11institutetext:  INAF – Osservatorio Astronomico di Padova, vicolo
dell’Osservatorio 5, 35122 Padova, Italy 22institutetext: Dipartimento di
Fisica e Astronomia “Galileo Galilei”, Università degli Studi di Padova,
Vicolo dell’Osservatorio 3, 35122 Padova, Italy 33institutetext: Deutsches
Zentrum für Luft- und Raumfahrt (DLR), Institut für Optische Sensorsysteme,
Rutherfordstraße 2, 12489 Berlin-Adlershof, Germany 44institutetext: INAF –
Osservatorio Astronomico di Roma, Via Frascati, 33, 00078 Monte Porzio Catone
(RM), Italy 55institutetext: SSDC-ASI, Via del Politecnico, snc, 00133 Roma,
Italy 66institutetext: Deutsches Zentrum für Luft- und Raumfahrt (DLR),
Institut für Planetenforschung, Rutherfordstraße 2, 12489 Berlin-Adlershof,
Germany77institutetext: Institute of Astronomy, KU Leuven, Celestijnenlaan
200D, 3001, Leuven, Belgium 88institutetext: Department of Astrophysics,
IMAPP, Radboud University Nijmegen, 6500 GL, Nijmegen, The Netherlands
99institutetext: Max Planck Institute for Astronomy, Koenigstuhl 17, 69117,
Heidelberg, Germany 1010institutetext: INAF – Osservatorio Astronomico di
Palermo, Piazza del Parlamento 1, 90134 Palermo, Italy 1111institutetext: Aix-
Marseille Université, CNRS, CNES, Laboratoire d’Astrophysique de
Marseille,Technopôle de Marseille-Etoile, 38, rue Frédéric Joliot-Curie, 13388
Marseille cedex 13, France 1212institutetext: Max-Planck-Institut für
Sonnensystemforschung, Justus-von-Liebig-Weg 3, 37077 Göttingen, Germany
1313institutetext: Institut für Astrophysik, Georg-August-Universität
Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany 1414institutetext:
Center for Space Science, NYUAD Institute, New York University Abu Dhabi, Abu
Dhabi, UAE 1515institutetext: LESIA, CNRS UMR 8109, Université Pierre et Marie
Curie, Université Denis Diderot, Observatoire de Paris, 92195 Meudon, France
1616institutetext: European Space Agency (ESA), European Space Research and
Technology Centre (ESTEC), Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands
1717institutetext: Centro de Astrobiología (CSIC–INTA), Depto. de Astrofísica,
28692 Villanueva de la Cañada, Madrid, Spain1818institutetext: INAF –
Osservatorio Astrofisico di Catania, Via S. Sofia 78, 95123, Catania, Italy
1919institutetext: Department of Physics, University of Warwick, Gibbet Hill
Road, Coventry CV4 7AL, UK 2020institutetext: Centre for Exoplanets and
Habitability, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL,
UK2121institutetext: Armagh Observatory & Planetarium, College Hill, Armagh,
BT61 9DG, UK 2222institutetext: Zentrum für Astronomie und Astrophysik, TU
Berlin, Hardenbergstraße 36, 10623 Berlin, Germany 2323institutetext:
Institute of Geological Sciences, Freie Universität Berlin, Malteserstraße
74-100, 12249 Berlin, Germany 2424institutetext: Observatoire de Genève,
Université de Genève, Chemin Pegasi 51, 1290 Sauverny, Switzerland

# The PLATO field selection process

I. Identification and content of the long-pointing fields

V. Nascimbeni E-mail<EMAIL_ADDRESS>G. Piotto 2211 A. Börner
33 M. Montalto 2211 P. M. Marrese 4455 J. Cabrera 66 S. Marinoni 4455 C. Aerts
778899 G. Altavilla 4455 S. Benatti 1010 R. Claudi 11 M. Deleuil 1111 S.
Desidera 11 M. Fabrizio 4455 L. Gizon 121213131414 M. J. Goupil 1515 V.
Granata 2211 A. M. Heras 1616 D. Magrin 11 L. Malavolta 2211 J. M. Mas-Hesse
1717 S. Ortolani 2211 I. Pagano 1818 D. Pollacco 19192020 L. Prisinzano 1111
R. Ragazzoni 2211 G. Ramsay 2121 H. Rauer 6622222323 S. Udry 2424

(Received 19 September 2021 / Accepted 26 October 2021)

PLAnetary Transits and Oscillations of stars (PLATO) is an ESA M-class
satellite planned for launch by the end of 2026 and dedicated to the wide-
field search of transiting planets around bright and nearby stars, with a
strong focus on discovering habitable rocky planets hosted by solar-like
stars. The choice of the fields to be pointed at is a crucial task since it
has a direct impact on the scientific return of the mission. In this paper, we
describe and discuss the formal requirements and the key scientific
prioritization criteria that have to be taken into account in the Long-
duration Observation Phase (LOP) field selection, and apply a quantitative
metric to guide us in this complex optimization process. We identify two
provisional LOP fields, one for each hemisphere (LOPS1 and LOPN1), and we
discuss their properties and stellar content. While additional fine-tuning
shall be applied to LOP selection before the definitive choice, which is set
to be made two years before launch, we expect that their position will not
move by more than a few degrees with respect to what is proposed in this
paper.

