar, 2023) and massive data
processing (van Terwisga et al., 2022). In this first work (hereafter Paper I)
we present the EMERGE Early ALMA Survey (see Sect.2) that includes 7 star-
forming regions in Orion systematically analyzed using a combination of high
spatial resolution resolution ALMA (interferometric) mosaics plus large-scale
IRAM-30m (single-dish) observations at 3 mm (N2H+, HNC, HC3N, HCN, and 3mm-
continuum). Designed as a proof of concept of this project, Paper I introduces
a standard set of procedures for data combination and massive data reduction.
In a series of accompanying papers we will investigate the effects of
interferometric filtering on ISM observations carried out with ALMA (Bonanomi
et al., 2024, Paper II) as well as present the analysis of the physical
properties of dense fibers identified in this first EMERGE sample (Socci et
al., 2024, Paper III). Future EMERGE papers will extend this analysis to other
regions.

Figure 1: EMERGE Early Science survey in Orion A (central panel), including
zoom-ins of the OMC-1, OMC-2, OMC-3, OMC-4 South, and LDN 1641N regions (see
subpanels), shown over the total gas column density derived from Herschel
observations (background image at 36 arcsec resolution; Lombardi et al.,
2014). The footprints of our ALMA mosaics (white lines) as well as our
IRAM-30m observations (colored lines; see color-code in Table 3) are indicated
in all panels together with the positions of O (yellow stars) and B stars
(white stars) at the distance of Orion. The positions of young protostars
(cyan triangles) and disk stars (black dots) (Megeath et al., 2012) and the
corresponding Herschel beamsize and 0.1 pc scale bar are marked in the
individual zoom-ins.  Figure 2: Similar to Fig.1 but for the case of Orion B
with zoom-ins of NGC 2023 and Flame Nebula (NGC 2024).  Table 1: EMERGE Early
ALMA Survey: Targets

Target | Region | D(1) | Mtot(2) |  $\mathrm{M}_{dense}$(3) | $f_{dense}^{(4)}$ |  $\left<\mathrm{T}_{\mathrm{K}}\right>$(5) | O-star? | P(6) | P+D(6) | P/D
---|---|---|---|---|---|---|---|---|---|---
|  | (pc) | (M⊙) | (M⊙) |  | (K) |  |  |  |
OMC-1 | Orion A | 400 | 1032⋆ | 582.2 | 0.56 | 33.7 | Yes | 36 | 423 | 0.09
OMC-2 | Orion A | 400 | 581.0 | 372.8 | 0.64 | 26.3 | No | 33 | 142 | 0.30
OMC-3 | Orion A | 400 | 643.2 | 351.1 | 0.55 | 25.4 | No | 26 | 102 | 0.34
OMC-4 South | Orion A | 400 | 469.4 | 73.6 | 0.16 | 20.3 | ? | 11 | 59 | 0.23
LDN 1641N | Orion A | 400 | 478.6 | 174.3 | 0.36 | 25.4 | No | 13 | 51 | 0.34
NGC 2023 | Orion B | 423 | 330.6 | 70.7 | 0.21 | 21.9 | No | 6 | 8 | 0.75
Flame Nebula | Orion B | 423 | 287.8⋆ | 94.1 | 0.33 | 28.8 | Yes | 21 | 141 | 0.18

222Physical parameters derived from low-resolution (Herschel and/or IRAM-30m)
maps within an area of 1.5$\times$1.5 pc2 ($\sim$ 700$\times$700 arcsec2)
around the phase center of our ALMA maps (see coordinates in Table 2): (1)
Adopted cloud distances; (2) Total mass, M(total), derived from Herschel
column density maps (Lombardi et al., 2014); (3) Total dense gas mass,
M(dense), derived from N2H+(1-0); (4) Fraction of dense gas (i.e.
$f=\mathrm{M}_{tot}/\mathrm{M}_{dense}$; (5) Mean gas kinetic temperature,
$\left<\mathrm{T}_{\mathrm{K}}\right>$; (6) Number of Protostars (P) and Disk
stars (D) identified by Spitzer (Megeath et al., 2012) (see also Fig. 7).
($\star$) Note how the mass contribution in stars could significantly increase
the mass load of high-mass, clustered regions such as OMC-1 and Flame Nebula.

##  2 The EMERGE Early ALMA Survey

With our EMERGE Early ALMA Survey we aim to investigate the internal gas
structure and physical conditions for star-formation in different environments
of Orion (Menten et al., 2007; Großschedl et al., 2018, D$\sim$400 pc; [; see
also Sect. 5). This first survey sampled a total of 7 prototypical star-
forming regions: OMC-1, OMC-2, OMC-3, OMC-4 South, and LDN 1641N in Orion A,
and NGC 2023 and the Flame Nebula (or NGC 2024) in Orion B (see Table 1). We
designed our ALMA survey to cover both low- and high-mass star-forming regions
showing different levels of stellar activity, mass, and evolution. We show the
location, column density maps, and stellar populations in these regions in
Figures 1 and 2.

Previous stellar surveys have classified these regions as function of their
clustering properties and star-formation activity (see Peterson & Megeath,
2008; Bally, 2008). OMC-1 and Flame Nebula are identified as the two most
massive embedded clusters in Orion showing largest peak stellar densities in
this complex ($>$ 1500 stars pc-2) including several high-mass stars. Although
less concentrated, OMC-2, OMC-3, and LDN 1641 N contain large embedded
populations of YSOs forming smaller groups. In contrast, OMC-4 South and NGC
2023 only form a hand full of low-mass sources mainly in isolation (see
Megeath et al., 2016, for a full comparison).

Our source selection also covers a wide range of environments. OMC-1 and Flame
Nebula are exposed exposed to intense EUV ionizing radiation (producing HII
regions) and to strong FUV dissociating radiation (creating neutral Photo
Dissociated Regions, PDRs) produced by the O-type $\theta^{1}$ Ori C (O6Vp)
and NGC 2024 IRS-2b (O8V) stars, respectively (see Bally, 2008). Although less
severe, the stellar winds and UV radiation of nearby clusters and high-mass
stars seem to influence the OMC-3 (NGC 1977 and 42 Orionis; Peterson &
Megeath, 2008), OMC-4 South (NGC 1980 and $\iota$ Ori; Alves & Bouy, 2012),
and NGC 2023 ($\sigma$ Ori; Brown et al., 1994) regions as denoted in previous
velocity-resolved observations of the FIR [CII] 158$\mu$m fine-structure line
(see Pabst et al., 2017, 2020). Despite active in star formation as denoted by
a large population of young outflows (e.g., Stanke & Williams, 2007; Sato et
al., 2023), OMC-2 and LDN 1641N appear as more pristine regions and therefore
become good candidates to also investigate unperturbed gas.

Most of our targets have been systematically surveyed at long wavelengths,
particularly at low spatial resolution ($\theta\gtrsim$15”). The Orion complex
has been mapped using large-scale FIR (36”; Lombardi et al., 2014) plus (sub-)
millimeter (sub-mm) (14”; Stanke et al., 2022) continuum surveys as well as in
some CO transitions (11”-14”; Berné et al., 2010; Goicoechea et al., 2020;
Stanke et al., 2022). In the Orion A cloud, the OMC clouds (OMC-1/2/3/4 South;
see Fig.1) are part of the famous Integral Shaped Filament (ISF; Bally et al.,
1987), the most massive filamentary cloud in the solar neighbourhood, and the
one containing the most massive cluster within the local kpc, the Orion Nebula
Cluster (see Peterson & Megeath, 2008, for a review). The ISF has been mapped
at sub-mm wavelengths in continuum (14”; Johnstone & Bally, 1999), by
molecular surveys ($\sim$30”; Kauffmann et al., 2017; Brinkmann et al., 2020)
and dedicated observations of both diffuse (Shimajiri et al., 2014; Stanke et
al., 2022) and dense molecular tracers ($\sim$30”; Tatematsu et al., 2008;
Hacar et al., 2017a), to cite few examples (for more information see Peterson
& Megeath, 2008; Hacar et al., 2018, and references therein). Away from this
main star-forming site, LDN 1641N has received less attention, although it has
been covered in some large-scale surveys in Orion A ($\gtrsim$15”; e.g.,
Nishimura et al., 2015; Mairs et al., 2016). NGC 2023 and Flame Nebula have
been observed as part of cloud-size studies in Orion B (see Fig.2) both in
single-line observations ($\geq$19”; Lada et al., 1991; Stanke et al., 2022)
and unbiased molecular surveys ($\gtrsim$22”; Pety et al., 2017; Orkisz et
al., 2019; Santa-Maria et al., 2023).

The clouds in our sample have been classified as filamentary in previous low
spatial resolution studies (e.g., Johnstone & Bally, 1999; Orkisz et al.,
2019; Könyves et al., 2020; Gaudel et al., 2023). Nonetheless, a close
inspection of Figs.1 and 2 reveals a variety of cloud morphology. Regions such
as OMC-2, OMC-3, and Flame Nebula appear elongated, following the large-scale
filamentary structure of their parental cloud (Johnstone & Bally, 1999). The
OMC-1 region is identified as a Hub-Filament System (HFS) structure
characteristic of massive clumps (Rodriguez-Franco et al., 1992). On the other
hand, OMC-4 South and NGC 2023 show more irregular and diffuse organization,
while LDN 1641N exhibits a cometary shape (Mairs et al., 2016; Kirk et al.,
2016).

Compared to the large number of low spatial resolution observations, the study
of these targets at higher spatial resolution ($\theta\leq$10”) is more
limited. Only those targets in Orion A (OMC-1/2/3/4 South, and LDN 1641N) have
been systematically observed using large-scale CO maps ($\sim$7”; Kong et al.,
2018; Suri et al., 2019) with no similar counterpart study of Orion B. Local
surveys mainly focused the northern part of the ISF region (OMC-1/2/3) mapped
in FIR ($\sim$8”; Schuller et al., 2021) or millimeter
($\sim$4$\aas@@fstack{\prime\prime}$5; Takahashi et al., 2013) continuum. On
the other hand interferometric observations of high density tracers ($>10^{4}$
cm-3) have been restricted to regions of specific interest ($\sim$4”; e.g.,
OMC-1 by Wiseman & Ho, 1998; Hacar et al., 2018), sections of these regions
($\sim$3-5”; e.g., parts of OMC-2 by Kainulainen et al. (2017) or Flame Nebula
by Shimajiri et al. (2023)), and/or peculiar targets within these fields
($\sim$1-2”; e.g., the head of LDN 1641N by Stanke & Williams (2007), OMC-2
FIR-4 by Chahine et al. (2022)).

We surveyed our EMERGE Early ALMA sample with a homogeneous set of high
spatial resolution, high sensitivity ALMA observations
(4$\aas@@fstack{\prime\prime}$5) in combination with additional large-scale,
low spatial resolution, single-dish (30”) maps (Sect.3) for which we also
created a new data reduction framework (Sect.4). We investigated the gas
properties in these regions (star-formation, column density, temperature, etc)
using a dedicated suite of molecular tracers (N2H+, HNC, and HC3N) and
continuum maps (Sect. 5). By selecting targets within the same star-forming
complex we carry our a direct comparison of these regions at similar
resolution while avoiding additional variations depending on their local
galactic environment. As primary goal of our study, we aim to systematically
investigate the physical conditions in both low- and high-mass star-forming
regions from pc-scales down to 2000 au resolutions (see also Papers II and
III).

