11institutetext:  Universität Wien, Institut für Astrophysik,
Türkenschanzstrasse 17, 1180 Wien, Austria
11email<EMAIL_ADDRESS>Université de Bordeaux,
Lab. d’Astrophysique de Bordeaux, CNRS, B18N, Allée Geoffroy Saint-Hillaire,
33615 Pessac, France 33institutetext: European Southern Observatory, Karl-
Schwarzschild-Strasse 2, 85748 Garching bei München, Germany 44institutetext:
Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg,
Germany 55institutetext: Universidade do Porto, Faculdade de Engenharia, Rua
Dr. Roberto Frias, 4200-465 Porto, Portugal 66institutetext: Universidade de
Lisboa, Faculdade de Ciências, CENTRA, Ed. C8, Campo Grande, P-1749-016
Lisboa, Portugal 77institutetext: Universidad de Valparaíso, Instituto de
Física y Astronomía, Gran Bretaña 1111, Valparaíso, Chile 88institutetext:
Leiden University, Leiden Observatory, Niels Bohrweg 2, 2333 CA Leiden, The
Netherlands 99institutetext: University of Hertfordshire, Centre for
Astrophysics Research, College Lane, Hatfield AL10 9AB, UK 1010institutetext:
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA
1111institutetext: The CHARA Array of Georgia State University, Mount Wilson
Observatory, Mount Wilson, CA 91023, USA 1212institutetext: Università degli
Studi di Milano, Dipartimento di Fisica via Celoria 16, I-20133 Milano, Italy
1313institutetext: Universidad de Chile, Departamento de Astronomía, Las
Condes, Santiago, Chile 1414institutetext: Terma GmbH, Bratustraße 7, 64293
Darmstadt, Germany 1515institutetext: European Space Agency (ESA), European
Space Research and Technology Centre (ESTEC), Keplerlaan 1, 2201 AZ Noordwijk,
The Netherlands 1616institutetext: Universidad de Antofagasta, Centro de
Astronomía (CITEVA), Av. Angamos 601, Antofagasta, Chile 1717institutetext:
Johannes Kepler University Linz, Industrial Mathematics Institute,
Altenbergerstraße 69, A-4040 Linz, Austria 1818institutetext: Johann Radon
Institute for Computational and Applied Mathematics (RICAM), Austrian Academy
of Sciences (AAS), Altenbergerstr. 69, A-4040 Linz, Austria 1919institutetext:
University of Vienna, Research Network Data Science at Uni Vienna, Austria
2020institutetext: INAF – Osservatorio Astrofisico di Arcetri, Largo E. Fermi,
5, 50125, Firenze, Italy 2121institutetext: University of St Andrews, SUPA,
School of Physics & Astronomy, North Haugh, St Andrews KY16 9SS, UK
2222institutetext: Space Telescope Science Institute, 3700 San Martin Dr,
Baltimore, MD 21218, USA

# VISIONS: The VISTA Star Formation Atlas

I. Survey overview††thanks: Based on observations collected at the European
Southern Observatory under ESO program 198.C-2009.

Stefan Meingast VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star
Formation Atlas João Alves VISIONS: The VISTA Star Formation AtlasVISIONS: The
VISTA Star Formation Atlas Hervé Bouy VISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation Atlas Monika G. Petr-Gotzens VISIONS:
The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Verena
Fürnkranz VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star
Formation Atlas Josefa E. Großschedl VISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation Atlas David Hernandez VISIONS: The
VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Alena
Rottensteiner VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star
Formation Atlas Magda Arnaboldi VISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation Atlas Joana Ascenso VISIONS: The VISTA
Star Formation AtlasVISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA
Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Amelia Bayo
VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation
Atlas Erik Brändli VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA
Star Formation Atlas Anthony G.A. Brown VISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation Atlas Jan Forbrich VISIONS: The VISTA
Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Alyssa Goodman
VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas
Alvaro Hacar VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star
Formation Atlas Birgit Hasenberger VISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation Atlas Rainer Köhler VISIONS: The VISTA
Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Karolina Kubiak
VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas
Michael Kuhn VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star
Formation Atlas Charles Lada VISIONS: The VISTA Star Formation AtlasVISIONS:
The VISTA Star Formation Atlas Kieran Leschinski VISIONS: The VISTA Star
Formation AtlasVISIONS: The VISTA Star Formation Atlas Marco Lombardi VISIONS:
The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Diego
Mardones VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star
Formation Atlas Laura Mascetti VISIONS: The VISTA Star Formation AtlasVISIONS:
The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation AtlasVISIONS:
The VISTA Star Formation Atlas Núria Miret-Roig VISIONS: The VISTA Star
Formation AtlasVISIONS: The VISTA Star Formation Atlas André Moitinho VISIONS:
The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Koraljka
Mužić VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation
Atlas Martin Piecka VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA
Star Formation Atlas Laura Posch VISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation Atlas Timo Prusti VISIONS: The VISTA
Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Karla Peña Ramírez
VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas
Ronny Ramlau VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star
Formation AtlasVISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star
Formation Atlas Sebastian Ratzenböck VISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation Atlas Germano Sacco VISIONS: The VISTA
Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Cameren Swiggum
VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas
Paula Stella Teixeira VISIONS: The VISTA Star Formation AtlasVISIONS: The
VISTA Star Formation Atlas Vanessa Urban VISIONS: The VISTA Star Formation
AtlasVISIONS: The VISTA Star Formation Atlas Eleonora Zari VISIONS: The VISTA
Star Formation AtlasVISIONS: The VISTA Star Formation Atlas Catherine Zucker
VISIONS: The VISTA Star Formation AtlasVISIONS: The VISTA Star Formation Atlas

(Received 22 December 2022 / Accepted 19 January 2023)

VISIONS is an ESO public survey of five nearby
($d<500\,$\mathrm{p}\mathrm{c}$$) star-forming molecular cloud complexes that
are canonically associated with the constellations of Chamaeleon, Corona
Australis, Lupus, Ophiuchus, and Orion. The survey was carried out with the
Visible and Infrared Survey Telescope for Astronomy (VISTA), using the VISTA
Infrared Camera (VIRCAM), and collected data in the near-infrared passbands
$J$ ($1.25\text{\,}\mathrm{\SIUnitSymbolMicro m}$), $H$
($1.65\text{\,}\mathrm{\SIUnitSymbolMicro m}$), and $K_{S}$
($2.15\text{\,}\mathrm{\SIUnitSymbolMicro m}$). With a total on-sky exposure
time of $49.4\text{\,}\mathrm{h}$ VISIONS covers an area of
$650\text{\,}{\mathrm{deg}}^{2}$, it is designed to build an infrared legacy
archive with a structure and content similar to the Two Micron All Sky Survey
(2MASS) for the screened star-forming regions. Taking place between April 2017
and March 2022, the observations yielded approximately 1.15 million images,
which comprise $19\text{\,}\mathrm{T}\mathrm{B}$ of raw data. The observations
undertaken within the survey are grouped into three different subsurveys.
First, the wide subsurvey comprises shallow, large-scale observations and it
has revisited the star-forming complexes six times over the course of its
execution. Second, the deep subsurvey of dedicated high-sensitivity
observations has collected data on areas with the largest amounts of dust
extinction. Third, the control subsurvey includes observations of areas of
low-to-negligible dust extinction. Using this strategy, the VISIONS
observation program offers multi-epoch position measurements, with the ability
to access deeply embedded objects, and it provides a baseline for statistical
comparisons and sample completeness – all at the same time. In particular,
VISIONS is designed to measure the proper motions of point sources, with a
precision of $1\text{\,}\mathrm{mas}\text{\,}{\mathrm{yr}}^{-1}$ or better,
when complemented with data from the VISTA Hemisphere Survey (VHS). In this
way, VISIONS can provide proper motions of complete ensembles of embedded and
low-mass objects, including sources inaccessible to the optical ESA Gaia
mission. VISIONS will enable the community to address a variety of research
topics from a more informed perspective, including the 3D distribution and
motion of embedded stars and the nearby interstellar medium, the
identification and characterization of young stellar objects, the formation
and evolution of embedded stellar clusters and their initial mass function, as
well as the characteristics of interstellar dust and the reddening law.