###### Key Words.:

Catalogues – Astronomical data bases – Techniques: photometric – Planetary
systems – Planets and satellites: detection – Stars: fundamental parameters –
Stars: clusters

##  1 Introduction

Since the dawn of modern exoplanetary science in the 1990s, one of its most
ambitious aspirations has always been the discovery and characterization of
“true” Earth analogs, that is rocky planets orbiting within the habitable zone
(HZ) of solar twins. By looking at the distribution of the currently known
exoplanets, it is evident that only a handful of candidates approach that
sweet spot in the parameter space, and that all of them actually miss a
reliable mass estimate, being hosted by stars too faint to be properly
investigated through ultra-high-precision radial velocities (RVs). After the
pioneering results from the CoRoT satellite (built by an international
consortium led by CNES; Auvergne et al. 2009) and from the very successful
NASA missions Kepler (Borucki et al., 2010) and TESS (Ricker et al., 2015)
demonstrated that space-based, wide-field photometry is extremely effective in
detecting transiting planets, and new-generation ultra-stable spectrographs
based on the HARPS/HARPS-N legacy (Mayor et al., 2003; Cosentino et al., 2012)
such as ESPRESSO (Pepe et al., 2021) and the planned HIRES@E-ELT (Marconi et
al., 2021) are getting closer and closer to the 10 cm/s RV precision level
needed to confirm true Earths. In this context, the ESA M-class mission
PLAnetary Transits and Oscillation of Stars (PLATO; Rauer et al. 2014),
planned for launch in 2026, is designed to push the current technology at its
extremes in combined terms of photometric precision and the overall field of
view (FOV). The latter is clearly linked to the number of available bright,
solar-type main-sequence stars, which in turn will allow not only a proper
follow-up by ground-based facilities, but also the extraction of stellar
parameters (including age) with an unprecedented accuracy from the
asteroseismological analysis of the light curves themselves.

The optical design of PLATO is based on 24 identical 20cm-class telescopes
with an entrance pupil of 12-cm (dubbed “normal” cameras) optimized for a
single 450-1000 nm band pass, each having a net circular FOV of about $1037$
$\textrm{deg}^{2}$ (Ragazzoni et al., 2016). Those cameras are not all co-
pointing, but rather arranged in four groups of six cameras each, with the
line of sight of each group offset by $9.2^{\circ}$ with respect to the
satellite bore-sight, along perpendicular directions (Fig. 1). Such a pattern
is nearly invariant to $90^{\circ}$ rotations around the bore-sight axis,
which are mandatory at a three-month cadence to keep the solar panels of PLATO
optimally oriented toward the Sun. The final result, after only accounting for
the parts of the focal planes covered by CCDs, is an overall FOV of about 2132
$\textrm{deg}^{2}$, covered by a variable amount of 24, 18, 12, and six
telescopes111The nominal covered FOV depends upon the geometry of the focal
plane and the optical quality of the centered optical system; it can be found
in detail elsewhere (Pertenais et al., 2021; Magrin, 2021) and it is expected
to slightly vary in actual cameras in a manner that is expected not to affect
the discussion hereafter.. As a consequence of this overlapping design, stars
within the inner region of the PLATO field (PF) are monitored with a much
higher photometric precision than the peripheral ones. In addition to the
normal cameras, two more similar telescopes (dubbed ”fast” cameras) will
monitor the inner region by adding color information and a much faster
sampling cadence (2.5 s versus 25 s).

Figure 1: Geometry of the PLATO field FOV, here it is centered at the origin
(0,0) of a generic spherical reference frame (units are degrees; projection is
orthographic). The number of “normal” cameras covering a given line of sight
is color-coded. The four blue shades, from dark to light, map regions observed
with 24, 18, 12, six cameras (corresponding to four, three, two, and one
group(s) of six co-pointing telescopes each, respectively).

Such a huge field, with a roundish square footprint of about $49^{\circ}\times
49^{\circ}$ overall, will simultaneously image about 5% of the whole sky. The
observing strategy of PLATO, which has yet to be finalized, will probably be
two-staged. The current operation baseline scenario includes two pointings
that are to be monitored during the Long-duration Observation Phase (LOP
fields) of two years each. The minimum duration of a LOP field is one year,
and LOP fields can be complemented by shorter pointings during the “Step and
stare” Observation Phase (SOP fields), which can expand the total areas
surveyed to 40% of the sky. It is worth emphasizing that the observing
strategy has not been formally fixed yet, nor the number or the exact duration
of the SOP and LOP fields. Nonetheless, the LOP fields are recognized as being
the most important ones to achieve the scientific objectives of the mission,
and will be very likely the first one(s) to be observed. Hence, the choice of
the LOP fields is both of crucial importance and also the most urgent one, and
we focus the present work on it. This urgency has been expressed not only for
the PLATO consortium, but also for the exoplanetary and stellar communities at
large since the final choice will have a large impact on many ongoing research
programs and on the planning of target characterization tools and follow-up
campaigns. For example, these include the monitoring of the level of stellar
activity of the high-priority targets.