##  3 Observations

Table 2: EMERGE Early ALMA Survey: ALMA Fields

Field | Phase center | Map size | No. of | Spectral Windows | Proj. ID
---|---|---|---|---|---
|  $\alpha$(J2000) |  $\delta$(J2000) | (arcsec2) | pointings |  |
OMC-1# | 05h35m27s.2 | -05∘09′42$"$ | $220"\leavevmode\nobreak\ \times\leavevmode\nobreak\ 600"$ | 148 | N2H+, 3mm-Cont. | 2015.1.00669.S
OMC-2# | 05h35m14s.2 | -05∘22′21$"$ | $220"\leavevmode\nobreak\ \times\leavevmode\nobreak\ 600"$ | 148 | N2H+, 3mm-Cont. | 2015.1.00669.S
OMC-3 | 05h35m22s.5 | -05∘02′00$"$ | $210"\leavevmode\nobreak\ \times\leavevmode\nobreak\ 500"$ | 124 | N2H+, HNC, HC3N, 3mm-Cont. | 2019.1.00641.S
OMC-4 South | 05h35m07s.5 | -05∘55′00$"$ | $230"\leavevmode\nobreak\ \times\leavevmode\nobreak\ 540"$ | 149 | N2H+, HNC, HC3N, 3mm-Cont. | 2019.1.00641.S
LDN 1641N | 05h36m27s.0 | -06∘25′00$"$ | $210"\leavevmode\nobreak\ \times\leavevmode\nobreak\ 540"$ | 132 | N2H+, HNC, HC3N, 3mm-Cont. | 2019.1.00641.S
NGC 2023 | 05h41m29s.0 | -02∘20′20$"$ | $150"\leavevmode\nobreak\ \times\leavevmode\nobreak\ 650"$ | 123 | N2H+, HNC, HC3N, 3mm-Cont. | 2019.1.00641.S
Flame Nebula | 05h41m46s.0 | -01∘56′37$"$ | $150"\leavevmode\nobreak\ \times\leavevmode\nobreak\ 600"$ | 111 | N2H+, HNC, HC3N, 3mm-Cont. | 2019.1.00641.S

333(#) Observations presented in Hacar et al. (2018).

Table 3: EMERGE Early ALMA Survey: observational setup

Species | Frequency | E${}_{u}^{(1)}$ | n${}_{eff}^{(2)}$ | ALMA | IRAM-30m (3) | ALMA+IRAM
---|---|---|---|---|---|---
|  |  |  | Proj.ID | $\delta v$ | Proj.ID | $\delta v$ | ID | Resol.
| (GHz) | (K) | (cm-3) |  | (km s-1) |  | (km s-1) |  | (km s-1)
N2H+ (J=1-0) | 93.173764 | 4.47 | 1.0$\times 10^{4}$ | 2015.1.00669.S | 0.098 | 032-13 | 0.06 | narrow | 0.10
|  |  |  | 2019.1.00641.S | 0.114 | 034-16 | 0.06 | narrow | 0.12
|  |  |  |  |  | 120-22 | 0.06 | narrow | 0.12
HNC (J=1-0) | 90.663564 | 4.53 | 3.7$\times 10^{3}$ | 2019.1.00641.S | 0.233 | 032-13 | 0.66 | broad | 0.66
|  |  |  |  |  | 034-16 | 0.66 | broad | 0.66
|  |  |  |  |  | 060-22 | 0.06 | narrow | 0.25
|  |  |  |  |  | 133-22 | 0.06 | narrow | 0.25
HC3N (J=10-9) | 90.979023 | 24.1 | 4.3$\times 10^{5}$ | 2019.1.00641.S | 0.233 | 032-13 | 0.66 | broad | 0.66
|  |  |  |  |  | 034-16 | 0.66 | broad | 0.66
|  |  |  |  |  | 060-22 | 0.16 | narrow | 0.25
|  |  |  |  |  | 133-22 | 0.16 | narrow | 0.25
HCN (J=1-0) | 88.631602 | 4.25 | 8.4$\times 10^{3}$ | —(⋆) | —(⋆) | 032-13 | 0.66 | broad | —
|  |  |  |  |  | 034-16 | 0.66 | broad | —
|  |  |  |  |  | 133-22 | 0.66 | broad | —
3mm-Cont. | 93, 104 | — | — | 2015.1.00669.S | 6.0 & 5.4 | —(⋆) | —(⋆) | — | Cont.(⋆⋆)
3mm-Cont. | 93.2, 91.2 | — | — | 2019.1.00641.S | 3.71 | —(⋆) | —(⋆) | — | Cont.(⋆⋆)

444(1) Values for the upper energy levels (Eu), without hyperfine structure,
are taken from the Leiden Atomic and Molecular Database (LAMDA; Schöier et
al., 2005; van der Tak et al., 2020). (2) Effective excitation density at 10 K
(Shirley, 2015). (3) The footprints of the different IRAM-30m data are color-
coded in Figs.1 and 2: 032-12 (black), 034-16 (brown), 120-20 (gold), 133-22
(blue), and 060-22 (red). ($\star$) No data available. ($\star\star$) ALMA-
alone maps.

###  3.1 Interferometric ALMA observations

We surveyed the molecular and continuum emission of the OMC-3, OMC-4 South,
LDN 1641, NGC 2023 and Flame Nebula regions using the Atacama Large Millimeter
Array (ALMA) (proj. ID: 2019.1.00641.S; PI: Hacar) in Chajnantor (Chile)
during ALMA Cycle-7. We summarize the main parameters of our ALMA observations
in Table 2. We used independent large-scale ALMA mosaics, containing between
111 and 149 pointings each, with a typical map sizes of $\sim 200\times 600$
arcsec2 (or $\sim 0.4\times 1.2$ pc2 at the distance of Orion). The footprints
of the individual ALMA fields are shown in Fig.1 and 2 (white contours)
superposed to the Herschel total gas column density maps of these regions
(background image; Lombardi et al., 2014). Our choice for the phase center,
size, and orientation of our maps follows the distribution of the N2H+ (1-0)
integrated emission reported in previous single-dish surveys of the Orion A
(Hacar et al., 2017a) and Orion B (Pety et al., 2017) clouds.

We mapped the molecular and continuum emission of all of our targets with the
ALMA-12m array (only) in its most compact configuration (C43-1), and with
baselines between 15 and $\sim$ 310 meters, achieving a native resolution of
$\theta\sim$ 3.5 arcsec. All fields, each observed as individual Scheduling
Blocks (SB), were covered at least twice and observed for a total of 2-3 hours
per field with a minimum of 45 antennas under average weather conditions
(Precipitable Water Vapor PWV=2.5-5 mm). Following standard procedures
pointing, bandpass, and flux calibrations were carried out at the beginning of
each observing run which phase calibration were obtained every 10-15 minutes
using different sources.

Our observations were carried out in Band 3 using a single spectral setup
combining three narrow line plus two broad continuum spectral windows (SPW)
simultaneously. We include a detailed description of the different
observational setup in Table 3. In our new ALMA observations we used a narrow
SPW to map the N2H+ (1-0) (93.17 GHz) emission at high spectral resolution
($\delta v=$ 0.114 km s-1). We set independent SPWs for both HNC (1-0) (90.66
GHz) and HC3N (10-9) (90.97 GHz) lines at intermediate spectral resolution
($\delta v=$ 0.233 km s-1). Moreover, we set two additional broad band, 1.875
GHz wide SPWs to map the continuum emission at 93.2 and 91.2 GHz.

To complete our survey we added similar ALMA Cycle-3 mosaics of the OMC-1 and
OMC-2 regions in Band 3 (proj. ID: 2015.1.00669.S; PI: Hacar). The description
of these previous datasets are presented in Hacar et al. (2018) (see also
Tables 2 and 3). Independent N2H+ (1-0) maps are carried out along OMC-1 and
OMC-2 at high spectral resolutions ($\delta v=$ 0.098 km s-1) together with
two 1.875 GHz wide, broad continuum bands at 93 and 104 GHz, respectively (see
also van Terwisga et al., 2019). Unfortunately no HNC (1-0) nor HC3N (10-9)
observations were possible at the time due to the limitations on the data
rates during Cycle-3. No ALMA map is therefore available for these transitions
in OMC-1 or OMC-2.

When needed (e.g., during data combination), we assumed standard flux
conversions, i.e.,
$\left(\frac{\mathrm{T}_{\mathrm{mb}}}{1\leavevmode\nobreak\
\mathrm{K}}\right)=\left(\frac{S_{\nu}}{1\leavevmode\nobreak\
\mathrm{Jy}}\right)\left[13.6\left(\frac{300\leavevmode\nobreak\
\mathrm{GHz}}{\nu}\right)^{2}\left(\frac{1\leavevmode\nobreak\
\mathrm{arcsec}}{\theta}\right)^{2}\right]$ (e.g., 6.96 and 0.16 K Jy-1 for
beams of 4$\aas@@fstack{\prime\prime}$5 and 30″, respectively; see ALMA
technical handbook, Remijan et al., 2019). The reduction of our ALMA
observations, including data combination with additional single-dish
observations (Sect. 3.2), is discussed in Sect. 4.

###  3.2 Single-dish IRAM-30m observations

We complement our ALMA survey with additional single-dish observations at 3 mm
carried out at the 30-meter Intitute Radioastronomie Millimetric telescope
(IRAM-30m) in Granada (Spain) in multiple observing campaigns between 2013 and
2023. We mapped the molecular emission of all our ALMA targets using
independent, large-scale IRAM-30m mosaics all observed with the Eight Mixer
Receiver (EMIR) receiver connected to both the VErsatile SPectrometer Array
(VESPA) and Fast Fourier Transform Spectrometer (FTS) backends. We summarize
these observations in Table 3. We observed all our fields with two independent
spectral setups. In a first set of observations (proj. IDs: 032-13, 034-16,
120-20) we obtained large-scale maps of N2H+ (1-0) (93.17 GHz) using VESPA at
high-spectral resolution ($\delta v=$ 0.06 km s-1). Simultaneously, we mapped
the emission of HNC (1-0) (90.66 GHz), HCN (1-0) (88.63 GHz) and HC3N (10-9)
(90.97 GHz), among other lines, using multiple FTS units in broad
configuration at low spectral resolution ($\delta v=$ 0.66 km s-1). In a
second set of maps (proj. IDs: 060-22, 133-22) we then focused on the local
HNC (1-0) (90.66 GHz) and HC3N (10-9) (90.97 GHz) emission and reobserved our
ALMA fields using smaller mosaics at higher spectral resolution with VESPA
($\delta v=$ 0.06 km s-1) and FTS in narrow configuration ($\delta v=$ 0.16 km
s-1).

Each large single-dish mosaic is constructed by several individual tiles,
typically of sizes of $200\times 200$ arcsec2, observed multiple times in
orthogonal directions using On-The-Fly (OTF), Position Switching (PSw)
observations. The footprints of our low- and high-resolution IRAM-30m maps is
again shown in Figs. 1 and 2. We carried out our data reduction using the
GILDAS-CLASS software (Pety, 2005; Gildas Team, 2013) using standard
procedures for OTF observations. We reduced each tile independently and
applied facility provided efficiencies to calibrate our spectra into main beam
temperature scale (Tmb). For each of our target lines we combine our
calibrated spectra, observed at a native resolution $\sim$ 27 arcsec, into a
regular grid and convolved them into a final resolution of $\theta=$ 30 arcsec
in order to obtain individual Nyquist-sampled cubes. As final data products of
our single-dish observations we obtained individual N2H+ (1-0), HNC (1-0), HCN
(1-0), and HC3N (10-9) datacubes (including their entire hyperfine structure)
for each of the sources in our survey (OMC-1/2/3/4 South, LDN 1641N, NGC 2023,
and Flame Nebula). In the case of HNC and HC3N, we reduced these cubes at low
and high spectral resolution.

Following Hacar et al. (2020), we combined our low spectral resolution (broad)
HCN (1-0) and HNC (1-0) observations to investigate the gas temperature of the
gas in our survey. We obtained individual integrated intensity maps of each of
these tracers (I(HCN) and I(HNC)) adjusted to the velocity range of each
target see in HCN. Both HCN and HNC present bright emission usually following
the column density distribution of our clouds and extend beyond the coverage
of our maps (see Pety et al., 2017; Hacar et al., 2020; Tafalla et al., 2023).
Yet, we consider only those positions with I(HNC) $\geq$ 2 K km s-1 and then
convert the resulting I(HCN)/I(HNC) intensity ratios into the corresponding
gas kinetic temperature $T_{\mathrm{K}}$(HCN/HNC) per pixel using the
empirical correlations defined by Hacar et al. (2020). The I(HCN)/I(HNC)
intensity ratios provides a good proxy of the gas kinetic temperature at
column densities N(H2) $>10^{22}$ cm-2 and within an optimal temperature range
between $\sim$15 and 40 K, with only minor contamination in local regions
affected by outflows (e.g., Ori BN/KL in OMC-1). Using this method we then
obtain large-scale, kinetic temperature $T_{\mathrm{K}}$(HCN/HNC) maps
(hereafter $T_{\mathrm{K}}$ maps) for all the sources in our sample (see
Sect.5.1).