###### Key Words.:

surveys, stars: pre-main sequence, stars: formation, stars: kinematics and
dynamics, ism: clouds

##  1 Introduction

We are currently witnessing a new golden age of astronomy by virtue of the
availability of an impressive array of powerful telescopes stationed both in
space and on Earth. The swift technological developments of the last few
decades have led to the deployment of increasingly complex astronomical
instrumentation. Equipped with such advanced instruments, said telescopes now
enable astronomers and data scientists alike to make new discoveries on an
unprecedented scale. In consequence, a topic that has seen tremendous advances
over the past decades is related to the formation of stars, a process that
regulates cosmic evolution from small to large scales and governs cosmology,
galaxy evolution, planet formation, and the origin of life.

Within the field of star formation, we have found certain empirical rules that
generally connect the attributes of molecular clouds with the properties of
their transformation process to new stars. Yet, despite all advances, we still
lack a quantitative theory that, given a limited set of initial conditions and
measured properties of molecular clouds, is capable of predicting the star
formation rate and the basic physical properties of stars. Arguably, the most
critical unsolved problems confronting star formation research today are:
understanding star formation in the context of the Galaxy and its components,
the origin of the stellar mass distribution, the physical processes that
control the star formation rate in molecular gas, and young stellar object
(YSO) evolution, together with its implications for planet formation, as well
as the origin and evolution of stellar clusters.

The innovations of the last decades have brought a wealth of new data to the
community at infrared and (sub-)millimeter wavelengths, where the star
formation process can be observed directly. Some of the main contributors to
the ever-increasing observational archives are the Very Large Telescope and
the Atacama Large Millimeter/submillimeter Array operated by the European
Southern Observatory (ESO), as well as the Gaia (Gaia Collaboration et al.
2016), Hubble, JWST (Gardner et al. 2006), WISE (Wright et al. 2010), Spitzer
(Werner et al. 2004), Herschel (Pilbratt et al. 2010), and Planck (Planck
Collaboration et al. 2011), space observatories. With this tremendous amount
of available data, an obvious miss in the landscape of currently available
astronomical surveys is a deep, wide-field, near-infrared (NIR) high-image
quality survey that not only enables the characterization of YSOs and their
surrounding environment on a scale of $100\text{\,}\mathrm{A}\mathrm{U}$ but,
at the same time, it also allows for a global and consistent probing of YSO
dynamics.

With thousands of YSOs distributed over several giant molecular cloud
complexes, the solar neighborhood represents one of the regions in our local
galaxy that are particularly rich in star formation sites. In a recent study,
Zucker et al. (2022) indicated that this plethora of nearby star formation
sites could itself be the result of feedback processes originating from a
previous generation of young stars. Our proximity to these sites of star
formation constitutes a fortunate circumstance, as it is only in these
environments that we possess the capability to spatially resolve individual
young stars, monitor the properties of gaseous filaments in high resolution,
and directly observe feedback mechanisms that regulate the rate at which new
stars are born.

The VISIONS survey program was established as a natural continuation of the
Vienna Survey in Orion project (VISION; Meingast et al. 2016), an observing
program focused on the characterization of dust properties and the YSO
population of the Orion A molecular cloud. With VISIONS, we provide a survey
of five nearby ($d<500\,$\mathrm{p}\mathrm{c}$$) star-forming complexes that
are observable from the southern hemisphere. The names of the regions are
derived from the constellations with which they are associated: Chamaeleon,
Corona Australis, Lupus, Ophiuchus, and Orion (for an overview of the star-
forming regions, see, e.g., Reipurth 2008a, b). In Figs. 1 and 2, we showcase
color images of particularly striking objects observed in the VISIONS program,
depicting young stars and their dusty surroundings in various shapes and
evolutionary stages. VISIONS is aimed at building a legacy NIR archive,
thereby extending the data products provided by the Two Micron All Sky Survey
(2MASS; Cutri et al. 2003; Skrutskie et al. 2006) to probe the contents of the
star-forming molecular clouds across the entire stellar and substellar mass
spectrum. As such, the survey will also constitute a valuable resources for a
new generation of instruments (e.g., MOONS; Cirasuolo et al. 2020). Moreover,
VISIONS is designed specifically to be complementary to the Gaia mission. The
survey will connect the Gaia astrometric frame to the embedded nearby stellar
populations, making it possible to derive proper motions of deeply embedded
YSOs to study their dispersal into the galactic field. Being complementary to
existing and future observations across the entire electromagnetic spectrum,
VISIONS will bring new insights into the processes that govern the
transformation of clouds composed of gas and dust into stars and planets.

In this manuscript, we first give a description of the instrumentation and
technical details of the survey in Sect. 2, before elaborating on the
scientific goals in Sect. 3. Furthermore, we adopt the terminology established
by the European Southern Observatory (ESO). For example, a single snapshot of
all detectors of the camera used is called a ”pawprint”. From a series of such
pawprints a contiguous image, called a ”tile”, can be constructed. Likewise,
the integration time for a single snapshot of the camera is called the
detector integration time (DIT), which can be stacked NDIT times for one
readout.

Figure 1: Ongoing star formation processes as observed by VIRCAM/VISTA in the
VISIONS program, using color images assembled from the near-infrared passbands
$J$, $H$, and $K_{S}$. The top panel shows L1688 in the Ophiuchus star-forming
complex, depicting several dozen prominent young stellar objects located
within one of the closest embedded clusters to Earth. The young stars are
surrounded by a highly structured cloud composed of gas and dust that is
largely being shaped by young, optically revealed hot stars in the vicinity.
The bottom panels further highlight the complex morphology of the gaseous,
dusty neighborhood of partly concealed young stars in the Ophiuchus molecular
clouds L1709 and L1689.

Figure 2: In the top panels, images of the Lupus 2 and Lupus 3 complexes
illustrate the intricate and diverse morphology of dust clouds. The panels in
the center portray parts of the Chamaeleon region, which includes young stars
that form spectacular cloud shapes, featuring large, cone-shaped reflection
cavities. In the bottom-left panel, the Coronet cluster in the Corona
Australis star-forming complex is seen illuminating large parts of the
surrounding gas and dust. In the bottom right panel, the star-disk-envelope
system of a deeply embedded young star becomes visible. By tracing the dust
cavity toward the lower left of the image, a faint part of an outflow, ejected
from the protostar, can be distinguished.

##  2 Survey description

In this section, we provide a brief overview of the instrumentation used to
collect survey data, followed by a detailed description of the design
philosophy of the survey, together with its implementation, requirements, and
scheduling.

###  2.1 Instrumentation

The VISIONS survey uses the Visible and Infrared Survey Telescope for
Astronomy (VISTA, Emerson et al. 2006), which is integrated into the Cerro
Paranal facilities operated by ESO in Chile. Although the VISTA telescope is
located on a separate peak from the nearby Very Large Telescope, the typical
observing conditions are very similar. The site is ideal for observations at
infrared wavelengths and offers photometric observing conditions (no visible
clouds; transparency variations less than 2%, Kerber et al. 2016) on about one
out of four nights, with a median seeing of $0.66\text{\,}\arcsec$ and
precipitable water vapor of $2\text{\,}\mathrm{mm}$ or lower for
$50\text{\,}\mathrm{\char 37\relax}$ of the nights111Information on the
astroclimate of the observing site was taken from
www.eso.org/sci/facilities/paranal/astroclimate/site.html.