In this paper we present all of the formal requirements, scientific criteria,
and prioritization algorithms, leading to a preliminary (yet close to final)
choice for the two LOP fields, one in the northern ecliptic hemisphere (LOPN1:
LOP field North 1) and one in the southern one (LOPS1: LOP field South 1).
This paper is also intended to stimulate a discussion within the community to
converge on a final choice for the LOP fields. A second, future paper will be
devoted to the fine-tuning of the LOP fields and to the selection of candidate
SOP fields. The present paper is organized as follows. In Section 2 we
describe the geometry and the general constraints on the field selection
problem. In Section 3 we define a quantitative metric to prioritize a given
LOP field according to its scientific value, and apply this metric to a grid
of possible pointings in Section 4. Finally, we identify and characterize the
content of LOPN1 and LOPS1 in Section 5 and 6, respectively. Some final
remarks and future perspectives are summarized in Section 7. For the sake of
clarity, a glossary with the most used acronyms in this work is shown in Table
1.

Table 1: Glossary of acronyms used throughout this article. Acronym |  Description
---|---
asPIC |  All-sky PLATO Input Catalog (Montalto et al., 2021)
CVZ |  Continuous Viewing Zone
DEB |  Detached Eclipsing Binary
DIA |  Difference Image Analysis
EB |  Eclipsing Binary
FOV |  Field Of View
GC |  Globular Cluster
GO |  PLATO Guest Observing program
HZ |  Habitable Zone
LMC |  Large Magellanic Cloud
LOP |  Long-duration Observation Phase
LOPN |  LOP field North
LOPN0 |  Obsolete LOPN proposal (Nascimbeni et al., 2016)
LOPN1 |  Current LOPN proposal (this work)
LOPS |  LOP field South
LOPS0 |  Obsolete LOPS proposal (Nascimbeni et al., 2016)
LOPS1 |  Current LOPS proposal (this work)
NPF |  Obsolete acronym for LOPN
NSR |  Noise-to-Signal ratio
OC |  Open Cluster
P/L |  PLATO Payload
PF |  PLATO field
PIC |  PLATO Input Catalog (Montalto et al., 2021)
PLATO |  PLAnetary Transits and Oscillations of stars (Rauer et al., 2014)
PPT |  PLATO Performance Team
PSF |  Point Spread Function
RV |  Radial velocity
S/N |  Signal-to-Noise ratio
SOP |  Step and stare Observation Phase
SPF |  Obsolete acronym for LOPS
SRD |  PLATO Scientific Requirements Document
SRJD |  PLATO Scientific Requirements Justification Document

##  2 The field selection problem

The present work is the first attempt to identify the LOP fields using a
consistent and quantitative approach. The coordinates of the SOP and LOP
fields previously selected, as reported in some papers (such as Nascimbeni et
al. 2016; Miglio et al. 2017; Marchiori et al. 2019; Montalto et al. 2021),
all centered at Galactic latitude $|b|=30^{\circ}$, were merely a working
hypothesis to verify their compliance with the PLATO Science Requirements
Document (SRD; v8.0). The definitive choice for the first observing field will
be formally delivered two years before launch with the possibility of fine
tuning the target list up to nine months before launch. In this section, we
review the starting points of this selection process.

Due to telemetry constraints, PLATO will not be able to download time series
of full-frame images. Rather, it will download light curves of preselected
targets, and “imagettes” for a fraction of them, drawn from the PLATO Input
Catalog (PIC; Montalto et al. 2021). This implies, as it will be further
discussed in Section 3, that any field-level prioritization scheme must be
based on a target-level scheme. With only a few exceptions, targets belong to
four formally defined (SRD) samples222For historical reasons, the sample P3
has been eliminated, but the numbering of the PLATO samples was left
unchanged.:

  * •

P1: main-sequence and subgiants stars in the F5-K7 range of spectral type,
brighter than $V=11$, which are to be observed by PLATO with a photometric
noise333The noise limit in this context is established for the random noise
component alone. As required by the SRD, the systematic noise component is, at
most, one-third of the random noise at $V=11$. smaller than 50 ppm in one
hour;

  * •

P2: same as P1, but brighter than $V=8.5$;

  * •

P4: cool late-type dwarfs (M dwarfs) later than K7 and brighter than $V<16$;

  * •

P5: main-sequence and subgiants stars from F5 to late K spectral types,
brighter than $V=13$ and with no constraints on photometric noise.

While the PIC will contain only the targets that are to be observed by PLATO
on the final LOP and SOP fields, an all-sky version of it, including all the
stars compliant with the P1-P2-P4-P5 definition (except the requirements on
photometric noise), is also available (asPIC; Montalto et al. 2021) and will
be the starting point of our simulations. Additional catalogs are being
compiled in parallel for operational purposes (including instrumental
calibration, validation, and fine pointing).