###  3.3 Complementary IR data

We aim to investigate the connection between stars and gas in our different
ALMA fields. In addition to our millimeter observations, our project benefits
from a wealth of ancillary, public surveys in Orion at multiple wavelengths.
In particular, we used the total H2 column density maps obtained by Lombardi
et al. (2014) from the FIR Herschel observations of the entire Orion A and B
clouds. We converted these maps, originally measuring the dust opacity at 350
$\mu$m ($\tau_{350\mu\mathrm{m}}$), first into equivalent K-band extinction
values (AK) using the conversion values provided by Lombardi et al, and later
into total H2 column density maps assuming a standard reddening law
(AK/AV=0.112; Rieke & Lebofsky, 1985) and dust-to-gas conversion factor
(N(H2)/A${}_{V}=0.93\times 10^{21}$ cm-2 mag-1; Bohlin et al., 1978). We
therefore obtained total H2 column density maps (N(H2)) of all our sources at
36” resolution (Sect.5.1).

For illustrative purposes we also make use of archival 3.4 $\mu m$ emission
images obtained by the Wide-field Infrared Survey Explorer (WISE) (Wright et
al., 2010). Together with the detection of most of the young stars in our
targets, the WISE 3.4 $\mu m$ band includes prominent Polycyclic Aromatic
Hydrocarbons (PAH) features, such as the C-H stretching band of FUV-pumped PAH
(e.g., Chown et al., 2023), highlighting the effects of external FUV radiation
onto the dust grains. Compared to the equivalent 12 $\mu m$ images, the WISE
3.4 $\mu m$ band provides a wide dynamic range in intensity without undesired
saturation effects in bright regions such as OMC-1. These WISE images offer a
complementary view compared to the molecular gas traced by our
(sub-)millimeter maps.

The YSOs in both Orion A and B clouds have been systematically surveyed by
Megeath et al. (2012) using Spitzer observations. From the analysis of their
mid-IR colours, Megeath et al classified these sources as protostars (P) or
pre-main sequence stars with disks (D), typically corresponding to Class 0/I
and Class II objects (Greene et al., 1994), respectively. We adopt the Megeath
et al. (2012) catalog as reference for our study. We refined this catalog by
including additional young objects (Protostars and Flat spectrum, all included
as P objects in our catalogue) reported in follow-up Herschel observations
(Furlan et al., 2016; Stutz et al., 2013). Overall, we expect a high
homogeneity and completeness of this catalogue in describing the low-mass, YSO
population within our ALMA fields with perhaps the exception of the OMC-1 and
Flame Nebula due to their bright nebulae and crowded populations (see also
Megeath et al., 2016; Großschedl et al., 2019, for a detailed discussion). The
lack of large-scale, systematic surveys of Class III objects in Orion B (e.g.
in X-rays) limits the extension of this analysis to older populations. In a
separate catalog for massive stars, we collected sources in Orion classified
as spectral types O and B using Simbad (Wenger et al., 2000).

##  4 Massive data reduction of ALMA observations

Figure 3: EMERGE Early Science data reduction workflow for our single-dish,
IRAM-30m (Upper panel) and interferometric ALMA (Lower panel) observations. In
the case of our ALMA reductions, both calibration (stage #1; light blue) and
imaging (stage #2; light red) processes are carried out at the SPIDER-SURFsara
supercomputer using parallel processing.

The nature and volume of our EMERGE Early ALMA Survey, including mosaics of a
large number of target areas (5+2) combining different molecular (3) and
continuum (1) maps, sometimes in multiple spectral configurations (2),
requires the development of automatic data reduction techniques. The large
number of potential data products plus the use of our single-dish data using
different data combination techniques (see below) make the manual and
customized reduction of each target not only highly inefficient but also
costly in time. The optimization of this process and the production of
homogeneous datasets is of paramount interest for our study.

In this section we introduce a data reduction framework where the
standardization of the calibration and imaging steps allows the full
automation of the ALMA data reduction process shortening the effective
processing time by more than an order of of magnitude. First, we detail the
description of the calibration and imaging steps for individual observing
setups in Sect. 4.1. Second, we describe how these standardized processes can
be easily parallelized using a supercomputer in Sect. 4.2. Third, we quantify
the quality of our reductions using advance quality assessments in Sect. 4.3.
Finally, we present the resulting molecular line cubes and continuum maps,
including when possible combined ALMA+IRAM-30m observations, as final data
products of our survey in Sect. 4.4.

###  4.1 Calibration, imaging, and data combination

We present the workflow of our data reduction scheme in Figure 3 (lower
panel). We exemplify this reduction process for the data obtained in the OMC-3
region, from the original ALMA Scheduling Block (SB) containing raw
visibilities including continuum and lines to the final data products in FITS
format in both interferometric-alone and combined maps and cubes. Overall, the
full data reduction process is divided in two stages: (1) the calibration of
the raw visibilities, and (2) the imaging (or deconvolution) process. We
carried out both calibration and imaging steps using the Common Astronomy
Software Applications (CASA) package (CASA Team et al., 2022; Emonts et al.,
2022). We discuss each of these steps independently below.

As part of stage #1, we applied standard calibration to the raw visibilities
in order to prepare our data for imaging (Fig. 3, bottom panel, left part).
Different executions of the same SB lead into different Measurement Sets (MS)
including multiple SPWs (lines + continuum). We recreated the ALMA pipeline
calibration of each MS file applying facility provided scripts (scriptforPI)
using CASA versions 4.7.2 or 5.4.0 depending on the target. We then applied a
continuum subtraction for all science SPWs in each calibrated MS file
independently and created individual files for both continuum and continuum-
subtracted line visibilities.

In stage #2, we imaged each line and continuum mosaic independently (Fig. 3,
bottom panel, right part). We first split (only for line SPWs) and then
concatenated the previous calibrated visibilities producing single visibility
files for each molecular species and continuum dataset per field. Each ALMA
mosaic was then imaged using the CLEAN deconvolution algorithm (Högbom, 1974;
Clark, 1980) using the task clean in CASA version 5.7555We note that the ALMA
task tclean showed unstable results when using a startmodel option during data
combination in CASA version 5.7. These issues are not present in clean which
motivated our preference for this latter task. Newer versions of CASA seem to
have fixed this problem and tclean will be used in future EMERGE data
reductions. . When possible, we applied data combination to include our
IRAM-30m data (Sect. 3.1) as short-spacing information (see below). To
facilitate its implementation, we homogenized most of the clean parameters in
our reductions. Our line and continuum mosaics are deconvolved adopting the
phase centers indicated in Table 2 with a mapsize of 1600”$\times$1600” with a
cellsize of 0$\aas@@fstack{\prime\prime}$45, and reduced using standard clark
plus briggs weighting schemes with robust parameter equal to 0.5 using 5$\cdot
10^{4}$ iterations. As main differences, we set thresholds of 1 mJy for maps
in continuum reduced in msf mode and of 10 mJy per channel for our line cubes
obtained in velocity mode. In the case of the line cubes, we also optimized
the spectral resolution (see last column in Table 3) and number of channels
for deconvolution depending on the species and resolution (narrow and broad)
of our interferometric and single-dish data (see Table 3).

Plunkett et al. (2023) recently highlighted the role of data combination in
the analysis of extended sources such as our ALMA targets. Due to the so-
called short-spacing problem (Wilner & Welch, 1994), the lack of sensitivity
at short-baselines leads to scale-dependent filtering effects in both
continuum and line observations (e.g., Pety et al., 2013). As shown in
previous works (e.g., Leroy et al., 2021), these issues can critically affect
the fidelity of interferometric observations. We explored the effects of data
combination using three independent reductions for each of our molecular line
mosaics: int-CLEAN, Feather, and MACF. A detailed description of these data
combination methods and their implementation can be found in Plunkett et al.
(2023).

In a first and basic reduction, we obtained an interferometric-only map by
deconvolving the ALMA-alone visibilities (int-CLEAN). Second, we use the task
feather (Cotton, 2017) as standard data combination CASA method to combine our
previous int-CLEAN maps with our IRAM-30m observations (Feather). Third, and
finally, we applied a Model Assisted Cleaning and Feather combination (MACF)
which introduces the single-dish information both as initial cleaning model
and feathering image. Contrary to the line observations, no single-dish
information is available in continuum. As a result, we were able to produce
only interferometric-alone (int-CLEAN) continuum maps for our sources.
Additional data combination techniques (see Plunkett et al., 2023, for a
review) will be explored in future papers of this series.

The deconvolution of our ALMA observations produces images with typical
beamsizes around $\theta_{0}\sim$ 3$\aas@@fstack{\prime\prime}$5\. We applied
a primary beam (PB) correction to all our cleaned images and masked all
emission channels (voxels) with PB values lower than 0.5. To increase the
stability of our maps, we also applied a small convolution after deconvolution
(and before feathering) and resampled the resulting cubes every half beam,
leading into Nyquist-sampled maps (i.e. $\theta/2$) and cubes with a final
resolution of $\theta=$ 4$\aas@@fstack{\prime\prime}$5.

Our specific choices for the above stage #2 parameters aim to define single
parameter values that can be homogeneously applied to our entire sample.
Spatial paramaters such as the mapsize are automatically defined to cover the
largest dimensions of our dedicated IRAM-30m data around the corresponding
ALMA fields. By design, we also adopt a single cellsize value of $\theta/10$
in order to correctly oversample the native beamsize $\theta_{0}$ (i.e.
cellsize$<\theta_{0}/5$) while at the same time facilitate the Nyquist
resampling of our data products at their final resolution (final pixel size =
5$\times$ original cellsize). Other cleaning parameters are chosen as a
compromise between the maximization of the quality of our maps (see Quality
Assessments in Sect. 4.3) and the simultaneously minimization of the total
computing time per field. Additional tests using larger number of iterations
or lower thresholds produce no significant differences on the final data
products while have a direct impact on the deconvolution timescales when
applied to the large number of targets and lines included in our sample (76
individual reductions; see below). Dedicated reductions could potentially
produce better results for individual observations at the expense of larger
computing times and human intervention per field. In contrast, our EMERGE
Early ALMA Survey leverages on a large statistical sample of targets and lines
homogeneously reduced in short timescales (see Sect.4.2). Similar optimization
strategies appear of critical importance for future ALMA archival studies.

###  4.2 Parallelization on a high-performance computer

The standardization of the calibration and imaging steps (Sect. 4.1) makes our
data reduction process easy to parallelize into separate CASA jobs. As shown
in Fig. 3, we also take advantage of the original ALMA data structure. Each of
our ALMA fields is stored in a single SB that can be recreated separately.
After an early split, the imaging process of the resulting continuum and line
calibrated visibilities can be then run independently in an automatic way.
Also, each of these modular steps can be carried out using dedicated CASA
versions.

We executed parallel runs of both calibration and imaging steps for our entire
ALMA sample in the SPIDER Data Processing
Platform666https://spiderdocs.readthedocs.io/en/latest/ part of the
SURF777https://www.surf.nl/ Data Processing facilities in Amsterdam888The
EMERGE Early Science Survey was executed as part of the projects “Advance ALMA
data reduction using SPIDER” (PI: A. Hacar) and ”Advanced ALMA data reduction”
(PI: A. Ahmadi) carried out at SURF-SPIDER in collaboration with the Allegro
Dutch ARC node in Leiden.. The high performance and flexibility of SURF-SPIDER
permits to run a large number of CASA instances in parallel. First, we
simultaneously recreate all our calibrated visibilities at once (stage #1).
Second, we carry out all individual line and continuum deconvolutions
(including data combination) simultaneously (stage #2). Each CASA run is
assigned to 5 CPUs providing a total memory of 40 GB per job to optimize the
CPU and queuing times. Additional tests indicate no performance improvement
when scaling our reductions neither with a larger number of cores999We remark
here that the use of a higher number of cores (CPUs) indeed reduces the
execution time per reduction although this does not significantly improve the
overall performance of the sample processing. Our tests indicate that the
reduction process inversely scales with the number of cores. However, this
performance scaling becomes sub-linear with more than 5 cores and usually
saturates at $\sim$10 cores. The request of $>5$ cores makes the reduction
process not only more expensive in computational resources but also more
difficult to schedule by the queuing system in large numbers. Our choice is
therefore justified in order to optimize the use of computational resources
while reducing the execution time (queuing + reduction) for the entire sample.
nor with CASA executions in parallel (mpicasa) mode in the SURF-SPIDER
architecture. This approach is similar to the parallel reduction method
introduced by van Terwisga et al. (2022) for the massive data processing of
ALMA single-pointing, continuum maps, this time extended into mosaics and
spectral cubes.