With a $4\text{\,}\mathrm{m}$ aperture, VISTA delivers an f/3.25 beam at the
Cassegrain focus. There, the VISTA infrared camera (VIRCAM; Dalton et al.
2006) images a field of view with $1.65\text{\,}\mathrm{deg}$ in diameter. The
camera houses sixteen 2k $\times$ 2k Raytheon VIRGO HgCdTe detectors with a
typical quantum efficiency of 90 – $95\text{\,}\mathrm{\char 37\relax}$
between 1 and $2.5\text{\,}\mathrm{\SIUnitSymbolMicro m}$. The detectors are
arranged in a sparse 4 $\times$ 4 pattern and individually cover a
$11.6\leavevmode\nobreak\ \times\leavevmode\nobreak\
11.6\,$\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{m}\mathrm{i}\mathrm{n}$$ field
with a mean pixel scale of
$0.339\text{\,}\arcsec\mathrm{/}\mathrm{p}\mathrm{i}\mathrm{x}\mathrm{e}\mathrm{l}$.
The intradetector gaps measure $\leavevmode\nobreak\
11.4\text{\,}\mathrm{arcmin}$ and $\leavevmode\nobreak\
4.9\text{\,}\mathrm{arcmin}$ in the X and Y axis of the system, respectively.
As a result, the camera captures an area of about
$0.6\text{\,}{\mathrm{deg}}^{2}$, albeit with large gaps between individual
images, with a single exposure. Consequently, the construction of a fully
contiguous tile requires a set of at least six individually shifted pawprints.
Across a co-added tile, this results in differences in field coverage, total
exposure time, and exact observation date. The effective exposure of a tile
can be calculated with
$t_{\mathrm{exp}}=2\times\mathrm{DIT}\times\mathrm{NDIT}\times\mathrm{NJITTER}$,
where NJITTER is the number of jittered positions per offset. The factor of 2
in this equation is a consequence of the offset pattern in which, disregarding
additional jitter positions, most of the areas of a tile are observed twice.
The process of jittering is used in the interest of enhancing the quality of
the post-processed data: The detectors show a large number of bad pixels (up
to $2\text{\,}\mathrm{\char 37\relax}$), many of which appear clustered rather
than randomly distributed. To facilitate the effective rejection of bad pixels
during co-addition, a jitter pattern can be executed at each offset position.
It should also be noted that the detectors suffer from non-linearity of the
order of up to a few percent at
$10\,000\text{\,}\mathrm{A}\mathrm{D}\mathrm{U}$.

VISIONS is one of the seven ESO VISTA cycle 2 public surveys (Arnaboldi et al.
2019). The observations are executed in service mode (also referred to as
queue scheduling mode) to collect data in the (NIR) $J,H,\mathrm{and}$ $K_{S}$
passbands, with mean wavelengths of 1.25, 1.65, and
$2.15\text{\,}\mathrm{\SIUnitSymbolMicro m}$ and a full width at half maximum
(FWHM) of 0.17, 0.29, and $0.31\text{\,}\mathrm{\SIUnitSymbolMicro m}$,
respectively. For more detailed information on the telescope and camera, we
refer the reader to the numerous resources available on the ESO website and
specifically to the VIRCAM user
manual222www.eso.org/sci/facilities/paranal/instruments/vircam.html.

###  2.2 Survey setup

Figure 3: Star-forming regions observed in the VISIONS survey plotted on top
of Planck data at $857\text{\,}\mathrm{GHz}$. The individual colors of the
marked regions portray the subsurveys of the program. The filled blue tiles
represent the coverage of the wide program, whereas the red and green
rectangles indicate the on-sky locations of data collected as part of the deep
and control observations, respectively. Science verification data, collected
in the region of the Orion star forming complex, is depicted in orange.

To meet the scientific goals of the VISIONS survey (Sect. 3) several aspects
must be considered, as not all science cases can be addressed with a single
observation strategy. For instance, studies on the embedded stellar
populations have different observation requirements than a survey with the aim
of measuring proper motions. Hence, for regions without a significant amount
of extinction, shallow and relatively fast observation sequences can deliver
high-quality, multi-epoch data for large areas on the sky. On the other hand,
parts of the clouds exhibiting large column densities require longer
integration times to both reach deeply embedded sources and observe a
sufficiently large number of background stars to construct well-sampled
reddening maps at the same time. To achieve an efficient distribution of the
available observing time, the VISIONS program is divided into three
independent but complementary subsurveys, namely: the wide, deep, and control
subsurveys. A summary of the locations, the observation setup, and other
metrics for each region can be found in Table 1.

Table 1: VISIONS observation setup. This table contains data on the number of images, data volume, on-sky area, and the imaging setup for each sub-survey. Region | $l$ | $b$ | Tiles | Tiles | Pawprints | Volume | Area | DIT | NDIT | NJITTER | Exp. time | Exp. time | Sky offset | Filter
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
|  |  | single | total | total |  |  |  |  |  | single | total |  |
| (deg) | (deg) | (#) | (#) | (#) | (GB) | (deg2) | (s) | (#) | (#) | (s) | (h) |  |
$Wide$
Chamaeleon | 298.6 | -16.1 | 51 | 306 | 9180 | 2464 | 84.5 | 3 | 2 | 5 | 60 | 5.1 | ✗ | $H$
Chamaeleon (Musca) | 301.5 | -7.7 | 5 | 30 | 900 | 242 | 8.8 | 3 | 2 | 5 | 60 | 0.5 | ✗ | $H$
Corona Australis | 360.0 | -18.8 | 25 | 150 | 4500 | 1208 | 41.4 | 3 | 2 | 5 | 60 | 2.5 | ✗ | $H$
Lupus | 337.9 | 12.3 | 70 | 420 | 12600 | 3382 | 115.9 | 3 | 2 | 5 | 60 | 7 | ✗ | $H$
Ophiuchus | 353.3 | 18.4 | 96 | 576 | 17280 | 4639 | 159.0 | 3 | 2 | 5 | 60 | 9.6 | ✗ | $H$
Orion | 209.9 | -19.8 | 114 | 684 | 20520 | 5508 | 188.8 | 3 | 2 | 5 | 60 | 11.4 | ✗ | $H$
Epoch $X$ | - | - | 20 | 120 | 3600 | 966 | - | 3 | 2 | 5 | 60 | 2 | ✗ | $H$
Subtotal |  |  | 381 | 2286 | 68580 | 18409 | 598.4 |  |  |  |  | 36.1 |  |
$Deep$
Chamaeleon | 296.9 | -15.5 | 2 | 12 | 216 | 58 | 3.5 | 5/2/2 | 10/25/25 | 3 | 300 | 1 | ✓ | $JHK_{S}$
Corona Australis | 359.9 | -17.8 | 1 | 6 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 3 | 300 | 0.5 | ✓ | $JHK_{S}$
Lupus North | 339.0 | 16.2 | 1 | 6 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 3 | 300 | 0.5 | ✓ | $JHK_{S}$
Lupus South | 339.5 | 9.3 | 1 | 6 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 3 | 300 | 0.5 | ✓ | $JHK_{S}$
Ophiuchus | 353.8 | 16.8 | 4 | 24 | 432 | 116 | 6.8 | 5/2/2 | 10/25/25 | 3 | 300 | 2 | ✓ | $JHK_{S}$
Orion (w/ sky) | 209.1 | -19.4 | 2 | 10 | 312 | 84 | 3.5 | 5/2/2 | 9/17/15 | 6/5/5 | 540/340/300 | 1.0 | ✓ | $JHK_{S}$
Orion (w/o sky) | 211.5 | -19.4 | 9 | 27 | 702 | 188 | 14.8 | 5/2/2 | 8/27/20 | 3/5/5 | 240/540/400 | 3.0 | ✗ | $JHK_{S}$
Pipe | 357.4 | 6.9 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Subtotal |  |  | 21 | 94 | 2094 | 562 | 35.8 |  |  |  |  | 9.0 |  |
$Control$
Chamaeleon | 308.7 | -15.6 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Corona Australis | 3.6 | -17.9 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Lupus North | 344.1 | 16.2 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Lupus South | 333.1 | 9.2 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Ophiuchus North | 345.3 | 16.8 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Ophiuchus South | 348.8 | -16.7 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Orion | 233.3 | -19.4 | 1 | 3 | 78 | 21 | 1.8 | 5/2/2 | 8/27/20 | 3/5/5 | 240/540/400 | 0.33 | ✗ | $JHK_{S}$
Pipe North | 352.1 | 6.6 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Pipe South | 1.2 | -3.8 | 1 | 3 | 108 | 29 | 1.8 | 5/2/2 | 10/25/25 | 6 | 600 | 0.5 | ✗ | $JHK_{S}$
Subtotal |  |  | 9 | 27 | 942 | 253 | 16.2 |  |  |  |  | 4.33 |  |
Total |  |  | 411 | 2407 | 71616 | 19224 | 650.4 |  |  |  |  | 49.4 |  |

The targeted star-forming regions span sizes on the order of tens of parsecs.
However, since they are relatively near to Earth (100 – $400$
$\mathrm{p}\mathrm{c}$), their contiguous gas distributions cover hundreds of
square degrees in the sky. VIRCAM images an area of about
$1.8\text{\,}{\mathrm{deg}}^{2}$ on a minimum of two different pixels with an
observing sequence that includes the six offsets and at least one additional
jitter position per offset. Yet, even with such a large field of view, several
hundred tiles are required to cover the areas of interest. Figure 3 shows the
on-sky coverage of all subsurveys, superimposed on a view of the Milky Way as
seen by Planck at $857\text{\,}\mathrm{GHz}$333Data obtained from the Planck
Legacy Archive, which is accessible at pla.esac.esa.int/pla, in a
stereographic projection centered on $l=280\,$\mathrm{deg}$$, and
$b=0\,$\mathrm{deg}$$. For each region, the figure depicts wide, deep, and
control fields in blue, red, and green, respectively. As described below, the
deep and control fields for Orion are not part of VISIONS itself, as these
data have already been collected, processed, and published by Meingast et al.
(2016). The yellow boxes mark the VISTA science verification fields, which
cover large parts of the Orion B molecular cloud complex (e.g., Petr-Gotzens
et al. 2011, program ID: 60.A-9285).