The PLATO scientific requirements for the PIC are described in the SRD for the
samples mentioned above; we refer the reader to Montalto et al. (2021) for a
detailed overview444A new paper by Rauer et al., giving a general status
update of the PLATO mission including the target and field selection work, is
currently in preparation.. Throughout this work, we mostly focus on
prioritizing the P1 sample because it is recognized as the “backbone of the
PLATO mission and must be considered as the highest priority objective” (SRD),
and the field choice should consequently be driven by it.

The field selection process involves a complex optimization task to merge
several (often competing) constraints and prioritization criteria of both an
engineering and scientific nature. We discuss those criteria by splitting them
into four main classes as detailed in the following subsections.

Figure 2: All-sky Aitoff projection in Galactic coordinates, showing the
formal constraints for the selection of the PLATO LOP fields and the synergies
with other transit-finding missions. The two pink circles represent the
$|\beta|>63^{\circ}$ technical requirement for the center of the LOP fields
(“allowed region”), implying that the overall envelopes of every allowed field
choice are two ecliptic caps at $|\beta|\gtrsim 38^{\circ}$ (green circles).
As a reference, the previous provisional choices for the LOP fields, here
called LOPN0 and LOPS0, are plotted with blue shades according to the number
of co-pointing cameras (refer to Fig. 1 for details). The footprints of CoRoT
(red), Kepler (magenta), and K2 (green) are over-plotted together with the
TESS continuous viewing zone at $|\beta|\gtrsim 78^{\circ}$ (yellow circle).
The background gray layer is color-coded according to the areal density of
$G<13.5$ stars from Gaia EDR3. The celestial equator and poles are marked with
a red circle and crosses, respectively.

###  2.1 Formal requirements

By “formal requirements”, we mean mandatory requirements that are listed and
justified in the PLATO SRD and Scientific Requirements Justification Document
(SRJD), respectively. The most relevant ones for the field selection task are
the following:

  1. 1.

_Geometrical constraints._ The center of the LOP fields shall lie within the
two caps at ecliptic latitude $|\beta|>63^{\circ}$ (SRD) to allow for a proper
orientation of the spacecraft throughout the year. We refer to these caps as
“allowed regions”, and to the LOP fields within them as North and South PLATO
LOP field (LOPN, LOPS), respectively. As this requirement holds just for the
center of the fields, part of them can extend outside of the allowed region
anyway (see below);

  2. 2.

_Target counts._ The number of observed targets included in the PLATO stellar
samples as defined by the SRD must pass a specified threshold for each of the
four stellar samples monitored by PLATO (P1, P2, P4, and P5). For the Stellar
Sample 1 (P1), the LOP fields combined must contain at least $15\,000$ (goal:
$20\,000$) F5-K7 dwarfs and subgiants brighter than $V=11$ and be observed
with a photometric precision better than 50 ppm in one hour. Other analogous
thresholds are set for samples P2, P4, and P5, but they can be safely
neglected in this context because they are always met for every possible
choice for the LOP fields (see Section 6).

The $|\beta|>63^{\circ}$ constraint is visualized with an Aitoff projection in
Fig. 2 (pink lines); the two allowed regions combined represent about
$1-\sin(63^{\circ})\simeq 11\%$ of the whole sky area. It is worth emphasizing
that this requirement holds just for the centers of the PF, that is, the outer
envelope of every possible PF choice can reach as far as the $|\beta|\simeq
38^{\circ}$ (green lines in Fig. 2). In other words, in principle,
approximately $1-\sin(38^{\circ})\simeq 38\%$ of the whole sky could be
accessible accounting for this constraint. The two allowed regions are located
at high absolute declinations ($|\delta|>40^{\circ}$), with the celestial
poles being located within them. On the other hand, they span a wide range of
Galactic latitudes, from being nearly tangent to the Galactic plane at $b\sim
2.8^{\circ}$ up to $b\sim 56.8^{\circ}$. Once the full envelope is taken into
account, that implies that every possible Galactic environment could be
potentially probed by a LOP field.

###  2.2 Prioritization criteria

A second class of criteria are based on scientific and practical
considerations. While not formally mandatory, these criteria can be applied to
prioritize those fields which, while fulfilling the formal requirements, also
maximize the scientific output of the mission and minimize the follow-up
effort. Among those, we would like to highlight the following:

  1. 1.

_Stellar contamination._ Moving the PF closer to the Galactic plane boosts the
stellar counts for some PLATO stellar samples (including P1; see Section 4)
and also greatly increases the density and combined flux of background
contaminants, which can be a source of both additional photometric noise
(photometric contamination) and of an increased fraction of false positives
mostly given by blended and grazing eclipsing binaries (astrophysical
contamination; Almenara et al. 2009; Bryson et al. 2013; Fressin et al. 2013)
combined with more exotic scenarios (such as the blending of genuine planetary
transits or eclipsing binaries for which only the secondary eclipses are
visible; Santerne et al. 2013). PLATO, similarly to TESS, is particularly
sensitive to both, owing to its large pixel scale ($\sim 15^{\prime\prime}$;
Rauer et al. 2014). Contamination has, of course, an important impact on the
full validation and confirmation chain, both based on the PLATO photometry
itself and on the follow-up observations.