The efficiency of our data reduction process strongly benefits from the use of
high-performance computers such as SURF-SPIDER. Our full ALMA survey consists
of a total of (5 targets $\times$ [(Cont. + N2H+ \+ HNC + HC3N) $\times$ 1
int-CLEAN + [(1$\times$ N2H+ \+ 2$\times$ HNC + 2$\times$ HC3N cubes)$\times$
2 datacomb.] + 2 targets $\times$ [(Cont. + N2H+) $\times$ (1 int-CLEAN + 2
datacombs)] ) = 76 individual data products. The processing time per
individual target line (including the three data combination methods) is about
30-60 hours of computing time depending on the spectral resolution and queuing
time. On the other hand, with the implementation of our parallel data
processing the entire EMERGE Early ALMA Sample can be reduced in
$\leavevmode\nobreak\ \lesssim$ 72 hours (including overheads), that is, at
least an order of magnitude less time than the standard (linear) reduction.
The application of this parallel methodology in massive ALMA archive research
will be presented in a future work.

###  4.3 Quality assessments

The use of automatic reductions (see Sect. 4.2) requires a careful evaluation
of the quality of our data products. To homogeneously quantify the goodness of
our reduction and combination processes, we have developed several of the
Quality Assessment (QA) metrics described by Plunkett et al. (2023). We focus
on the comparison of the flux recovered in our spectral cubes (N2H+, HNC,
HC3N) using different methods (int-CLEAN, Feather, and MACF) with respect the
total flux present in our single-dish, IRAM-30m observations (see Fig. 3). A
detailed discussion of our QA results can be found in Appendix A.

Our analysis demonstrates how the addition of the the short-spacing
information significantly improves the image fidelity of our ALMA observations
(Appendix A.1; see also Paper II). Similar to the results discussed in
Plunkett et al. (2023), our int-CLEAN reductions exhibit flux losses of more
than 70% of the flux detected in the single-dish data due to interferometric
filtering. In contrast, the implementation of data combination techniques such
as Feather and MACF improve the flux recovery to $\gtrsim$ 90%. The lack of
short-spacing information has a dramatic impact also on the gas kinematics at
all scales (Appendix A.3). Different velocity components are selectively (and
largely unpredictably) filtered in velocity altering derived parameters such
as line intensities and ratios, centroid velocities, and linewidths. Our QAs
highlights the need of data combination for the analysis of regions with
complex and extended emission similar to those included in our sample.

While both Feather and MACF methods produce satisfactory reductions, MACF at
high spectral resolution (narrow) shows the most stable results in our
assessments (total flux recovery, flux per intensity bin, and flux per
channel) in most of our fields and spectral setups (see Appendix A.2 for a
full discussion). For a homogeneous analysis, and to facilitate the comparison
with the OMC-1 and OMC-2 results (Hacar et al., 2018), we adopted our MACF
reductions as final science products in all our fields (see Sect. 4.4) and
will use them during the analysis of our EMERGE sample (hereafter referred to
as ALMA+IRAM-30m data). On the other hand, no single-dish data were available
to complement our continuum observations. Our ALMA Band-3 continuum int-CLEAN
maps should be then treated with appropriate caution.

###  4.4 Final data products & Data Release 1

Our final EMERGE Early ALMA Survey consists of 76 ALMA datasets, including
spectral and 3 mm continuum maps as well as multiple data combinations, in the
OMC-3, OMC-4 South, LDN 1641N, NGC 2023, and Flame Nebula star-forming
regions, all with a final resolution of 4$\aas@@fstack{\prime\prime}$5 or
$\sim$ 2000 au at the distance of Orion. Our sample also includes a total of
30 IRAM-30m-alone spectral maps of the same line tracers (see Sect. 3.2) at
30″resolution. Moreover, we obtained the $T_{\mathrm{K}}$ (30″; Sect. 3.2) and
derived a set of total column density maps at high spatial resolution
(4$\aas@@fstack{\prime\prime}$5) using a new technique to be presented in
Hacar et al (in prep) in all these targets (5$\times$ each) as high-level data
products. Our new observations are also complemented by similar ALMA (only
Continuum and N2H+) plus IRAM-30m observations in OMC-1 and OMC-2 (Hacar et
al., 2018; van Terwisga et al., 2019).

All our fully-reduced, ALMA plus IRAM-30m data products will be included in
different Data Releases (DR) accessible in a dedicated page on our
website101010https://emerge.univie.ac.at/results/data/. Accompanying this
paper, we have released all integrated intensity maps of our ALMA and IRAM-30m
observations in FITS format as part of the EMERGE Data Release 1 (DR1). Future
DRs will include all spectral cubes and column density maps of our sample.

##  5 Sample properties

In this section we present the main physical properties of the target sample
observed in our EMERGE Early ALMA Survey based on the analysis of their
integrated intensity and continuum maps at both (low-) single-dish (Sect. 5.1)
and (high-) interferometric (Sect. 5.2) resolutions. To illustrate our results
(e.g. emission distribution, gas structure, etc) we use OMC-3 as
representative showcase of our dataset (e.g., Fig. 4). Similar plots for other
regions can be found in Appendix B. To facilitate the comparison between
targets we display all regions within the same plotting ranges in each of the
figure panels. Our ALMA targets cover a wide range of physical conditions in
terms of mass, structure, stellar content, and evolution (see below). We
quantify and summarize some of these properties in Table 1. Additional
analysis using the full spectral information will be presented in future
papers of this series (e.g., gas kinematics; see Paper III).

In our calculations we adopted standard distances to our targets (see Table
1). For sources in Orion A (OMC-1/4 and LDN 1641N) we assume a typical
distance of 400 pc as a compromise between the VLBI measurements of the ONC
(414$\pm 7$; Menten et al., 2007) and the Gaia estimates for the head of the
ISF (393$\pm$13; Großschedl et al., 2018). On the other hand, we use a
distance of 423 pc for Orion B (NGC 2023 and Flame Nebula) in agreement with
the Gaia (423$\pm$21; Zucker et al., 2019) and VLBI ($\sim$ 420 pc; Kounkel et
al., 2017) results. Uncertainties of $\pm$ 10-20 pc (or $\sim$ 5% of the cloud
distance; see references above) are expected from these values producing only
minor effects on our mass and size calculations. For a typical distance of 400
pc, the corresponding physical resolution of our data would then be 0.058 pc
(or 12000 au) for our IRAM-30m observations (30″) and 0.009 pc (or 1800 au)
for our ALMA maps (4$\aas@@fstack{\prime\prime}$5).

###  5.1 Low-resolution observations

####  5.1.1 Gas column density and temperature

Figure 4: Low-resolution observations in OMC-3. From left to right and from
top to bottom: (a) Herschel, total gas column density, N(H2) (36”); (b) WISE
3.4 $\mu$m emission (6.1”); (c) gas kinetic temperature, $T_{\mathrm{K}}$, (d)
N2H+ (1-0), (e) HNC (1-0), and (d) HC3N (10-9) integrated intensity maps
obtained in our IRAM-30m (broad) observations (30”). Symbols are similar to
those in Fig. 1. The corresponding beam size and scale bar are indicated in
the lower par of each panel. For reference, panel a includes contours at N(H2)
= [15, 50, 100, 150, 200] $\times 10^{21}$ cm-2 as well as the footprint of
our ALMA observations shown in Fig. 8. Similar plots for all the other regions
can be found in Appendix B.

While limited to only 7 targets, our EMERGE Early ALMA Sample covers an
extensive range of physical conditions and environments. In addition to the
overall description of our survey (Sect. 2), we quantified additional
evolutionary properties of our sources considering all positions covered by
our IRAM-30m (broad) observations within representative area of 1.5$\times$1.5
pc2 from the center of our maps ($\sim$ 700$\times$700 arcsec2; roughly
similar to the zoom-in maps in Figs. 1 and 2).

The wide range of stellar activity in our sample is reflected in the
populations of protostars (P) plus disk (D) YSOs (Table 1). Our survey covers
almost two orders of magnitude in average stellar density (i.e. (P+D)/Area or
simply P+D) from the highly clustered OMC-1 region to the more diffuse NGC
2023 forming stars almost in isolation. We note here that the stellar density
is largely underestimated in the case of OMC-1 and Flame Nebula since our
stellar counting does not include any of the optical stars identified in these
clouds as well as the bright emission of their nebulae rapidly increases the
incompleteness of IR surveys (Megeath et al., 2016).

Figure 5: Histograms of the (Left) gas column density N(H2) and (Right) gas
kinetic temperature $T_{\mathrm{K}}$ distributions detected at low spatial
resolution (Herschel or IRAM-30m) within the central 1.5$\times$1.5 pc2
($\sim$ 700$\times$700 arcsec2) of all regions part of the EMERGE Early ALMA
Survey (see also Tables 1 and 2). From top to bottom: OMC-1, OMC-2, OMC-3,
OMC-4 South, LDN 1641N, NGC 2023, and Flame Nebula. Positions showing detected
emission above the first contour in Fig. 4 (and Figs.13-18) of HNC (black
dashed lines), N2H+ (black solid lines), and HC3N (black dotted lines), as
well as including protostars (grey hatched areas) are highlighted in all
histograms. A vertical grey dashed line in the N(H2) histograms indicates the
minimum column density of 1023.4 cm-2 (or 1 g cm-2) needed to form a high-mass
star (Krumholz & McKee, 2008).

The diversity of our sample becomes apparent in the distribution of the Column
Density Probability Distribution Functions (N-PDF) displayed in Fig. 5 (left
panel)111111We note that the Herschel maps used in Lombardi et al. (2014) show
several saturated pixels at the brightest positions in both OMC-1 and Flame
Nebula fields. The derived N(H2) values at these positions should be then
taken with caution (see secondary peak in the N-PDF of OMC-1 in Fig. 5, top
left panel).. Large amounts of gas at N(H2) $>$ 1023 cm-2 are seen in regions
forming O-type stars (OMC-1 and Flame Nebula), close to the theoretical
predictions for the formation of high-mass stars (i.e. N(H2) $=$ 1023.4 cm-2;
Krumholz & McKee, 2008). In contrast, low-mass and diffuse regions (e.g.,
OMC-4 South or NGC 2023) reach maximum column densities of N(H2) $\sim$ 1023
cm-2 in only few locations. Likewise, the high-end of the N-PDF distributions
in active regions with higher fractions of dense gas (OMC-1/2/3, LDN 1641N,
and Flame Nebula) shows shallower slopes than those found more diffuse clouds
(OMC-4 South and NGC 2023) in agreement with previous works in some of these
clouds (Stutz & Kainulainen, 2015).

Our gas kinetic temperature $T_{\mathrm{K}}$ maps (see Fig. 4c) illustrate the
thermal evolution of gas during the star formation process. Well shielded,
high column density regions with N(H2) $>2\times 10^{22}$ cm-2 usually show
cold temperatures of $\lesssim 25$ K. In contrast, lower column density
material is usually found at higher temperatures up to $\sim$ 40 K heated up
by external radiation resulting in a positive thermal gradient towards the
edge of the cloud. Local heating effects are seen associated to some locations
with strong star formation activity (e.g., compare the positions of the WISE
sources with our $T_{\mathrm{K}}$ maps around OMC-2 FIR-2 in Figs. 14b and
14c). More prominently, regions under the influence of high-mass stars (OMC-1
and Flame Nebula) show large areas of both low- and high column density gas at
temperatures above 40 K coincident with the bright emission in the WISE
3.4$\mu$m maps (see Figs. 13 and 18, respectively). These results demonstrate
the sensitivity of low-resolution HCN/HNC measurements to describe the thermal
properties across a wide range of column densities and environments (see Hacar
et al., 2020, for a discussion).