####  2.2.1 Wide subsurvey

Figure 4: Multi-epoch imaging data for the high proper motion star 2MASS
19205383-3950043. The leftmost panel shows 2MASS observations captured in
1998, whereas the middle panel displays data from the VHS survey in the $J$
passband from 2011 and the rightmost panel shows VISIONS $H$-band data
collected in August 2019. The crosses mark the star’s position in each epoch
and the white solid line depicts its trajectory. Also visible in the VHS and
VISIONS images is another source with an offset toward the Galactic West of
about $5\text{\,}\arcsec$, which is not discernible in 2MASS data due to the
shallower sensitivity limit of the survey.

The goal of the wide survey is to provide data for large areas over the course
of six epochs, which will facilitate proper motion measurements for sources
beyond the sensitivity limit of the Gaia mission. We selected different survey
areas for each region to cover their distribution of gas and young stars. For
Ophiuchus, the survey covers $159\text{\,}\deg\mathrm{{}^{2}}$, comprising the
greater Rho Ophiuchi cloud complex with the molecular clouds Barnard 42
through 47, 51, 57, 229, and 238. Furthermore, the field extends several
degrees to the Galactic North, also including the Upper Scorpius OB
association (de Zeeuw et al. 1999). The Lupus wide field measures
$115.9\text{\,}\deg\mathrm{{}^{2}}$ and contains the molecular clouds Lupus I
through IV. In the Galactic South, this field borders the VVVX public survey
limit at $b=5\,$\deg$$ (Minniti et al. 2010; Minniti 2018). In the Corona
Australis region, VISIONS encompasses $41.4\text{\,}\deg\mathrm{{}^{2}}$,
containing the Coronet cluster and the surrounding low column-density gas. In
Chamaeleon, a contiguous field of about $84.5\text{\,}\deg\mathrm{{}^{2}}$
connects observations for the clouds Chamaeleon I, II, and III, while a
separate field ($\leavevmode\nobreak\ 9\text{\,}\deg\mathrm{{}^{2}}$) images
the Musca filament, which is situated close to the Galactic plane. Orion
constitutes the largest wide survey area with
$188.8\text{\,}\deg\mathrm{{}^{2}}$. This field comprises the giant molecular
clouds Orion A and Orion B, as well as further outlying, oftentimes cometary-
shaped clouds, found far to the Galactic east and south. In addition to the
regular wide fields, which have been observed six times over the $\sim$
$5\,$\mathrm{y}\mathrm{r}$$ duration of the survey program, we have scheduled
an additional epoch at the very end of the VISIONS program for the deep
subsurvey pointings. This addition, called Epoch X, guarantees the longest
possible time baselines for the deeply embedded population. Summing all
individual fields, the wide survey area measures
$598.4\text{\,}\deg\mathrm{{}^{2}}$, for which we have requested observations
of a total of $2286\text{\,}\mathrm{t}$iles, comprising
$68\,580\text{\,}\mathrm{V}$IRCAM pawprints or
$1\,097\,280\text{\,}\mathrm{i}$ndividual images.

Figure 5: Maximum available time baseline between VISIONS and VHS
observations. Each panel shows a close-up view of the star-forming complexes,
as imaged in the wide subsurvey, on top of a Planck $857\text{\,}\mathrm{GHz}$
map. The colorscale shows the time difference between the first VHS
observations and the last VISIONS wide field, mapped onto a healpix grid with
a pixel size of
$47\text{\,}\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{m}\mathrm{i}\mathrm{n}^{2}$.
Time intervals of less than $3\text{\,}\mathrm{y}\mathrm{r}$ are indicated in
red. A distinct pattern becomes visible, depicting the complex observation
strategy of both surveys.

The images for this subsurvey were taken exclusively in the $H$ passband in
order to complement the VISTA Hemisphere Survey (VHS; McMahon et al. 2013, ESO
program ID 179.A-2010), a first-generation VISTA public survey. Over its
runtime from 2009 to 2022, VHS mapped the entire southern hemisphere
(DEC$\leavevmode\nobreak\ <0\leavevmode\nobreak\ $\deg$$) in the $J$ and
$K_{S}$ passbands, with an effective tile exposure time of
$60\text{\,}\mathrm{s}$. Combined, the VISIONS wide and VHS programs can
provide the full set of NIR passbands, which are already well known from
2MASS. Furthermore, similarly to the VHS data collection strategy, we have
favored relatively short integration times to efficiently cover large parts of
the star-forming regions over multiple epochs. Consequently, a single tile of
our wide survey has a total effective exposure time of $60\text{\,}\mathrm{s}$
and is built from from five jittered positions at each offset, with
DIT$=3\leavevmode\nobreak\ $\mathrm{s}$$ and NDIT$=2$. However, in contrast to
VHS, which includes only two jittered positions with longer DITs, we have
preferred a greater number of individual images over longer single integration
times, to ensure a proper rejection of bad pixels during co-addition.
Altogether, the wide subsurvey constitutes the largest part of the VISIONS
program, with a total of $36.1\text{\,}\mathrm{h}$ of on-sky exposure time.

In addition to providing photometry in the $J$ and $K_{S}$ passbands, VHS also
presents a significant extension of the observed time baseline, a parameter
highly relevant for measuring proper motions. As a first-generation VISTA
public survey, VHS began collecting data in the VISIONS fields during ESO
Period 85, with the first observations executed on March 23, 2010 in Lupus. As
a consequence, complementing VISIONS with VHS potentially offers a baseline of
more than $10\text{\,}\mathrm{yr}$, facilitating high-precision proper motion
measurements. For bright sources, this baseline can even be extended by
another $10\text{\,}\mathrm{yr}$ by including observations from 2MASS. To
illustrate the effects of combining VISIONS with the two older surveys to
create longer baselines, Figure 4 shows the case of the star 2MASS
19205383-3950043, for which Gaia has determined a proper motion of
$196.41\text{\,}\mathrm{mas}\text{\,}{\mathrm{yr}}^{-1}$. From left to right,
the panels in the figure indicate the position of the star as imaged by 2MASS,
VHS, and VISIONS, respectively. The star’s position in each epoch, provided by
Gaia, is marked with a cross whose color corresponds to the indicated
observing date in the top right corner of the respective panel. The solid
white line marks the trajectory computed from Gaia astrometry. The location of
the source in each image aligns well with the Gaia measurements and its
movement along the projected trajectory is clearly visible across the
different frames.

As data collection for VHS continued well into the phase of the second-
generation VISTA public surveys, the interval between VISIONS and VHS
observations is significantly shortened for large parts of the observed
fields. Figure 5 shows the sky coverage of VISIONS in a healpix grid with a
pixel size of
$47\text{\,}\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{m}\mathrm{i}\mathrm{n}^{2}$
(resolution order 9), where the color scale depicts the maximum available time
interval between VISIONS and VHS. The figure reveals a complex pattern in time
baselines caused by the discrete and often irregular observing schedule of
both public surveys. The spatial distribution of time intervals shows a
characteristic pattern aligned along lines of constant declination as a
consequence of the VHS observation sequence. In addition, superimposed on this
global pattern, some individual tiles become visible. These are caused, for
instance, by wide epoch X on the VISIONS deep fields or necessary repetitions
due to poor data quality. The red areas in the Ophiuchus and Orion regions
have VHS-VISIONS baselines that span less than
$3\text{\,}\mathrm{y}\mathrm{r}$. Regarding these regions, it should be noted
that at the time of the VISIONS observations, the corresponding fields in
Ophiuchus were not available from VHS, most likely because of difficulties
associated with the limitation of available stars for the autoguider and
active optics CCDs. As a consequence, we have included additional observation
blocks to collect these missing data in the $J$ and $K_{S}$ passbands, along
with our usual $H$-band observations. In Orion, the fields marked in red lie
above the declination limit for VHS ($\delta$ ¡
$0\text{\,}\mathrm{d}\mathrm{e}\mathrm{g}$), however, the corresponding areas
are still included in the wide observations, as this area has already been
covered with VIRCAM during the phase of VISTA science verification in 2009.