  2. 2.

_Follow-up resources._ The whole follow-up process is an important part of the
mission, in particular to confirm the planetary nature of the candidates and
to measure their masses through high-precision radial velocities (RVs). The
location of the LOP fields has some obvious and important implications for the
available ground-based facilities (based on the celestial coordinates of the
targets and their annual visibility) that will be assessed in Section 2.3.
More subtly, the fraction of false positives and the actual content of the
target list in terms of astrophysical parameters also have a profound impact
on the follow-up (see Sec. 6);

  3. 3.

_Special targets of interest._ Especially at the fine-tuning stage, it makes
sense to check whether specific objects of high scientific interest land on
the actual CCD collecting area or if this could be made feasible by only minor
adjustments to the pointing. This includes, for instance, nearby open
clusters, known transiting planets, and very bright solar analogs. Some of
these objects will be discussed in Section 6.

  4. 4.

_Synergy with other missions._ Other space missions, most notably Kepler and
TESS, have already monitored a significant fraction of the sky in search of
planetary transits. While the main goal of PLATO is to exploit its
unprecedented level of photometric precision for the detection and
characterization of true Earth analogs, it is worth investigating whether
interesting additional science may come up by the overlap with other observed
fields.

As for the synergies with other exoplanetary missions, we note that all the K2
and CoRoT (Auvergne et al., 2009) fields lie very close to the ecliptic and
celestial equator, respectively, that is completely out of reach of the LOP
envelope (Fig. 2). On the other hand, the center of the Kepler Field is
located within the LOPN allowed region, and the TESS continuous viewing zone
(CVZ) at $|\beta|\gtrsim 78^{\circ}$ is of course centered on the ecliptic
poles, that is, it at least partly overlaps with every technically acceptable
LOP field choice. The James Webb Space Telescope (JWST) “strict” CVZ at
$|\beta|>85^{\circ}$ (Gardner et al., 2006) is a much smaller cap.
Nevertheless, JWST will be able to observe every source at
$|\beta|>45^{\circ}$ for at least 200 days per year in one continuous chunk,
therefore essentially the vast majority of targets within the LOP outer
envelope can be optimally monitored by JWST.

Figure 3: _Left panel:_ Integrated yearly observing time $T$ in hours of a
given star from a hypothetical astronomical observatory at latitude
$\varphi=28.5^{\circ}$, as a function of its equatorial coordinates $\alpha$
(color scale) and $\delta$ (horizontal axis). The two curves refer to two
different sets of thresholds imposed on the maximum airmass $X$ and the
maximum solar elevation $h_{\odot}$, as in legend. The declination range
spanned by the $|\beta|>38^{\circ}$ “outer envelope” and $|\beta|>63^{\circ}$
“allowed region” (see Fig. 2) for the LOPN field is highlighted with gray
boxes. Choosing an observatory with the same $|\varphi|$ but in the southern
hemisphere would yield the same results but with the $\delta$ sign reversed.
_Right panel:_ Same as above, but now the integrated time $T_{w}$ is weighted
by $1/X$, as a more effective follow-up metric.

###  2.3 Ease of follow-up

The impact of the field choice on the follow-up effort, from a purely
observational point of view, can be quantitatively addressed by computing the
total amount of observing time $T$ available for a given target and from a
given ground-based site as a function of its coordinates, under reasonable
assumptions. Most top-class facilities that are or will be available for the
PLATO follow-up are located at tropical or subtropical latitudes (for
instance, La Palma at $\varphi\sim 28^{\circ}.5$, Paranal at
$\varphi\sim-24^{\circ}.5$, La Silla at $\varphi\sim-29^{\circ}$, and Mauna
Kea at $\varphi\sim 20^{\circ}$). The “net” observing time must of course be
computed with reasonable constraints based on the airmass $X$ and on the
apparent elevation of the Sun $h_{\odot}$, with

  1. 1.

most observations being carried out at $X<2$, $h_{\odot}<-12^{\circ}$
(hereafter case 1), and

  2. 2.

very challenging and/or faint targets requiring much safer limits at
$X\lesssim 1.5$, $h_{\odot}<-18^{\circ}$ (hereafter case 2).

Between these two extremes lies the intermediate case of ultra-high-precision
RVs, usually requiring $X\lesssim 1.5$, $h_{\odot}<-12^{\circ}$ to maximize
the signal-to-noise ratio (S/N) and mitigate telluric contamination (Cunha et
al., 2014).