Feedback also shapes the overall temperature distribution within these clouds
as seen in the $T_{\mathrm{K}}$ histograms (or Temperature PDF,
$T_{\mathrm{K}}$-PDF) shown in Fig. 5 (right panels) as well as changes the
average gas temperature in these regions (see Table 1). Most of the gas in
low-mass star-forming regions (e.g., OMC-3 or OMC-4 South) is found at
temperatures between 10-30 K with mean values around $\sim$25 K. In contrast,
regions with embedded massive stars show larger average values of $\gtrsim$ 30
K with significant amounts of gas at T${}_{K}>45$ K (see the tails of the
$T_{\mathrm{K}}$-PDF in OMC-1 and Flame Nebula). Interestingly, LDN 1641N
shows a significant fraction of low column density gas above 40 K surrounding
the cloud (see external heating in Fig.16c). The origin of this warm component
is unclear although the cometary shape of this cloud pointing towards the NGC
1980 cluster might suggest a potential interaction with $\iota$ Ori and NGC
1980.

####  5.1.2 Molecular emission and density selective tracers

We describe the global emission properties of our sample using our low spatial
resolution observations shown in Figure 4 (lower panels). For that, we take
advantage of the extended spatial coverage of our (broad spectral resolution)
IRAM-30m maps at low spectral resolution (see Sect. 3.2). Together with the
N(H2), WISE, and $T_{\mathrm{K}}$ maps (panels a-c) already presented in Sect.
5.1.1, we display the N2H+ (1-0), HNC (1-0), and HC3N (10-9) integrated
intensity maps in Fig. 4 (panels d-f) in OMC-3. We obtained each of these
molecular maps adapting the range of integrated velocities to the
corresponding cloud and transition. Similar plots for other regions can be
found in Figs. 13-18.

The emission of all molecular tracers is clearly detected in all sources
(panels d-f) with the only exception of the HC3N emission in OMC-4 South (see
Fig. 15). The overall emission properties in our maps are similar to the
results reported in previous large-scale surveys in Orion (Pety et al., 2017;
Kauffmann et al., 2017). N2H+ exhibits the brightest integrated intensity
values typically associated to regions of high-column density above N(H2)
$>2\times 10^{22}$ cm-2 (or A${}_{V}\gtrsim$ 20 mag). HNC (as well as HCN, not
shown) displays the most extended emission in our sample down to column
densities of N(H2) $\sim 10^{21}$ cm-2 (or few AV), in many cases extending
over the limits of our IRAM-30m maps. On the other hand, the HC3N emission is
usually clumpier, located in smaller areas, and comparatively weaker, although
showing bright emission features closely related to the position of some (but
not all) young protostars. The relative contributions of the different tracers
become apparent in the N(H2) and $T_{\mathrm{K}}$ histograms in Fig. 5 where
we highlight those pixels with N2H+ (black solid line), HNC (black dashed
line), and HC3N (black dotted line) emission within the first contours our of
map (e.g., see Fig. 4), and well as those pixels including protostars (grey
hatched area).

In terms of line brightness, our IRAM-30m maps display a large dynamic range
over up to two orders of magnitude in integrated intensity and with regions
such as OMC-1 showing peak values $>$200 Jy beam-1 km s-1 (or $>$30 K km s-1;
e.g., Fig.4). Moreover, the total luminosity of our maps systematically
changes between the regions in our sample (see comparison between the total
number of pixels includes in the histograms in Fig.5) and is closely related
to their physical characteristics (see Sect.5.1). Regions reaching high column
densities (OMC-1/2/3 and LDN 1641N) and/or high temperatures (OMC-1 and Flame
Nebula) show the brightest lines and total intensity maps in all transitions.
In contrast, the emission of the same lines in more diffuse and colder regions
(NGC 2023 and OMC-4 South) is always weaker.

The results of our molecular maps match the expected behavior for the suite of
selective tracers included in our survey (see line upper energies and
effective densities in Table 3). Formed after the depletion of CO (see Bergin
& Tafalla, 2007, for a review), N2H+ is a density selective species tracing
the cold ($\lesssim$ 20 K) and dense ($>10^{4}$ cm-3) gas (Tafalla et al.,
2002) while its ground transition is associated to dense cores (Caselli et
al., 2002) and filaments (Hacar et al., 2017b, 2018). Complementary to N2H+,
the HNC (1-0) line (as well as the HCN (1-0)) is effectively excited at
densities of a feq $10^{3}$ cm-3 (Shirley, 2015) and its emission traces the
cloud gas from low- to intermediate column densities before also depletes at
high densities (Tafalla et al., 2021, 2023). On the other hand, the excitation
of the HC3N (10-9) transition requires higher temperatures (E${}_{u}=24.1$ K)
and it is only effectively excited in lukewarm regions exposed to feedback
(Hacar et al in prep.).

####  5.1.3 Evolutionary state and dense gas Star Formation Efficiency

Additional insight on the evolutionary state of our targets can be inferred
from the comparisons of their populations of P and D stars, respectively. From
the comparison of the number of Class II (similar to D) and Class I (similar
to P) stars in nearby clouds, and assuming a typical age of 2 Myr for a
typical Class II objects, IR surveys of nearby stars have inferred a median
evolutionary timescale for a Class I object of $\sim$ 0.5 Myr (Evans et al.,
2009). Given these typical ages, the observed P/D ratio can be used as proxy
of the relative ages between targets (see Table 1). Young regions in which
most of the stars were formed within the last Myr are expected to show high
P/D$\gtrsim$1\. Clouds continuously forming stars for long periods of time
should approach a steady-state ratio in which P/D $=\frac{0.5\
\mathrm{Myr}}{2.0\ \mathrm{Myr}}\sim 0.25$. Finally, older regions with an
already declining star-formation rate should exhibit ratios such as
P/D$<$0.25, and approach P/D$\rightarrow$0 once they exhaust their gas
reservoir for forming stars. Within our sample, OMC-1, Flame Nebula, and OMC-4
South appear to be older and more evolved (P/D$\lesssim$0.2) compared to
younger regions such as OMC-2/3 and LDN 1641N (P/D$>$0.3) and particularly NGC
2023 (P/D=0.75).

We quantified the potential of these regions to form new stars from the amount
of gas detected in our Herschel maps (see Fig. 4a). We obtained two
measurements adding all Herschel column density measurements: (a) the total
mass of gas in our maps (Mtot), (b) mass of dense gas Mdense showing
significant N2H+ emission ($\geq 15$ Jy beam-1 km s-1 or $\gtrsim$ 2.5 K km
s-1 in Tmb units; see also Sect.5.1.4) and (c) fraction of dense gas
($f_{dense}$) obtained from the ratio between Mtot and Mdense. Active regions
such as OMC-1, OMC-2, OMC-3, and LDN 1641N exhibit large mass reservoirs
(M${}_{tot}>$ 500 M⊙) with significant amount of dense gas (M${}_{dense}>$ 300
M⊙ and $f_{dense}\gtrsim 0.4$) within their central $\sim$ 1 pc2. Although
comparable in total mass, OMC-4 South and NGC 2023 are more diffuse
(M${}_{dense}<$ 90 M⊙ and $f_{dense}\lesssim 0.2$). Flame Nebula is a
particularly interesting case showing the lowest mass available (M${}_{tot}<$
300 M⊙) although with a significant fraction of dense gas ($f_{dense}=0.33$).
In its latest stages of evolution (see above) the dense gas in the Flame
Nebula (as well as in OMC-1) appear to be more resistant to the disruptive
effects of feedback in agreement to simulations (Dale et al., 2014).

Figure 6: From top to bottom: (Upper panel) number of protostars (P) with
respect to the total amount of dense gas Mdense, (Middle panel) fraction of
dense gas $f_{dense}$, (Lower panel) and star formation efficiency in the
dense gas across the EMERGE Early ALMA survey sample (see values in Table 1).
We display the linear fit of all our targets (dashed red line) and their
corresponding Pearson p-coefficients (see values in legend) in both upper and
middle panels.

Despite the above exceptions, the values of Mdense and $f_{dense}$, both
correlated with the amount of N2H+ emission, appear as better proxies of the
current star-formation than the total gas mass Mtot of the region. We
illustrate these correlations in Fig. 6 by comparing the number of Protostars
(P) with the amount of dense gas Mdense (upper panel) and the fraction of
dense gas $f_{dense}$ (middle panel) across our sample (see values in Table
1). As shown in Fig. 6 (upper panel), the number of protostars in all our
targets shows a tight linear dependence (dashed red line) with Mdense (with a
Pearson coefficient p=0.90) stronger than the one obtained with Mtot (p=0.77;
not shown). The correlation with Mdense further improves after removing the
Flame Nebula (p=0.96; not shown). A similar linear dependence is seen in Fig.
6 (middle panel) when comparing the number of protostars with $f_{dense}$
(p=0.91). Figure 6 reinforces the direct correspondence between the amount of
dense gas (traced by N2H+) and the ongoing star formation in our targets.

The linear correlation between total number of YSOs (P+D) and the amount of
gas at column densities above A${}_{V}\gtrsim 8$ mag, used as proxy of Mdense
for the dense gas above $10^{4}$ cm-3, has been well documented in the past in
large-scale surveys of nearby molecular clouds (e.g., Lada et al., 2010). A
similar dependence is found when comparing the surface density of (only)
protostars (P) and high column density gas (Heiderman et al., 2010). This
positive correlation indicates how high density material is needed to
efficiently form stars within clouds (e.g., Lada, 1992). Figure 6 indicates
that this correlation gets tighter when considering the youngest protostars
(P) and the highest density material (traced by N2H+) in our targets.

The linear correlation found in Fig. 6 (upper panel) suggests a roughly
constant star formation efficiency per unit of dense gas SFE(dense) of a few
percent. In Fig. 6 (lower panel) we estimate this efficiency as SFE(dense)
$=\frac{\mathrm{M}_{proto}}{\mathrm{M}_{dense}+\mathrm{M}_{proto}}$ (e.g.,
Megeath et al., 2022), where $\mathrm{M}_{proto}$ ($=0.5\times\\#Protostars$)
corresponds to the total mass in protostars each assumed with a typical mass
of 0.5 M⊙. Our targets exhibit a typical SFE(dense) around $0.05\pm 0.03$ (or
$\sim$5%; grey dashed line) for Mdense values between $\sim 100$ and $600$ M⊙.
Compared to the $\sim$ 0.1 Myr free-fall time $\tau_{ff}=\sqrt{3\pi/32G\rho}$
for a typical dense gas density of $n\sim 10^{5}$ cm-3 traced by N2H+, the
above values for SFE(dense) translate into an approximate efficiency per free-
fall time of $\epsilon_{ff}\sim$ 1% in agreement with previous predictions
(Krumholz & Tan, 2007) and surveys (Pokhrel et al., 2021). The roughly
constant SFE(dense) derived from our data indicates the total mass of dense
gas as the primary factor determining the current star formation activity of
our regions leading into a number of young protostars directly proportional to
the amount of Mdense available in each case (see also Lada et al., 2010).

This is expected as the cold ($T_{\mathrm{K}}\lesssim 20$ K) and dense
($n(\mathrm{H}_{2})>10^{5}$ cm-3) material traced by N2H+ (Sect. 5.1.1)
promotes the conditions for gravitational collapse and it is therefore prone
to form stars. Active star-forming regions showing higher number of protostars
(e.g., OMC-1) are simply the result of their (currently) higher content of
this dense and cold material, showing higher star formation rates
(SFR(dense)=$\frac{\epsilon_{ff}}{\tau_{ff}}\times\mathrm{M}_{dense}$. Once
this dense gas is generated, it forms stars at a constant SFE(dense)
irrespective of the star formation regime (low- vs high-mass) and evolutionary
state (young vs old, or P/D value; see above) of the cloud.

####  5.1.4 Timescales for the evolution of dense gas

Figure 7: Cumulative distributions of the N2H+ (1-0) integrated emission
I(N2H+) at the positions of both protostars (solid colored line) and disk
stars (dashed colored line) within the central 1.5$\times$1.5 pc2 ($\sim$
700$\times$700 arcsec2) in each of the regions part of the EMERGE Early
Science sample. All panels are displayed within the same ranges of N2H+
intensity to facilitate their comparison. In each sub-panel, the local number
of protostars (P) and disk stars (D), as well as their ratio (P/D), are
indicated in the legend (see also Table 1). In each of these regions, the
average result of simulating the expected cumulative distributions of a P+D
number of objects following (a) a random distribution (grey solid line), (b) a
linear dependence (grey dotted line), and (c) a quadratic dependence (grey
dashed line) are shown in the corresponding panels.