Figure 6 displays the VISIONS-VHS time baseline of the healpix grid shown in
Fig. 5 as a histogram. The blue-filled histogram shows the covered area in
$\deg\mathrm{{}^{2}}$ as a function of the time interval between VISIONS and
VHS. The red line represents the same data in cumulative form444The cumulative
sum of the area in Fig. 6 does not match the values listed in Table 1 because
a patch in Orion lies outside the area covered by VHS and is therefore not
counted in the histogram.. The multi-modal distribution in the blue histograms
is derived from the visibility of the regions, which is in turn dictated by
the discrete timings of the VISIONS wide schedule and the priorities assigned
to the individual observation blocks. The histograms reveal that about
$150\text{\,}\deg\mathrm{{}^{2}}$ of the observed star-forming regions have a
maximum time interval of only $4\text{\,}\mathrm{y}\mathrm{r}$ or less. Most
of these areas are associated with the Corona Australis and Chamaeleon fields.
Furthermore, we find that for half of the survey area the time interval is
approximately $6\text{\,}\mathrm{y}\mathrm{r}$ or less, with only a relatively
small fraction possessing baselines of more than
$10\text{\,}\mathrm{y}\mathrm{r}$. As a consequence, we expect the precision
of future proper motion measurements to vary significantly throughout the
various survey fields.

####  2.2.2 Deep subsurvey

The deep subsurvey of the VISIONS program targets areas characterized by high
column densities and aims to deliver highly sensitive observations,
penetrating deeply into the molecular clouds. For a precise field selection,
we inspected the local column density distribution in dust emission measured
by Planck at $857\text{\,}\mathrm{GHz}$, before arranging the field coverage
so that parts with high extinction and ongoing star formation are positioned
within the selected observed areas. For Ophiuchus, we included four adjacent
tile positions in a $2\times 2$ arrangement, covering, among others, the
prominent dark clouds L1688, L1689, and L1709 associated with the Rho Ophiuchi
cloud complex (Lynds 1962). In Lupus, two spatially separate tiles focus on
the clouds Lupus I and III, called Lupus North and Lupus South in the VISIONS
deep program. For Corona Australis, we observe the Coronet star cluster
together with the surrounding dense gas and in Chamaeleon two North-South
aligned tiles capture the Chamaeleon I dark cloud. Concerning the Orion cloud
complex, data regarding Orion A have already been collected by Meingast et al.
(2016), and Orion B measurements have been performed as part of the VISTA
science verification phase. In addition to the observations of the five star-
forming complexes in VISIONS, the deep subsurvey also targets Barnard 59, an
actively star-forming cloud at the Galactic northern end of the Pipe nebula.

Regarding the deep data, the observation setup aims to provide observations of
similar or better quality than the Orion A data from Meingast et al. (2016),
especially with respect to the sensitivity and resolution. We map the targeted
areas in $J$, $H$, and $K_{S}$, where all individual tile pointings feature a
total on-sky integration time of $600\text{\,}\mathrm{s}$ in each passband.
For scheduling purposes, all deep fields except Barnard 59 in the Pipe nebula,
are imaged twice, each with a $300\text{\,}\mathrm{s}$ tile exposure time and
sky offsets. The purpose of repeated measurements is to facilitate a more
reliable construction of a background model, which is a complex process due to
the presence of extended emission. The Pipe field is imaged without a sky
offset, reaching the target exposure time of $600\text{\,}\mathrm{s}$ with a
single observation block. For all fields, the detector integration time (DIT)
is set to $5\text{\,}\mathrm{s}$ in the $J$ band and $2\text{\,}\mathrm{s}$ in
$H$ and $K_{S}$ bands. Furthermore, the number of exposures with exposure time
DIT (NDIT) is set to 10 in $J$ and 25 in $H$ and $K_{S}$. With three jitter
positions and the obligatory six offsets per tile, the total integration time
then amounts to $300\text{\,}\mathrm{s}$. The number of individual tiles that
include (exclude) Orion amounts to 94 (57) constructed from 2094 (1080)
pawprints. Together, these data capture an area of 35.8
$(17.5)\,$\deg\mathrm{{}^{2}}$$ with a total on-sky exposure time of 9
$(5)\,$\mathrm{h}$$.

Figure 6: Distribution of the maximum available time interval between VHS and
VISIONS observations, constructed from the healpix grid in Fig. 5. The blue
histogram shows the captured area as a function of the available time
difference between the surveys. The red line displays the same statistic in
cumulative form. As a consequence of the broad, multi-modal range of available
time baselines, the accuracy of derived proper motions will vary accordingly.

####  2.2.3 Control subsurvey

The purpose of the control subsurvey is to provide data for regions without an
excess of extinction to complement the deep fields. Such observations enable
the sampling of stellar populations as well as of the extragalactic component
to facilitate a statistical comparison between field and star-forming regions.
For this reason, the control fields necessarily need to be located at the same
Galactic latitude as their science counterparts and should ideally also be
relatively close in terms of their Galactic longitude. However, choosing a
control field with such characteristics can be especially challenging for
regions in the vicinity of the Galactic Plane or projected against the Milky
Way bulge.

Similarly to the deep observations, we inspected the dust emission as captured
in the Planck $857\text{\,}\mathrm{GHz}$ band for the control field
selections. For Corona Australis and Chamaeleon, their relatively isolated
location with respect to other star-forming regions renders the choice
trivial, and for Orion, suitable data are already provided by Meingast et al.
(2016). For the Ophiuchus and Lupus North deep fields, we choose a region
between the two star-forming complexes at about $l\approx
345\,$\mathrm{d}\mathrm{e}\mathrm{g}$$, $b\approx
16.5\,$\mathrm{d}\mathrm{e}\mathrm{g}$$. For these regions, we further include
an additional control field since this area still exhibits a moderate amount
of extinction and, moreover, is projected against the Upper Scorpius and Upper
Centaurus Lupus OB associations. This additional control field, named
Ophiuchus South, is taken on a mirrored position in Galactic latitude with
respect to the first control field of the region, at
$l=348.8\,$\mathrm{d}\mathrm{e}\mathrm{g}$$,
$b=-16.7\,$\mathrm{d}\mathrm{e}\mathrm{g}$$. By coincidence, this position
also constitutes a suitable control field for Corona Australis. The Lupus
South control field is pointed toward a relatively extinction-free region in
the Galactic West of the Lupus III cloud. For Barnard 59 in the Pipe nebula,
there is no appropriate region with low extinction. To nevertheless provide a
reasonable estimate of the Galactic population, two separate tiles are imaged:
a field offset to the Galactic West and another imaging Baade’s window close
to the Galactic Center.

The observation strategy for the control subsurvey closely follows that of the
deep fields. Specifically, we used the same values for DIT, NDIT, and NJITTER,
while omitting a sky offset. With this setup, we obtain a total of 942 (864)
pawprints that include (exclude) Orion. The 27 (24) tiles capture an area of
16.2 $(14.4)\,$\mathrm{d}\mathrm{e}\mathrm{g}^{2}$$ with a total exposure time
of 4.33 $(4)\,$\mathrm{h}$$.

###  2.3 Scheduling and requirements

For optimal results regarding the wide survey’s goal of measuring proper
motions, we scheduled the observations so that the individual epochs would be
observed with an as long as possible time interval between them. In total, six
epochs were planned for each region, and their spacing was adjusted and
optimized for the visibility of a region over the course of a year. For
Ophiuchus, Lupus, and Chamaeleon, observations were performed during each ESO
observing semester. For Corona Australis and Orion, it was necessary to
include two epochs in one ESO period, the first at the start and the second
toward the end of the corresponding semester.