As an exercise, we took a hypothetical astronomical observatory at
$\varphi=28^{\circ}.5$ as a reference site555Of course, choosing a southern
observatory with a similar $|\varphi|$ and reversing the declination sign
would yield the same results for the LOPS choice, so we refer to $|\delta|$
hereafter., computed a uniform HEALPix level-4 grid in equatorial coordinates
$(\alpha,\delta)$ (i.e., $3\,072$ grid points; Górski et al. 2005), and
integrated over a full solar year the total number of hours at which a star at
given $(\alpha,\delta)$ can be observed below the thresholds on $X$ and
$h_{\odot}$ mentioned above. The simulation was performed through the SLALIB
library (Wallace, 2005) and its output is plotted in the left panel of Fig. 3
as a function of $\delta$ (horizontal axis) and $\alpha$ (color scale). As
expected, declination is the key variable, with the yearly observing time
reaching the maximum at $|\delta|\simeq 75^{\circ}$ for case 1 (upper
sequence) and $|\delta|\simeq 45^{\circ}$ for case 2 (lower sequence). The
dependence of $T$ on right ascension $\alpha$ is quite weak ($\lesssim 15\%$,
everything else being equal) because with such constraints, the nightly
observing window is mostly limited by the net arc length of the sky track (set
by $X$) rather than by the twilight-to-twilight night duration (set by
$h_{\odot}$). As expected, $T$ drops when approaching the celestial pole,
which is always at $X>2$ at a $\varphi=28^{\circ}.5$ observing site; this drop
crosses 50% of the $T$ peak at $|\delta|\simeq 87^{\circ}$ and $|\delta|\simeq
75^{\circ}$ for case 1 and 2, respectively. In other words, the LOP fields
covering those high-declination caps should be given lower priority in the
field selection process because they severely limit the follow-up
observations.

Taking a step further, as a more effective metric, we could compute the same
quantity by giving higher priority to observations carried out at lower
airmass. This can be achieved by defining the airmass-weighted observing time
$T_{w}$ as $T$ weighted by a $1/X$ factor (Fig. 3, right panel). By looking at
the general trend, there is clearly a “sweet spot” with more then 800 h/year
available at $20^{\circ}\lesssim|\delta|\lesssim 60^{\circ}$, where the
follow-up can be carried out at a much higher efficiency. Coincidentally, this
is approximately the declination range spanned by our initial LOP field
choices LOPS0 and LOPN0666In some previous works, the LOPS0 and LOPN0 field
were referred to as South and North PLATO Fields (SPF and NPF), respectively.
(Fig. 2). By setting a granularity of 10 minutes as minimum on-target time per
night for case 2, within the same declination range, we get an approximate
number of $\sim 250$ available nights per year, enabling an optimal phase
sampling of the RV signal from HZ planets. Future simulations will be focused
on the actual observing sites involved in the PLATO follow-up program and will
include additional variables not considered here, such as Moon constraints and
weather statistics.

###  2.4 Field rotation

The PLATO attitude will be defined not only by a pointing direction, but also
by a rotation angle around the $Z$ axis of the payload (P/L) module. Given a
pointing direction, different rotation angles will result in a different set
of stars observed. Some of the field selection criteria are only weakly
dependent on the choice of the rotation angle (e.g., the P1 counts), whereas
some are strongly dependent (e.g., visibility of individual targets close to
the edges of the field of view). The choice of the rotation angle determines
the angle between the solar panels of the spacecraft and the Sun. Therefore,
there is a dependency between the choice of the rotation angle at the start of
the observations and the time at which it is necessary to make the first 90
degree roll to keep the action of the sun shield within the requirements. The
constraints related to the spacecraft such as: power produced by the solar
panels at a given sun incidence angle, allowed ranges for early or late 90
degree rolls along the year compliant with the gap requirements, stray light
requirements, etc., are not consolidated at this stage of the project and
therefore we cannot define reliable criteria for the choice of the optimal
rotation angle now. The mission team is working toward a better understanding
of the design in order to provide enough information to the PLATO Science
Working Team to make a choice.

##  3 The prioritization metric

As a starting point for the identification of the LOP fields, we have to
define a quantitative metric to evaluate and compare the priorities of each
possible choice. As discussed in the previous section, there are many
available criteria and they have a complex interplay with each other.
Nevertheless, a simple metric can be devised as guidance for a more educated
guess. Since fixing a field implies freezing the pool from which the target
list is drawn (according to the requirements for samples P1-P2-P4-P5 specified
in the SRD), our approach is 1) to define a prioritization metric at target
level, then 2) to define the same metric at the field level by evaluating its
integral over the list of P1 targets, which are considered to be those that
are most crucial for the success of PLATO. It is a natural first step to
review what has been done in the past to solve the same problem by Kepler and
TESS teams.