,

As demonstrated in previous sections, N2H+ appears as the most direct proxy of
the young, star-forming gas in all targets of our survey. Following the
methodology introduced by Hacar et al. (2017b), in Figure 7 we display the
cumulative distribution of the N2H+ integrated intensity (in K km s-1) at the
position of protostars (P; colored solid line) and disk stars (D; colored
dotted line) found in the 7 regions of our study (see panels). In all cases,
the distribution for P stars always runs towards the right side of the one for
D stars, indicating that protostars are typically associated to brighter N2H+
emission in our maps, as expected for this dense gas tracer.

We have compared the above observational results with the expected cumulative
plots for a similar total number of objects per cloud (see legend) located
following a series of simulated distributions: (a) a random distribution (grey
solid line) as well as independent distributions in which the position of
these objects is favoured at the position of the N2H+ emission in our maps
using (b) linear (grey dotted line) and (c) quadratic (grey dashed line)
weights, respectively. In all our targets, the distribution of D stars closely
resembles a (quasi-)random distribution with respect to the dense gas traced
in N2H+ indicating that the D stars and N2H+ emission are uncorrelated. In
contrast, P stars have a much stronger connection with these emission features
typically showing a linear or even quadratic correlation with the N2H+
intensities in regions such as OMC-2/3 and LDN 1641N.

Deviations from the above general behaviour seen in OMC-1 (linear) and, more
prominently, in the Flame Nebula (sublinear) can be explained by the
evolutionary state of these targets (see Sect. 5.1.3) and the direct impact of
radiative and mechanical feedback in regions hosting high-mass stars. Only
small amounts of dense gas is detected in the immediate vicinity of the
Trapezium stars (including $\theta^{1}$ Ori C) and NGC 2024 IRS-2b, which
demonstrates the rapid dispersion and photo-dissociation of the molecular
material after the formation of these high-mass objects. Yet, the still
visible correlation between the N2H+ emission and the location of the young
protostars in these targets indicate that the dense gas is not heavily
affected once the cloud manages to shield this intense radiation field (e.g.,
O’Dell, 2001). In fact, large fractions of dense, undisturbed gas are still
detected in regions such as OMC-1 Ridge and Orion South at a distance of
($\sim$ 0.1-0.2 pc in projection) (e.g., Wiseman & Ho, 1998).

The results in Figure 7 suggest a fast evolution of the dense gas in the
targers included in our survey. The strong correlation between P stars and
N2H+ indicates that the dense gas survives at least the typical duration of
the protostellar (class 0/I) phase with $\sim$0.5 Myr. On the other hand, the
random distribution of D stars indicates that this correlation is lost at the
typical timescales of $\sim$2 Myr for this older (Class II) YSO population.
Analogous trends are reported in other filamentary clouds such as Taurus
(Hartmann et al., 2001, comparing CO maps with the position of YSOs) and
Perseus (Hacar et al., 2017b, using a similar analysis based on N2H+
observations). The combination of the above results suggests that the typical
evolutionary timescales of the dense gas currently observed in N2H+ is
therefore fast and within $\sim$1 Myr. A similar fast evolution for the dense,
star-forming gas traced by N2H+ has been recently proposed from simulations
(Priestley et al., 2023). Within these timescales, a small fraction of the
dense gas would be either accreted onto stars following its small SFE(dense)
(Sect. 5.1.3) while most of the remaining dense gas will be recycled by the
turbulent motions inside these regions (e.g., Padoan et al., 2016). The
diversity of environments and regions explored here rules out exotic ejection
mechanisms (e.g., Stutz, 2018). Instead, this fast evolution appears to be
driven by the continuous assembly of dense gas within these regions and its
subsequent local destruction once star-formation is ignited in them. In
contrast to more traditional quiescent scenarios (e.g., quasi-static
fragmentation), these results depict a dynamical evolution for typical star-
forming regions such as Orion in agreement with recent simulations (Padoan et
al., 2016; Smith et al., 2020; Seifried et al., 2020; Ibáñez-Mejía et al.,
2022).

###  5.2 High-resolution observations: cloud sub-structure at 2000 au
resolution

Figure 8: High-resolution observations in OMC-3. From left to right and from
top to bottom: (a) ALMA interferometric-alone (int-CLEAN) continuum map
(4$\aas@@fstack{\prime\prime}$5); (b) WISE 3.4 $\mu$m emission (6.1”); (c) gas
kinetic temperature, $T_{\mathrm{K}}$ (30”), (d) N2H+ (1-0), (e) HNC (1-0),
and (d) HC3N (10-9) integrated intensity maps obtained in our ALMA+IRAM-30m
MACF observations (4$\aas@@fstack{\prime\prime}$5). Symbols are similar to
those in Fig. 1. Similar plots for all the other regions can be found in
Appendix B. The enhanced resolution of our ALMA+IRAM-30m maps (panels a, d-f)
can be seen from their comparison with the gas $T_{\mathrm{K}}$ map (panel c)
displayed at the single-dish resolution.

Our ALMA observations provide a new perspective of the gas structure down to
$\sim$ 2000 au resolution (or 4$\aas@@fstack{\prime\prime}$5 at the distance
of Orion). In analogy to our low-resolution data (Sect. 5.1), in Fig.8 we
illustrate the high-resolution 3-mm continuum (panel a), as well as the
integrated emission maps of N2H+ (1-0) (panel d), HNC (1-0) (panel e), and
HC3N (10-9) (panel f) lines, respectively, observed by ALMA in the OMC-3
region. Similar plots for all the other targets in our sample can be found in
Figs. 19-24 in Appendix B.

The interferometric-alone (int-CLEAN) 3mm-continuum maps such in Fig. 8a show
a combination of two different emission mechanisms in our targets. We find
large emission areas with fluxes of $>$10 mJy beam-1 are detected in both
OMC-1 (Fig.19a) and Flame Nebula (Fig.24a) coincident with their bright
emission nebulae and contaminated by the free-free (bremssthrahlung) emission
in these HII regions up to 100 GHz (see Mason et al., 2020, for a discussion).
Outside these areas, the 3mm emission is dominated by the thermal dust
continuum reaching values of $\lesssim$ 5 mJy beam-1. In fields such as OMC-3,
we identify multiple bright, compact sources coincident with the position of
previously identified millimeter compact objects (Takahashi et al., 2013),
YSOs (Megeath et al., 2012), and protoplanetary disks (van Terwisga et al.,
2019), all of them unresolved in our maps. We detect a more extended cloud
emission, with fluxes of $\lesssim$ 2 mJy beam-1, towards column densities of
N(H2) $\gtrsim 50\times 10^{21}$ cm-2 showing a large number of filamentary
structures with sizes of few 104 au. Because of this limited sensitivity,
however, regions such as OMC-4 (Fig.21a) or NGC 2023 (Fig.23a) become mostly
undetected in our survey. On the other hand, we identify clear negative side-
lobes in regions with extended and bright emission such as OMC-1/2/3.
Additional short-spacing information in continuum (via SD data) is thus needed
to carry out a full analysis of these data products121212We remark that,
similar to our spectral observations (see Appendix A for a discussion), the
interferometric filtering of large-scale emission can artificially reduce the
continuum emission at small scales. The lack of short-spacing information
therefore reduces the effective dynamic range and sensitivity of our int-CLEAN
maps to recover the true dust continuum emission..

The enhanced dynamic range of our molecular ALMA+IRAM-30m (MACF) maps provides
a more complete picture of the gas distribution in our targets. The N2H+ (1-0)
emission above $\gtrsim$ 1 Jy beam-1 km s-1 ($>$7 K km s-1) shows in a
plethora of small-scale, elongated and narrow structures in all our targets
(Fig.8d). The previous characterization of the N2H+ emission in OMC-1 and
OMC-2 (see Figs.19d and 20d) identified these filamentary features as
velocity-coherent, sonic fibers (Hacar et al., 2018). The analogous emission
features seen in all our maps suggests a similar fiber organization of the
dense gas in all targets in our sample (see Socci et al., 2024, for a full
characterization). More striking, this filamentary gas organization continues
into the more diffuse material traced by HNC (1-0) at $\gtrsim$ 1 Jy beam-1 km
s-1 ($\gtrsim$ 7 K km s-1; Fig.8e) sometimes running perpendicular to the
above dense fibers. Yet, the complexity and emission properties of the gas
change across our sample: dense regions such as OMC-3 (Fig.8e-d) and LDN 1641N
(Fig.22e-d) show bright and highly contrasted emission in both N2H+ and HNC,
while more diffuse clouds such as OMC-4 (Fig.21e-d) and NGC 2023 (Fig.23e-d)
present systematically weaker and shallower emission in these tracers. In
contrast, we find the HC3N (10-9) emission $\gtrsim$ 0.15 Jy beam-1 km s-1
($>$1 K km s-1) to be typically more clumpy and concentrated towards local
regions with dense gas (showing N2H+ emission) directly exposed to some
stellar feedback (either by nebulae or YSOs) as seen in OMC-3 (Fig.8f) or
Flame Nebula (Fig.24f) although the lack of ALMA observations of this tracer
(as well as HNC) in OMC-1 and OMC-2, both with bright emission in our SD maps
(Figs.13 and 14), prevent drawing further conclusions on the origin of its
emission.

Figure 8 also highlights the ability of our molecular maps to reproduce the
internal gas distribution at high spatial resolutions. OMC-3 appears as highly
extincted, MIR-dark region showing multiple narrow structures in the WISE 3.4
$\mu$m image in Fig.8b (see Juvela & Mannfors, 2023, for a detailed discussion
on the MIR absorption in OMC-3). We find a direct spatial correlation (both in
location and size) between these MIR absorption features and the emission
distribution of our N2H+ maps (Fig.8d). These similarities extend to the more
diffuse gas where we identify several “whisker-like” features in the WISE
image (see northern part of OMC-3) also detected in HNC (Fig.8e). Although
less clear, we also observe these similarities in the case of NGC 2023
(Fig.23). The nature of these diffuse gas features remains unclear at this
point. Yet, this unique correspondence with the MIR absorption reinforces the
potential of our N2H+ and HNC (ALMA+IRAM-30m) maps to describe the fine gas
substructure within our cloud sample with high fidelity.

The inspection of our molecular ALMA+IRAM-30m (MACF) maps reveals a new and
complex cloud substructure at high spatial resolution. This is particularly
remarkable in comparison with previous single-dish observations. Regions such
as OMC-3 and Flame Nebula have been identified as single, elongated (and
broad) filaments using low spatial resolution, continuum (Schuller et al.,
2021; Könyves et al., 2020) and line (Orkisz et al., 2019; Gaudel et al.,
2023) observations. Under the sharper eyes of ALMA, however, the gas
organization in all our fields is clearly more complex, nebulous, and wispy,
and sometimes still unresolved by our $\sim$ 2000 au beamsize.

Both low- and high-mass star-forming regions exhibit a strong fibrous and
gaseous sub-structure. Far from idealize cylinders, the size, width, radial
distributions, and morphology of these slender fibers vary both locally and
between regions. Yet, these fiber properties appear to vary smoothly across
our sample showing no discontinuity between low- and high-mass star-forming
regions. Obtaining high-resolution, high dynamic range ALMA observations is
therefore not only one of the main goals of the EMERGE project but also
crucial to correctly interpret the ISM structure leading to the formation of
all types of stars. We will characterize the physical properties of these gas
substructure in following papers of this series (e.g., Socci et al., 2024) as
well as compare these results with similar ongoing ALMA surveys (e.g.,
Sanhueza et al., 2019; Anderson et al., 2021; Barnes et al., 2021; Motte et
al., 2022; Liu et al., 2023).

##  6 Conclusions

The EMERGE project aims to describe how high-mass stars and clusters originate
as part of the complex interactions in dense filamentary (fiber-like)
networks. This work (Paper I) introduces the EMERGE Early ALMA Survey and a
series of novel methodologies for data reduction and exploration of ALMA
observations. Accompanying papers will investigate the effects of filtering
and data combination on similar ALMA observations (Bonanomi et al., 2024,
Paper II) as well as characterize the physical properties of the dense fibers
found in these regions (Socci et al., 2024, Paper III). The main results of
this paper can be summarized as follow:

  1. 1.