Figure 7 shows the distribution of observation times for all tiles throughout
the duration of the survey. The blue histogram represents the data collection
for the wide survey and observations for the other subsurveys are marked with
arrows. The figure highlights consecutive ESO Periods with alternating shades
of gray in the background of each panel. Originally, the VISIONS survey was
planned to last three years. However, due to incomplete observations and
necessary repetitions due to poor data quality, some data were taken after the
original survey period ended. Furthermore, during ESO period 105 (1 April to
30 September 2020), as well as for large parts of period 106, no data
collection was carried out due to the COVID-19 pandemic. As a consequence, the
end-to-end survey duration for VISIONS amounts to almost five years, with the
first and last exposures taken on April 1, 2017 and March 27, 2022,
respectively.

In contrast to the wide observations, we imposed less strict scheduling
requirements on the deep and control subsurveys. For these, we distributed the
observations throughout the planned duration of the survey and, when possible,
we also included the corresponding control field for each target region in the
same observation semester. For Chamaeleon, most of the data were taken in
April 2019, with a single observation block, namely, the second execution of
the northern field in $J$, imaged in January 2020. For Corona Australis and
Lupus, all deep data could be collected over a period of approximately two
weeks in April 2018. For Ophiuchus, most of the deep images were taken at the
beginning of the survey in April 2017, with two observation blocks in $J$
executed one year later. As already mentioned, all data for Orion within the
deep and control subsurveys were part of an earlier publication.

Figure 7: VISIONS observations over the course of the entire survey. The
individual panels show the data collection progress for each separate target
region. The blue histograms depict the number of imaged tiles in the wide
survey. The observing times for the deep and control surveys are marked with
red and green arrows at the top axis of each panel. The alternating gray
background refers to individual ESO periods, with period 99 indicating the
start of VISIONS observations on April 1, 2017.

Since the wide survey comprises by far the largest subsurvey and the execution
of individual observation blocks was time-critical, we imposed relatively
loose constraints on observing conditions. Specifically, we required an
airmass of less than 1.7, an image quality of $1\text{\,}\arcsec$ or better, a
minimum angular distance of $30\text{\,}\mathrm{d}\mathrm{e}\mathrm{g}$ to the
moon without any restriction on the moon phase, and a thin cirrus (sky
transparency variations above $10\text{\,}\mathrm{\char 37\relax}$) was
acceptable as well. For the deep and control fields, the same constraints
applied to the moon and the airmass. However, image quality was required to be
$0.8\text{\,}\arcsec$ or better, with clear (less than
$10\text{\,}\mathrm{\char 37\relax}$ of the sky above
$30\text{\,}\mathrm{d}\mathrm{e}\mathrm{g}$ elevation covered in clouds;
transparency variations under $10\text{\,}\mathrm{\char 37\relax}$) or
photometric (no visible clouds; transparency variations under
$2\text{\,}\mathrm{\char 37\relax}$) sky observing conditions. Observations
were also allowed during twilight for all subsurveys.

VISIONS did not require special calibration data, but it does rely on the
standard VISTA calibration plan, as detailed in the VISTA user
manual555www.eso.org/sci/facilities/paranal/instruments/vircam/doc.html. These
data include dark frames, twilight flats, and dome flats for determining the
non-linearity of the detector array, as well as a dedicated sequence for
calculating the read noise and gain levels of the detectors. Furthermore, the
survey does not require additional observations to determine zero points
because the processing pipeline calibrates all flux measurements with the aid
of 2MASS photometry.

###  2.4 Data processing, products, and dissemination

The standard processing pipeline for VIRCAM data, operated by the Cambridge
Astronomy Survey Unit (CASU666casu.ast.cam.ac.uk), is not the optimal choice
for surveys aiming to deliver the best possible image quality. As shown by
Meingast et al. (2016), the CASU pipeline exhibits two major drawbacks. First,
to construct a fully contiguous tile, the images captured by VIRCAM need to be
resampled onto a common reference frame. In the CASU pipeline, this process is
based on bilinear interpolation, which oftentimes degrades image quality, thus
lowering the output resolution by about 20%. Second, the photometric
calibration applies an extinction correction, rendering VIRCAM data regarding
star-forming regions provided by CASU difficult to interpret. At the same
time, CASU calibrates the data products toward the VISTA-specific photometric
system, requiring a color-correction to facilitate a combination with 2MASS
photometry. VISIONS strives to deliver both the highest-quality image products
and unbiased photometric properties that enable the determination of intrinsic
stellar colors. At the same time, our intention is to combine the VISIONS
source catalogs with 2MASS, making a strict calibration toward the 2MASS
photometric system an indispensable requirement. To achieve these goals, the
VISIONS team deploys a dedicated pipeline infrastructure at the University of
Vienna, where the core functionality has already been tested by Meingast et
al. (2016). A description of the data flow in the pipeline and a detailed view
of the functionality of individual modules is available in the second entry in
the series of VISIONS articles (Meingast et al. 2023).

Briefly summarized, the VISIONS pipeline produces science-ready images
comprising co-added tiles and stacks, as well as corresponding source
catalogs. Fluxes are calibrated with respect to 2MASS photometry in the Vega
system and Gaia Data Release 3 (DR3) is used as an astrometric reference (Gaia
Collaboration et al. 2022; Lindegren et al. 2021). For bright sources
($\lesssim 14\,$\mathrm{m}\mathrm{a}\mathrm{g}$$), photometric uncertainties
amount to about $5\text{\,}\mathrm{m}\mathrm{m}\mathrm{a}\mathrm{g}$, while
absolute position errors are of the order of
$10\text{\,}\mathrm{m}\mathrm{a}\mathrm{s}$ for individual sources on co-added
data products. Apart from on-sky positions and source magnitudes, the
published data will further include errors on positions and magnitudes, point
spread function shape descriptions (e.g., full width at half maxima and
ellipticities), and a series of quality flags indicating the reliability of
individual measurements. The final product content may vary between different
data releases, and detailed descriptions will be published simultaneously with
the data products themselves.

The intended release schedule comprises consecutive publications of the data
products, each stage introducing all observations for a particular star-
forming region. At the time of writing, the VISIONS team has processed and
published two data releases, which are available through the ESO Phase 3
Release Manager777eso.org/rm/publicAccess#/dataReleases. Data release 1
consists of a small set of products, only including deep data in the Ophiuchus
region. Data release 2 contains all observed pointings associated with the
Corona Australis molecular cloud complex. For the remaining star-forming
regions and the wide field observations in Ophiuchus, we plan to process the
observations and publish separate data releases. These future public releases
will be made available through the ESO archive, in accordance with the ESO
Science Data Products
Standard888www.eso.org/sci/observing/phase3/p3sdpstd_v6.pdf. We note here that
these releases do not include proper motion measurements since, for optimal
results, these require to also process VHS data in the VISIONS pipeline
framework.

##  3 Scientific objectives

The VISIONS program has collected multi-epoch NIR imaging data of five nearby
star-forming regions. These data products will facilitate the construction of
a sub-arcsecond image atlas in the $J$, $H$, and $K_{S}$ passbands,
accompanied by source catalogs. Additionally, the survey has visited each
region at least six times over its runtime, allowing for the computation of
precise stellar proper motions, as well as determining long-term variability.
We expect that the survey has collected data on about $10^{8}$ individual
sources in all star-forming complexes999The expected number of observed
individual sources is an extrapolation from VISIONS observations in the Corona
Australis star-forming region.. For the closest star-forming regions, VISIONS
observations are capable of capturing objects with masses only several times
that of Jupiter and deeply embedded young stellar objects at a physical
resolution of up to $100\text{\,}\mathrm{A}\mathrm{U}$. All data will be made
publicly available, allowing the community to address a range of scientific
questions.