A first proposal of the Kepler Field was centered very close to the Galactic
plane, at $b\simeq 5^{\circ}$ (Borucki et al., 2003; Koch et al., 2004), but
then revised by increasing its Galactic latitude by ten degrees to mitigate
the impact of false positives (Jenkins et al., 2005; Batalha et al., 2006).
Unfortunately, no detail about this optimization process has ever been
published. On the other hand, while the sectors pointed by TESS have a very
small margin of adjustment given its fixed sky-scanning law (Ricker et al.,
2015), its target-level prioritization scheme has been presented and discussed
in great detail by Stassun et al. (2018, 2019). The basis of the TESS metric
is to compute a quantity proportional to S/N at which a planet of fixed
radius, with depth $\delta$ and transit duration $d$, is detected transiting a
star of radius $R_{\star}$ observed by TESS on $N_{s}$ sectors with an average
photometric precision $\sigma$ (mostly dependent on its magnitude in the TESS
band). A simple calculation (Stassun et al., 2018) based on white noise
assumptions returns the following:

$\Pi_{\textrm{TESS}}=\frac{S}{N}=\frac{\delta\sqrt{d}}{\sigma/\sqrt{N_{s}}}\propto\frac{R_{\star}^{-1.5}\sqrt{N_{s}}}{\sigma}\quad\textrm{.}$
(1)

This quantity is then arbitrarily normalized to lie in the $[0,1]$ range and
tuned by manually adding penalizing factors to de-prioritize or exclude
specific classes of targets: stars at $b\leq 10^{\circ}$ (to avoid extremely
crowded regions), stars at $\beta\leq 6^{\circ}$ (not to be observed by the
nominal 2-year mission), and stars with unphysical or suspicious physical
parameters listed in the TESS Input Catalog (TIC; Stassun et al. 2019).

Figure 4: Grid-based approach for the LOP field selection, step 1 (coarse
grid). A coarse grid is defined within the allowed regions at
$|\beta|>63^{\circ}$ (pink circle) from a HEALPix level-3 scheme in Galactic
coordinates. The 86 grid points, corresponding to the centers of 86 possible
PF choices, are color-coded according to the number of P1 targets available
within each field. The grid points not meeting the science requirement of
$>7\,500$ P1 targets per field are marked with a cross, while the 32 surviving
fields are labeled with the percentage increase of P1 targets with respect to
the minimum requirement. The background gray layer is color-coded according to
the areal density of $G<13.5$ stars from Gaia EDR3. The TESS CVZ at
$|\beta|\gtrsim 78^{\circ}$ (yellow circle), the Kepler footprint (lilac
region), and the centers of the previous provisional fields LOPN0 and LOPS0
(black empty diamond points) are also shown.

Figure 5: Grid-based approach for the LOP field selection, step 2 (fine grid).
A finer grid is defined within the sky regions compliant with the P1
requirement (see Fig. 4), from a HEALPix level-4 scheme in Galactic
coordinates. The 128 grid points, corresponding to the centers of 128
P1-compliant PF choices, are color-coded according to the value of the
prioritization metric introduced in Section 3, Eq. 4. The LOPN1 and LOPS1
fields newly identified in Section 5 have their footprints plotted as
background gray regions and their centers marked with empty magenta points. In
the case of LOPN1 (left panel), the final choice does not correspond to the
formally highest value of the metric (cyan diamond point; see text for
details). The TESS CVZ at $|\beta|\gtrsim 78^{\circ}$ (yellow circle), the
Kepler footprint (lilac region), the centers of the previous provisional
fields LOPN0 and LOPS0 (black empty diamond points), and the celestial poles
(red crosses) are also shown.

In our case, we could ask ourselves how to develop a metric based on Eq. 1,
but that is more suited to the PLATO LOP field selection. First of all, the
$\sqrt{N_{s}}$ factor does not make sense in our case and can be set to one,
because by design every LOP target is continuously monitored throughout the
nominal duration of the LOP field. Then, as mentioned above, this target-based
metric should be summed over the total number of P1 targets within a given
field, that is:

$\Pi_{\textrm{PLATO}}=\sum_{i\,\in\,\textrm{P1}}\frac{R_{\star,i}^{-1.5}}{\sigma_{i}}\quad\textrm{,}$
(2)

in which $R_{\star}$ can be extracted from the All-Sky PLATO Input Catalog
(asPIC; Montalto et al. 2021), where all the basic astrophysical parameters
for every eligible P1-P2-P4-P5 target in the sky are homogeneously extracted
from Gaia DR2 astrometry and photometry combined with interstellar extinction
maps. To give a reliable estimate of the photometric error $\sigma$
(equivalently called noise-to-signal ratio, or NSR777Here for “noise” we mean
the total noise, including both the random and systematic components.), on the
other hand, requires a detailed modeling of the optical and electronic
performances of PLATO to 1) compute the FOV footprint corresponding to a given
PF pointing and 2) estimate $\sigma$ for each asPIC entry, according to its
magnitude in the PLATO photometric band888See Marchiori et al. (2019) for a
preliminary definition of the PLATO photometric system. and its position in
the optical FOV and on the CCD detector of each camera. Within the PLATO
Consortium, the responsibility of computing the NSR is on the PLATO
Performance Team (PPT), which develops and maintains the simulation software.