As part of our EMERGE Early ALMA Survey we systematically investigated 7 star-
forming regions part of the Orion A and B clouds, namely OMC-1, OMC-2, OMC-3,
OMC-4 South, LDN 1641N, NGC 2023, and Flame Nebula (Sect.2). We homogeneously
surveyed this sample combining large-scale single-dish (IRAM-30m; 30”)
observations together with dedicated interferometric mosaics (ALMA-12m array,
Cycles 3+7; 4$\aas@@fstack{\prime\prime}$5) maps in both (3mm-)continuum and
density selective molecular lines (N2H+ (1-0), HNC (1-0), and HC3N (10-9)) in
Band 3. We complemented these data with additional archival FIR observations
and IR surveys (Sect.3).

  2. 2.

We developed an optimized framework for the massive and automatic data
reduction of our ALMA observations (Sect.4). This includes the paralellization
of the calibration, imaging, and combination process of single-dish and
interferometric (ALMA+IRAM-30m) observations carried out in high-performance
computers.

  3. 3.

The analysis of the large-scale properties of our targets demonstrates the
large variety of physical conditions sampled by our survey including low-
(OMC-4 South, NGC 2023), intermediate- (OMC-2, OMC-3, LDN 1641N) and high-mass
(OMC-1, Flame Nebula) star-forming regions covering a wide range of surface
density of star formation, gas column densities, fractions of dense gas,
temperatures, and evolutionary stages (Sect.5.1).

  4. 4.

Our suite of selective molecular tracers sample distinct gas regimes in our
sample. N2H+ (1-0) highlights the densest and coldest gas in our targets at
column densities $\gtrsim 20\times 10^{21}$ cm-2. HNC (1-0) efficiently traces
the cloud material at low- and intermediate densities down to $\sim 5\times
10^{21}$ cm-2. On the other hand, HC3N (10-9) is connected to lukewarm
temperatures in regions exposed to feedback (Sect.5.1.2). Among the three
tracers, N2H+ (1-0) appears as the best descriptor of the star-forming gas
leading to the formation of protostars at constant efficiency (Sect.5.1.3) and
within timescales of $\sim$ 1 Myr (Sect.5.1.4).

  5. 5.

When observed in the (low-resolution) Herschel column density maps (36”) our
targets appear clumpy and filamentary. Similar structures are recognized in
the N2H+ and HNC (1-0), IRAM-30m maps ($\sim$30” or $\sim$ 12 000 au) showing
total intensities that positively correlate with the total gas column density
and kinetic temperatures of these regions (Sect.5.1.2).

  6. 6.

In contrast, our (high-resolution) ALMA+IRAM-30m observations
(4$\aas@@fstack{\prime\prime}$5 or $\sim$ 2000 au) provide a new perspective
of the gas structure within these star-forming regions (Sect.5.2). At the
enhanced interferometric resolution the gas organization in all our targets
exhibits an increasing level of complexity down to the beamsize of ALMA.
Dozens of small-scale, dense fibers can be recognized in the dense gas traced
by their N2H+ (1-0) emission similar to those previously identified in OMC-1
and OMC-2. Additional filamentary features are seen in more diffuse material
traced by the HNC emission.

  7. 7.

The gas organization observed in our high-resolution ALMA maps suggest the
common presence of dense fibers in all targets in our sample of our sample
irrespective of their gas content (mass of diffuse vs dense gas), stellar
density (isolated vs clustered), star-formation activity (low- vs high-mass),
and evolutionary state (young vs evolved). The properties of these fibers will
be fully characterized in other papers of this series (e.g., see Paper III).
These results highlight the hierarchical nature of the ISM showing filaments
down to $\sim$2000 au resolution.

###### Acknowledgements.

This project has received funding from the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation programme
(Grant agreement No. 851435). M.T. acknowledges partial support from project
PID2019-108765GB-I00 funded by MCIN/AEI/10.13039/501100011033. D.H. is
supported by Center for Informatics and Computation in Astronomy (CICA) grant
and grant number 110J0353I9 from the Ministry of Education of Taiwan. D.H.
acknowledges support from the National Technology and Science Council of
Taiwan through grant number 111B3005191. J.R.G. thanks the Spanish MCIU for
funding support under grant PID2019-106110GB-I00. This paper makes use of the
following ALMA data: ADS/JAO.ALMA#2019.1.00641.S.,
ADS/JAO.ALMA#2013.1.00662.S. ALMA is a partnership of ESO (representing its
member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST
and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the
Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO and
NAOJ. This work is based on IRAM-30m telescope observations carried out under
project numbers 032-13, 120-20, 060-22, and 133-22. IRAM is supported by
INSU/CNRS (France), MPG (Germany), and IGN (Spain). This research has made use
of the SIMBAD database, operated at CDS, Strasbourg, France. This research has
made use of NASA’s Astrophysics Data System. This work was carried out on the
Dutch national e-infrastructure with the support of SURF Cooperative. The
authors acknowledge assistance from Allegro, the European ALMA Regional Center
node in the Netherlands.

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##  Appendix A Assessing data quality in interferometric observations

###  A.1 On the effects of the short-spacing information

Figure 9: Effects of the different data combination methods in OMC-3: (Top)
N2H+ (1-0), (Middle) HNC (1-0), and (Bottom) HC3N (10-9) integrated intensity
maps. Different columns show the results for the (Left) interferometric-alone
(int-CLEAN), (Centre) feather (Feather), and (Right) Model Assisted Cleaning
plus Feather (MACF) data combination methods, respectively. To facilitate
their comparison all integrated intensity maps of the same molecule are
displayed with the same intensity scale (see colorbars). The location of the
spectra shown in Fig. 11 are indicated by cyan crosses in all maps. Compared
to our standard PB cut above 0.5 (Sect.4.1), these maps are shown down to PB
values of 0.2 in order to identify potential issues at their edges (e.g., HNC
MACF map). We remark here that these edges issues do not affect our scientific
results as they are removed when our stricter PB cut is applied. Similar
features can be identified in all the ALMA fields observed in these three
tracers shown in Figs. 25-28.

As recognized in multiple studies in the literature (see Leroy et al. 2021;
Plunkett et al. 2023, and references therein), the lack of short-spacing
information fundamentally impacts the recovery of extended emission in
interferometric observations. We illustrate the severity of these issues in
real observations in Fig. 9 by comparing different high spectral resolution
N2H+ (1-0), HNC (1-0), and HC3N (10-9) maps in OMC-3 obtained using the int-
CLEAN, Feather, and MACF data combination methods (see descriptions in
Sect.4.1). Similar comparisons for all the other targets observed in these
tracers can be found in Figs. 25-28. An eye inspection of these integrated
intensity maps reveal how interferometric-alone (int-CLEAN; left panels)
reductions misses most of the line emission of all our tracers. These
filtering effects are particularly prominent in the case of molecules with
extended and bright emission, such as N2H+ and HNC (top and bottom panels),
easily recognized by the presence of prominent negative sidelobes surrounding
large emission features. Less obvious, filtering also affects more compact
emission features such as those seen in HC3N (bottom panels) down to the ALMA
resolution. Similar issues are seen in all targets of our sample.

The addition of the short-spacing information significantly changes the amount
of recovered emission in all our spectral maps. Both Feather (central planels)
and MACF (right panels) reductions reveal significant levels of extended
emission detected in bright tracers such as N2H+ (top panels). Yet, data
combination improves the flux recovery of both compact and extended emission
features seen. The most dramatic improvements are seen however in our HNC maps
(middle panels), the most diffuse tracer in our sample (see Sect.5.1.2).
There, Feather and MACF manage to recover large amounts of extended emission
systematically filtered our by the interferometer. Little or no sign of
remaining negative sidelobes are observed in these combined maps.

The above improvement on the recovery of extended emission after data
combination can be understood from the uv-coverage in our ALMA+IRAM-30m
observations. In Figure 10 we show the uv-sampling of our ALMA-12m data (blue)
in OMC-3 within the inner 50 meters of the uv-plane. The large number of
baselines in the ALMA-12m array produces a densely sampled coverage of uv-
plane above (projected) distances of $\geq$ 12 meters. On the other hand, the
inner gap in the uv-plane is uniformly sampled by our IRAM-30m data (grey
area) up to 30 meters and with a sufficient overlap with the ALMA-12m array in
C43-1 configuration (hatched area). While ALMA provides unprecedented
sensitivity at large baselines ($\sim$12-300 meters in our case), the
inclusion of the IRAM-30m data as short-spacing information becomes essential
for recovering the extended emission in these targets. As demonstrated in
Paper II (Bonanomi et al. 2024), the homogeneous uv-sampling provided by a
large SD such as IRAM-30m produces a significant improvement on the image
quality during the study of the filamentary ISM compared to the more sparse
coverage obtained using equivalent ALMA Compact Array (ACA; sampling
intermediate uv-distances) plus total power (TP; sampling the innermost
baselines) observations.

Figure 10: ALMA-12m array uv-coverage (blue), with effective baselines between
$\sim$ 12 and 300 meters (C43-1), obtained in our OMC-3 observations. We
highlight the uv-area sampled by the IRAM-30m data ($\leq$ 30 m; grey) as well
as its overlap with the ALMA baselines (hatched area). Note that we display
only those baselines at u,v-distances $\leq$ 50 meters.

###  A.2 Statistical results

Table 4: Quality assessment results

|  | OMC-3 | Flame Nebula | NGC 2023
---|---|---|---|---
Transition | Method | A-par(⋆) | FR(⋆⋆) | A-par | FR | A-par | FR
N2H+ (1-0) | int-CLEAN | -0.79$\pm$0.04 | 18.8% | -0.72$\pm$0.06 | 35.3% | -0.90$\pm$0.02 | 4.7%
(narrow) | Feather | -0.02$\pm$0.05 | 90.6% | -0.13$\pm$0.05 | 84.9% | -0.07$\pm$0.01 | 91.4%
| MACF | -0.00$\pm$0.03 | 99.5% | -0.08$\pm$0.03 | 91.7% | -0.02$\pm$0.01 | 97.0%
HNC (1-0) | int-CLEAN | -0.94$\pm$0.01 | 6.3% | -0.94$\pm$0.01 | 8.6% | -0.97$\pm$0.02 | 4.5%
(narrow) | Feather | -0.01$\pm$0.03 | 94.6% | -0.03$\pm$0.03 | 95.7% | -0.05$\pm$0.04 | 93.6%
| MACF | -0.03$\pm$0.01 | 99.3% | 0.00$\pm$0.02 | 99.1% | -0.01$\pm$0.03 | 98.1%
HC3N (10-9) | int-CLEAN | -0.69$\pm$0.03 | 32.9% | -0.59$\pm$0.01 | 43.7% | -0.72$\pm$0.04 | 32.7%
(narrow) | Feather | -0.16$\pm$0.01 | 83.5% | -0.04$\pm$0.03 | 91.6% | -0.16$\pm$0.03 | 80.6%
| MACF | -0.07$\pm$0.01 | 92.9% | 0.00$\pm$0.02 | 98.1% | -0.09$\pm$0.02 | 87.1%
HNC (1-0) | int-CLEAN | -0.94$\pm$0.01 | 6.3% | -0.94$\pm$0.01 | 8.6% | -0.97$\pm$0.01 | 4.4%
(broad) | Feather | -0.02$\pm$0.07 | 93.0% | -0.02$\pm$0.03 | 94.3% | -0.03$\pm$0.02 | 93.6%
| MACF | -0.07$\pm$0.06 | 84.9% | 0.02$\pm$0.02 | 89.9% | 0.01$\pm$0.02 | 88.5%
HC3N (10-9) | int-CLEAN | -0.79$\pm$0.03 | 24.0% | -0.59$\pm$0.05 | 43.6% | -0.67$\pm$0.04 | 38.1%
(broad) | Feather | -0.27$\pm$0.04 | 72.2% | -0.09$\pm$0.03 | 86.4% | -0.18$\pm$0.02 | 81.0%
| MACF | -0.20$\pm$0.02 | 75.6% | -0.04$\pm$0.04 | 92.4% | -0.11$\pm$0.02 | 90.5%
131313($\star$) Mean $\pm$ standard deviation A-par values. ($\star\star$)
Percentage of the total flux recovered (FR) respect to the one detected in our
single-dish data. We note that most of the best results are usually produced
by the MACF method.