###  3.1 Proper motions of young stars and low-mass objects

We have designed the VISIONS observations largely out of a motivation to
facilitate proper motion measurements of both the entire population of
embedded objects in molecular clouds and optically revealed young and low-mass
stars. With this strategy, our survey is to a great extent complementary to
the data provided by Gaia. Given it carries out observations in the optical
wavelength range, Gaia is unable to capture the vast majority of embedded
sources, either due to large amounts of foreground extinction or because of
extinction caused by circumstellar disks or envelopes. Moreover, low-mass
objects, such as brown dwarfs, also become increasingly difficult to detect
with Gaia during their evolution. To visualize this issue, Fig. 8 shows the
apparent magnitude of objects with masses between 0.01 and
$1\text{\,}\mathrm{M}_{\odot}$ for two different scenarios for the VISIONS $H$
and Gaia $G$ bands in red and blue, respectively. All masses and absolute
magnitudes of the objects are taken from Baraffe et al. (2015). In this
figure, the solid lines represent a young population (e.g., OB association) at
an age of $10\text{\,}\mathrm{M}\mathrm{y}\mathrm{r}$ and an extinction of
$A_{K}=0.1\,$\mathrm{m}\mathrm{a}\mathrm{g}$$. The dashed lines denote objects
in the Galactic field, without extinction and with an age of
$1\text{\,}\mathrm{G}\mathrm{y}\mathrm{r}$. To compute the apparent magnitudes
for both cases, we assumed a distance of $400\text{\,}\mathrm{p}\mathrm{c}$,
namely, the distance of the farthest star-forming complex in the VISIONS
survey, and we used the extinction law of Wang & Chen (2019). We also show the
hydrogen burning limit (marked with HBL at $0.07\text{\,}\mathrm{M}_{\odot}$)
as a dotted vertical line and the approximate sensitivity limits for both
surveys as dotted horizontal lines ($G_{\mathrm{lim}}\approx
20\,$\mathrm{m}\mathrm{a}\mathrm{g}$;H_{\mathrm{lim}}\approx
21\,$\mathrm{m}\mathrm{a}\mathrm{g}$$). We note here that the magnitude limit
in the Gaia survey depends on the scanning law and local source density and
can reach up to $21\text{\,}\mathrm{m}\mathrm{a}\mathrm{g}$ for selected areas
(e.g., Cantat-Gaudin et al. 2022).

Following Fig. 8, for the young embedded case (solid lines), Gaia reaches its
sensitivity limit at about a mass of $0.1\text{\,}\mathrm{M}_{\odot}$, whereas
the VISIONS $H$ observations theoretically are able to recover sources with
masses even below $0.01\text{\,}\mathrm{M}_{\odot}$ ($\approx
10\,$\mathrm{M}_{\mathrm{Jup}}$$). Sources at the hydrogen burning limit of
$0.07\text{\,}\mathrm{M}_{\odot}$ are easily detected at magnitudes of
approximately $H=16\,$\mathrm{m}\mathrm{a}\mathrm{g}$$ at this distance.
Nevertheless, low-mass objects in particular grow increasingly faint during
their evolution. In the second scenario depicted in Fig. 8, with regard to
field stars (dashed lines), sources near the hydrogen burning limit with ages
around $1\text{\,}\mathrm{G}\mathrm{y}\mathrm{r}$ have an $H$-band magnitude
of about $19\text{\,}\mathrm{m}\mathrm{a}\mathrm{g}$ and, at the same time,
they exhibit an optical brightness that is fainter by several orders of
magnitude – far below Gaia’s sensitivity limit. In this case, Gaia is able to
provide astrometry for objects with masses of $0.2\text{\,}\mathrm{M}_{\odot}$
or greater, whereas VISIONS still captures objects below the hydrogen burning
limit.

Based on preliminary tests, including data in the framework of VHS, we were
able to to calculate proper motions with a precision of the order of
$1\text{\,}\mathrm{mas}\text{\,}{\mathrm{yr}}^{-1}$ for bright sources
($H\lesssim 16\,$\mathrm{m}\mathrm{a}\mathrm{g}$$), or the equivalent
transverse velocity accuracy of
$0.5\text{\,}\mathrm{km}\text{\,}{\mathrm{s}}^{-1}$ at a distance of
$100\text{\,}\mathrm{p}\mathrm{c}$. These numbers are comparable to the
precision of the radial velocities of embedded objects from the APOGEE survey
(Majewski et al. 2017) in the Orion star formation region (e.g., Hacar et al.
2016; Großschedl et al. 2021). In addition, VISIONS will enable the
combination of its astrometric information with high-precision positional
information from radio observations. For embedded sources accessible with VLA,
VLBA, or ALMA, absolute astrometry down to the micro-arcsec-level will become
available, thereby further boosting the absolute calibration of the
astrometric reference frame and improving the already determined proper
motions.

Figure 8: Apparent magnitudes in the Gaia $G$ (blue) and VISIONS $H$ (red)
passbands as a function of mass for objects at a distance of
$400\text{\,}\mathrm{p}\mathrm{c}$. Solid lines represent a case of young
stars with an age of $10\text{\,}\mathrm{M}\mathrm{y}\mathrm{r}$ located in a
region with $A_{K}=0.1\,$\mathrm{m}\mathrm{a}\mathrm{g}$$ foreground
extinction. The dashed lines represent field stars at an age of
$1\text{\,}\mathrm{G}\mathrm{y}\mathrm{r}$ without any foreground extinction.
The dotted vertical and horizontal lines mark the hydrogen burning limit (HBL)
and the surveys’ sensitivity limits, respectively.

###  3.2 YSO identification and characterization

In our previous work, we showed that a redesigned data processing workflow can
improve the resolution of VIRCAM data products by approximately 20%, compared
to the products of other pipelines (Meingast et al. 2016). As a result of the
excellent resolution of the images of that study, it was possible to inspect
the morphology of YSO systems in Orion. In particular, the appearance of light
scattered off nebulae surrounding young stars proved to be a powerful tool for
confirming their protostellar nature. Großschedl et al. (2019) complemented
the VISTA/VIRCAM NIR images with data products from Spitzer, Herschel, and
WISE to perform a rigorous search for YSOs in the Orion A molecular cloud.
Their work produced the most complete and reliable sample of YSOs in this
region to date and validates the importance of NIR observations for
identifying young stars and detecting false positives. Furthermore, high-
resolution imaging products will allow for the estimation of various
properties of YSOs, such as the inclination of the envelope cavity, which is
critical for modeling YSO systems (Robitaille et al. 2006).

Outflows and high-velocity bipolar jets are further examples of features made
accessible by high-quality NIR imaging data. They are characteristic of the
early evolutionary stages of YSOs and represent an important instance of
feedback during the process of star formation. The high velocities of these
ejections (hundreds of $\mathrm{km}\text{\,}{\mathrm{s}}^{-1}$) allow for the
study of their morphological evolution and thus the possibility to gather
information on the mechanisms responsible for the creation of these objects.
In particular, VISIONS and VHS observations provide time baselines of up to
$10\text{\,}\mathrm{yr}$ or more that can be used to study outflow progression
(e.g., Reipurth & Bally 2001). Said baselines could even be extended by
another decade by including 2MASS data, although with a significantly reduced
image quality.

###  3.3 Cluster formation and the initial mass function

VISIONS observations are particularly effective in detecting young low-mass
objects. VISIONS can observe objects with masses of only a few
$\mathrm{M_{\mathrm{Jup}}}$ in all cloud complexes included in the survey and,
depending on their distance, even at ages above
$10\text{\,}\mathrm{M}\mathrm{y}\mathrm{r}$. Together with the available
astrometric information, the survey enables a robust identification of star
formation products across the entire mass spectrum (Fig. 8). Furthermore, the
detection of free-floating planets is within reach (Caballero 2018). As such,
with VISIONS it will become possible to more precisely investigate regional
variations of the mass function at the lowest substellar masses between and
within the star formation sites. At the same time, questions concerning the
effects of local feedback on the production of low-mass stars and the
connection between the maximum stellar mass and the cluster size can be
evaluated.

It is thought that the evolution of star clusters is primarily determined by
the early stages of their evolution. In this context, a number of parameters
and processes influence the fate of proto-clusters. This includes the number
of ejected objects and their masses, mass segregation processes, viralization,
the fraction of observed binaries, disk lifetimes, gas expulsion timescales,
and the effects of feedback. The quantification of these parameters is within
reach with the help of VISIONS data. For example, young wide binary systems
are particularly difficult to detect due to blending with fore- or background
sources. VISIONS observations are set to enable the determination of young
binary systems from both their co-moving nature from proper motions, and
spatially resolving the individual components. Moreover, the survey monitors
the closest embedded clusters to Earth, providing the best possible physical
resolution and allowing constraining model predictions and parameters.