Beyond the visual comparisons in maps and spectra (see above), we performed a
quantitative analysis of the QA of our molecular datasets implementing the
assessment metrics presented in Plunkett et al. (2023). In particular, we
focused our assessments on the analysis of the Accuracy parameter (A-par):

$A_{\nu}(x,y)=\frac{I_{\nu}(x,y)-R_{\nu}(x,y)}{|R_{\nu}(x,y)|}.$ (1)

Intuitively, A-par measures the relative error (in percentage) between a
reference ($R_{\nu}(x,y)$) and a target ($I_{\nu}(x,y)$) image while its sign
indicates whether the flux is overestimated ($A_{\nu}(x,y)>0$) or
underestimated ($A_{\nu}(x,y)<0$). A-par can be evaluated in space (x,y) and
velocity ($\nu$) and applied to maps and cubes alike within a dedicated
assessment region (see Plunkett et al for a full discussion).

In the case of our molecular observations, we adopt our IRAM-30m datasets as
reference and evaluate the performance of the target int-CLEAN, Feather, and
MACF reductions. For that, we convolved and regridded each combined map
($\theta=$4$\aas@@fstack{\prime\prime}$5 resolution) into the single-dish
frame ($\theta=$30”) and applied an assessment threshold of approximately five
times the noise level in our ALMA maps. From the statistical analysis of
$A_{\nu}(x,y)$ we then aim to quantitatively measure the amount of flux
recovered by our data combinations in comparison with those found in the
single-dish data, assumed to represent the true sky flux distribution at least
at the single-dish resolution. Reduced with the same deconvolution parameters,
our QA allows us to systematically compare the goodness of each data reduction
method applied in our study (Sect. 4.1).

We summarize the results of our QA in Table 4 in three representative regions
in our sample selected by presenting different line intensities and source
structure: OMC-3 (bright and extended), Flame Nebula (compact and bright), and
NGC 2023 (weak and extended). We display the values of A-par statistics (mean
and standard deviation) obtained from the analysis of all voxels within the
assessment area as well as the fraction of total flux recovered (FR) in the
final combined datacubes of our three lines of interest (N2H+, HNC, and HC3N).
We display the results for both high- (top three rows; narrow) and low-
(bottom two rows; broad) spectral resolution.

Our QAs confirm the trends suggested during the visual inspection of our maps
(Fig. 9). All interferometric-alone reductions (int-CLEAN) are subject of
widespread filtering missing $>$70% of the flux both per pixel (A$<$-0.70) and
in absolute (FR$\lesssim$40%) terms. On the contrary, data combination
techniques (Feather and MACF) exhibit an significant improved performance
combining an enhanced of the local accuracy (—A—$\lesssim$0.10) and flux
recovery (FR$\gtrsim$85%) in most targets and lines. Data combination is
particularly relevant in regions with bright, extended emission (e.g., OMC-3
and Flame Nebula). Nonetheless, data combination shows some limitations in
targets with either sharp features (e.g. HC3N in OMC-3) or weak extended
(e.g., HC3N in NGC 2023) emission, probably connected to the quality of our
single-dish data. Regardless of these minor issues, our QAs statistically
quantify the great impact of advanced data combination techniques during the
reduction of ALMA observations (see also Plunkett et al. 2023).

The differences between them Feather and MACF are more subtle. The results in
Table 4 indicate some systematic differences between the Feather and MACF
results. Overall, MACF produces the most stable results per pixel with A-par
values close to —A—$\lesssim$0.05 and a better total flux recovery
(FR$>92\%$). In some cases, MACF appears to produce noisier edges, although
this effect is easily mitigated by a sensible PB cut after combination (Sect.
4.1). Based on these results, and for consistency with the analysis in OMC-1
and OMC-2 (Hacar et al. 2018), we have adopted the MACF method as our standard
reduction. Yet, we remark that in specific cases Feather may produce similar
or even slightly better (by few percent) results.

While both Feather and MACF produce satisfactory results, we find systematic
improvements on their data products when comparing high- (narrow) and low-
(broad) spectral resolution reductions (see results of HNC and HC3N in Table
4). Reductions including single-dish data at high spectral resolutions
consistently increase the accuracy (A-par) and flux recovery (FR) of the
combined datasets. Resolving the line structure (and its possible filtering in
different channels) seems to help on recovering high fidelity images. Further
analysis are needed to confirm this trend.

###  A.3 Velocity-dependent filtering effects in molecular line observations

Figure 11: ALMA spectra obtained using int-CLEAN (black), Feather (dashed
blue), and MACF (red) data combination methods. We display the (narrow) N2H+
(1-0) (Top), HNC (1-0) (Middle), and HC3N (10-9) (Bottom) spectra (rows) in
three representative positions (columns) within the OMC-3 region (see Fig. 9):
(Left panels) ($\alpha,\delta$)=(05:35:19.6,-05:00:29.5), (Cental panels)
($\alpha,\delta$)=(05:35:25.5,-05:04:19.0), and (Right panels)
($\alpha,\delta$)=(05:35:20.7,-05:01:11.9). Note how the filtering effects
experienced in the int-CLEAN maps are unpredictable and change depending on
position, velocity, and tracer consider in each case.

Plunkett et al. (2023) discussed how the lack of short-spacing information can
have a critical impact on the recovered emission both in space and velocity
(i.e. spectral cubes). We illustrate these effects in Fig. 11 by comparing the
int-CLEAN (blue), Feather (dashed black), and MACF (red) spectra in three
representative positions in our OMC-3 region. As seen in the hyperfine
structure of our N2H+ spectra (top panels), filtering can act selectively in
velocity changing the emission profile of all our lines independently of their
brightness.

Figure 12: HNC (1-0) line properties derived from the automatic fit of one
gaussian component in OMC-3 obtained from our int-CLEAN (blue points) and
Feather (yellow points) reductions (targets) in comparisons with MACF. For
simplicity, only fits with I(HNC, MACF)$\geq 0.5$ Jy beam-1 and S/N$\geq$5 are
shown. From left to right: (Left panel) Line peak ratio; (Central panel) Full-
Width-Half-Maximum ratio; (Right panel) Velocity difference. The lower
envelope of the central panel is determined by the spectral resolution of our
(narrow) reductions with $\delta$V=0.25 km s-1.

Filtering can critically alter all line properties (line flux and peak,
centroid and FWHM, hyperfine distribution…) and distort any physical property
derived from them. This is clear in many int-CLEAN spectra exhibiting
physically unrealistic hyperfine ratios, artificially narrow FWHM, and/or show
negative artefacts. To explored the effects of filtering on the line
parameters we automatically fitted one gaussian component to all HNC (1-0)
spectra in OMC-3 obtained by our different data combination methods. In Fig.12
we show a pixel-by-pixel comparison of the peak temperature (Tpeak; left
panel), Full-Width-Half-Maximum (FWHM; central panel), and line velocity
(VLSR; right panel) between the int-CLEAN and Feather results with respect to
MACF, the latter assumed as reference. As expected from the results in Sect.
A.2, we find an excellent agreement between the line properties derived in
Feather and MACF methods with most variations within the spectral resolution
(e.g., see FWHM). The results of the int-CLEAN reduction are however dramatic.
Interferometric filtering produces significant changes not only in the line
peak (similar to the flux), but also completely corrupts the recovered FWHM
and VLSR parameters. As shown from the comparison in velocity (right panel),
pure filtering artefacts can alter the entire velocity structure of the cloud
producing artificially narrow profiles and systematic drifts across the field.

Selective filtering effects can be also tracer dependent. The different
brightness, velocity distribution, and emission structure of lines and
continuum produces differential effects on the recovered emission at the same
position. This is seen in Fig. 11 when comparing N2H+ (top panels) and HNC
(middle panels) spectra in similar positions. The bright and extended HNC
distribution across the entire OMC-3 cloud completely suppresses the emission
of this molecule in different parts of the cloud more efficiently than in the
case of N2H+ (e.g., left middle panel). Other regions can not compensate the
negative sidelobes coming from nearby bright spots and produce more prominent
negative features (e.g., right middle panel). We remark here that these
effects are also seen in the weaker HC3N spectra, despite this molecule
showing a more compact distribution (see bottom panels).

We also notice that large-scale filtering produced by sources with extended
and complex emission structure can be highly deceptive. Negative sidelobes can
be compensated by bright emission features at similar velocities producing
almost flat spectra (e.g. Fig. 11, left middle panel). Analysis in such
spectra could lead into measurements showing artificially low noise values
giving the false impression of high quality data reductions. On the contrary,
Feather and MACF reductions actually show bright emission features in many of
these positions. These effects are apparent in the case of our continuum int-
CLEAN maps. There it is clear that most of the continuum emission is filtered
out while our maps show a surprisingly low-noise level.

The above maps (Sect. A.1) and spectra (this section) illustrate the perils of
using interferometric-alone reductions for the analysis of spatially resolved
sources. Our results discourage the use of interferometer-alone reductions
(int-CLEAN) when characterizing the emission properties of (resolved) physical
structures larger than few times the interferometric beamsize. Projects
exploring similar targets are strongly encouraged to carefully evaluate the
impact of the missing short-spacing information in both maps and spectra. In
an accompanying paper (Bonanomi et al. 2024) we quantify how the lack of the
single-dish information severely corrupts the characterization of the physical
properties (masses, sizes, widths, etc…) of cores and filaments in our ALMA
observations. Data combination becomes therefore essential for our line
emission and kinematic studies in our ALMA sample.

##  Appendix B Data products

In this appendix we present all the data products of our EMERGE Early ALMA
survey in OMC-1, OMC-2, OMC-4 South, LDN 1641N, NGC 2023, and Flame Nebula
(see also Figs. 4 and 8 for those on OMC-3). We include all the integrated
intensity and continuum maps of our IRAM-30m and ALMA observations together
with the additional Herschel and WISE data. We display the individual
molecular maps of each of these regions using low spatial resolution (single-
dish) observations in Figs. 13-18 (see Sect.5.1). Similar maps for the high
spatial resolution (ALMA+IRAM-30m) datasets can be found in Figs. 19-24 (see
Sect.5.2). Complementing Fig. 9, we display the comparisons between the
different data combination methods for all our ALMA fields observed in N2H+
(top panels), HNC (middle panels), and HC3N (lower panels) in Figs. 25-28. To
facilitate the comparison between targets we display all regions within the
same plotting ranges in each of the panels.

Figure 13: Low-resolution observations in OMC-1 similar to Fig. 4 Figure 14:
Low-resolution observations in OMC-2 similar to Fig. 4 Figure 15: Low-
resolution observations in OMC-4 South similar to Fig. 4 Figure 16: Low-
resolution observations in LDN 1641N similar to Fig. 4 Figure 17: Low-
resolution observations in NGC 2023 similar to Fig. 4 Figure 18: Low-
resolution observations in Flame Nebula similar to Fig. 4 Figure 19: High-
resolution observations in OMC-1 similar to Fig. 8. Note that no ALMA maps are
available in the case of the HNC (1-0) nor HC3N (10-9) lines (see Sect. 3.1).
Figure 20: High-resolution observations in OMC-2 similar to Fig. 8. Note that
no ALMA maps are available in the case of the HNC (1-0) nor HC3N (10-9) lines
(see Sect. 3.1).  Figure 21: High-resolution observations in OMC-4 South
similar to Fig. 8.  Figure 22: High-resolution observations in LDN 1641N
similar to Fig. 8.  Figure 23: High-resolution observations in NGC 2023
similar to Fig. 8.  Figure 24: High-resolution observations in Flame Nebula
similar to Fig. 8.

Figure 25: Comparison of the different data combination methods in OMC-4
South, similar to Fig. 9

Figure 26: Comparison of the different data combination methods in LDN 1641N,
similar to Fig. 9

Figure 27: Comparison of the different data combination methods in NGC 2023,
similar to Fig. 9

Figure 28: Comparison of the different data combination methods in Flame
Nabula, similar to Fig. 9