###  3.4 3D maps and motion of the interstellar medium

Figure 9: Source density as function of extinction for the VISIONS, Gaia, and
2MASS missions, using data collected on the Orion A star-forming region. The
red and blue lines represent VISIONS wide and deep data, while the green and
yellow lines mark the Gaia and 2MASS surveys, respectively. Across all levels
of extinction, VISIONS outperforms the other surveys by up to an order of
magnitude.

Following the advent of Gaia, it has become possible to construct 3D maps of
the dust distribution in the nearby interstellar medium (e.g., Green et al.
2018; Lallement et al. 2019; Green et al. 2019; Leike et al. 2020; Lallement
et al. 2022). Prior to the availability of Gaia parallaxes on large scales,
Green et al. (2016) had already constructed a 3D map of the dust distribution
using Pan-STARRS (Kaiser et al. 2010) and 2MASS photometry, thereby
demonstrating that stellar photometry plays a vital role in correctly
inferring distances to stars. However, even the most current 3D dust maps,
constructed with Gaia, only manage to accurately map the low-density ISM and
the envelopes of star-forming complexes. With the upcoming data releases,
VISIONS will provide an accurate census of the embedded and background
populations of nearby cloud complexes, thus complementing Gaia and enabling a
more robust mapping of high-density ISM environments. To visualize the gain in
available background sources that can be used for measuring reddening due to
dust extinction, Fig. 9 displays the source density as a function of the
foreground extinction for VISIONS, 2MASS, and Gaia data. We calculate the
densities in a field that covers about
$20\text{\,}\mathrm{d}\mathrm{e}\mathrm{g}^{2}$ of the Orion A molecular
cloud, as published by Meingast et al. (2016). Gaia manages to capture about
twice as many sources as 2MASS at low extinctions, and only at
$A_{K}=0.5\,$\mathrm{m}\mathrm{a}\mathrm{g}$$ does 2MASS provide better
completeness than Gaia. Nevertheless, VISIONS outperforms both other surveys
by almost an order of magnitude across all extinction levels.

Furthermore, recent studies have managed to determine the 3D motion of nearby
star-forming gas. Specifically, Großschedl et al. (2021) used Gaia astrometry
and CO gas radial velocities to establish a connection between a subset of the
embedded YSOs in the Orion star-forming complex and the local 3D gas motion.
One major constraint of their study was the limited availability of proper
motion measurements of embedded stars from Gaia. VISIONS will enable measuring
the proper motions of young embedded objects, which, together with infrared
radial velocities (from e.g., APOGEE), will permit a far more reliable
determination of the 3D gas motion in the nearby ISM.

###  3.5 Properties of dense cores

VISIONS deep observations will provide source catalogs built from long
integration times, reaching about $6\text{\,}\mathrm{m}\mathrm{a}\mathrm{g}$
fainter sensitivity limits than 2MASS. These observations, combined with data
from WISE and Spitzer, will provide the best census of stars so far in the
background of dense cores. As a consequence, the construction of extinction
maps, reaching resolutions of 10 – $15\text{\,}\arcsec$ towards the bulge and
of 30–$60\text{\,}\arcsec$ towards the Galactic anticenter will become
possible (e.g., Meingast et al. 2018). These extinction maps will be free from
bias due to dust temperature, $\beta$ variations, gas excitation conditions,
or molecular depletion. In combination with Herschel dust emission
measurements, we expect to be able to construct a well-sampled core mass
function that can be compared with its stellar counterpart. Also, VISIONS will
enable comparing starless and star-forming cores, establishing a connection
between dense cores and YSOs, and mapping their distribution in the ensemble
of clouds to correlate them with the distribution of YSOs.

###  3.6 Dust properties and reddening law

The NIR wavelength regime is ideally suited for inferring the column-density
distribution of highly extincted areas. For such applications, extinction
mapping has not only been shown to be the most reliable method (Goodman et al.
2009), but also the reddening law in the NIR itself appears to show variations
of only a few percent in different environments (Meingast et al. 2018).

VISIONS will enable further testing of the universality of the NIR reddening
law, since the observed dust clouds are mostly isolated and allow for an
unbiased investigation free from background confusion. Furthermore, the
combination of extinction mapping with submillimeter dust emission data
obtained with Herschel and Planck will also provide constraints on the ratio
of the submillimeter dust opacity and the NIR extinction coefficient. Where
available, VISIONS, in combination with ground-based bolometer data, would
also allow evaluating the dust opacity spectral index $\beta$ (e.g., Forbrich
et al. 2015).

##  4 Summary

This manuscript serves as the main reference to the ESO public survey VISIONS:
The VISTA Star Formation Atlas. The survey targets five nearby star-forming
regions, namely Ophiuchus, Lupus, Chamaeleon, Corona Australis, and Orion.
VISIONS observations have been carried out with the VISTA telescope and its
integrated NIR camera VIRCAM, located at the ESO Paranal observatory in Chile.
The observation schedule, starting in April 2017 and lasting until March 2022,
was split into three different subsurveys: the wide, deep, and control
programs. They are targeted at collecting wide-field multi-epoch observations
in the $H$ band, highly sensitive $JHK_{S}$ imaging data of highly extincted
areas, and $JHK_{S}$ data on areas largely free of extinction, respectively.
While the wide observations facilitate proper motion measurements of objects
beyond the reach of the ESA Gaia mission, the deep and control data provide a
detailed view of the embedded populations. Furthermore, VISIONS has been
designed to be complementary to the first generation ESO VISTA public survey
VHS, which provides the $J$ and $K_{S}$-band observation for the wide
subsurvey. Additionally, using VHS extends the available time baseline for
proper motion measurements up to $10\text{\,}\mathrm{y}\mathrm{r}$ or more.

The main objective of VISIONS is to establish a legacy NIR archive that will
allow the community to improve our understanding of the process of star
formation. VISIONS will facilitate addressing a range of scientific topics,
including the determination of the kinematics of embedded objects down to
substellar masses, the reliable identification and characterization of YSOs,
the construction of an unbiased view of the formation and evolution of
embedded clusters, the verification of the products of star formation in
relation to the initial mass function, the support of the creation of 3D dust
maps and the deduction of 3D dynamics of the local interstellar medium, as
well as the reliable investigation of dust properties and the reddening law.
To achieve these goals, VISIONS is set to join ranks with prominent surveys
and missions, such as 2MASS, APOGEE, Spitzer, WISE, ALMA, and Gaia, to deliver
the most accurate view of the star formation processes in the solar
neighborhood.

###### Acknowledgements.

We thank the anonymous referee for the useful comments that helped to improve
this publication. We thank the ESO Survey Team and the Archive Science Group
for their helpful and constructive feedback and collaboration during the
survey’s preparation, execution, and data publication phases. This research
has used the services of the ESO Science Archive Facility. This research has
made use of Astropy101010www.astropy.org, a community-developed core Python
package for Astronomy (Astropy Collaboration et al. 2013, 2018). This research
has made use of ”Aladin sky atlas” developed at CDS, Strasbourg Observatory,
France (Bonnarel et al. 2000) and the table processing tools TOPCAT and STIL
(Taylor 2005). We also acknowledge the various Python packages that were used
for the preparation of this manuscript, including NumPy (van der Walt et al.
2011), SciPy (Jones et al. 2001), scikit-learn (Pedregosa et al. 2011),
scikit-image (van der Walt et al. 2014), and Matplotlib (Hunter 2007). This
research has made use of the SIMBAD database operated at CDS, Strasbourg,
France (Wenger et al. 2000). This research has used the VizieR catalog access
tool, CDS, Strasbourg, France (Ochsenbein et al. 2000; DOI:
10.26093/cds/vizier). This work has made use of data from the European Space
Agency (ESA) mission Gaia (www.cosmos.esa.int/gaia), processed by the Gaia
Data Processing and Analysis Consortium (DPAC,
www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been
provided by national institutions, in particular the institutions
participating in the Gaia Multilateral Agreement. A.B. acknowledges partial
funding by the Deutsche Forschungsgemeinschaft Excellence Strategy - EXC 2094
- 390783311 and the ANID BASAL project FB210003. A.H. acknowledges the support
and funding from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation programme (Grant agreement Nos.
851435). D.M. acknowledges support from ANID Basal project FB210003. K.M.
acknowledges funding by the Science and Technology Foundation of Portugal
(FCT), grants No. PTDC/FIS-AST/7002/2020 and UIDB/00099/2020. K.P.R.
acknowledges support from ANID FONDECYT Iniciación 11201161.

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