It is worth investigating how we can enrich the Eq. 2 metric to include other
prioritization criteria among those discussed in Section 2.2. The most
critical missing factor for PLATO, owing to its very large pixel scale ($\sim
15"/\textrm{pix}$, with the PSF spreading 90% of the flux over an area of
approximately $3\times 3$ pixels; Rauer et al. 2014) is clearly stellar
contamination, both by its photometric and astrophysical components. As for
the photometric contamination, the PPT simulations already take it into
account by estimating the fraction of contaminated flux through a list of
neighbor sources provided along with the asPIC. In other words, the impact
from photometric contamination is already included in our calculation of
$\sigma_{i}$.

As for the astrophysical contamination, which is mostly due to blended,
detached eclipsing binaries mimicking a planetary transit, a more
sophisticated approach is required. Focusing on the specific characteristics
of PLATO, including its pixel scale and PSF size, Bray et al. (in prep.)
exploited a binary population model to estimate the false positive ratio (FPR)
(defined as the ratio between FPs and the total number of detections) as a
function of the line of sight, and for different bins of planetary radius. As
expected, the Galactic latitude $b$ is the key variable in determining the
FPR, the dependence on longitude $l$ being very weak and not even
statistically significant for most bins. After neglecting the $l$ dependence,
we fit the parameters of an exponential law to the $(|b|,\texttt{FPR})$
relation found for terrestrial-sized planets by Bray et al.:

$\texttt{FPR}(b)=0.211\cdot 10^{-0.035\,\cdot\,|b|}\textrm{ ,}\qquad
0<\log\left(\frac{R_{p}}{R_{\oplus}}\right)<0.2\textrm{ ,}$ (3)

with the planetary radius range corresponding to $1<R_{p}/R_{\oplus}\lesssim
1.58$ in linear units. As a simplifying step, we can assume that the “value”
of a given target is inversely proportional to the additional follow-up time
wasted in rejecting the FPs. We can therefore modify our metric by introducing
a factor $(1-\texttt{FPR})$ to give, as an example, half weight to a P1 target
with a 50% FPR, and so on. The field-based metric is then as follows:

$\Pi_{\textrm{PLATO}}^{\prime}=\sum_{i\,\in\,\textrm{P1}}\left[\frac{R_{\star,i}^{-1.5}}{\sigma_{i}}\times\left({1-\texttt{FPR}(b)_{i}}\right)\right]\textrm{
.}$ (4)

In the next section, we evaluate and compare the
$\Pi_{\textrm{PLATO}}^{\prime}$ quantity for a set of different LOP choices as
guidance in the field selection process. Adding too many parameters at once in
such a complex task would be confusing, and it would create an issue about how
these additional terms are arbitrarily weighted with each other. Rather, we
discuss the other prioritization criteria a posteriori, after having
identified the sky regions where the expression in Eq. 4 is maximized.

##  4 The grid-based approach for field selection

Simulating the geometry and content of a given PF is a computationally
intensive task, especially when it comes to the noise calculation. To speed up
the process, rather than maximizing $\Pi_{\textrm{PLATO}}^{\prime}$ as a
continuous function, we implemented a two-staged, grid-based approach. We
first defined:

  1. 1.

a coarse grid to identify the sky regions where the formal requirements (Sec.
2.1) are met, then

  2. 2.

a finer grid where $\Pi_{\textrm{PLATO}}^{\prime}$ (Eq. 4) could be
individually computed and the additional scientific criteria assessed (Sec.
2.2).

###  4.1 The “coarse” grid: Checking the requirements

The coarse grid was built starting from an all-sky HEALPix level-3 partition
(Wallace, 2005), indexed with the RING scheme in Galactic coordinates, made of
768 points with an average spacing of $7.3^{\circ}$. A subset of 86 pointings
allowed by the $|\beta|>63^{\circ}$ constraint was then selected (plotted as
big circles in both panels of Fig. 4). After having ingested asPIC 1.1 as
input catalog, for each field pointing the PPT simulated the photometric
performances of PLATO with the most updated instrumental model, delivering
back to us the corresponding 86 stellar catalogs inclusive of the expected NSR
$\sigma_{i}$ for the individual targets (and hence their membership flag to
the P1-P2-P4-P5 samples). The simulations generating the NSR estimates were
carried out with the PINE software (Börner et al., in prep.). As explained in
Section 3, this simulation takes into account the additional noise from
background sources (i.e., the photometric contamination). It is then
straightforward to extract the P1-P2-P4-P5 counts for each field and check
their compliance with the SRD requirements. Such requirements are formally
defined as the sum over two LOP fields, thus in a broad sense we refer to a
single field as “compliant” when it reaches half of the minimum counts
specified on the SRD. As a result, only 32 grid points (21 in the northern
Ecliptic cap and 11 in the southern one) meet both the P1 and P5 requirements,
with the former being the most stringent one (the non-compliant fields are
struck with a black cross; Fig. 4). The P2 and P4 requirements are always
satisfied, over all the 86 grids points.

As a general trend, the P1 counts increase toward the Galactic plane (by up to
$\sim\\!\\!17\%$ within the compliant region). The northern region (Fig. 4,
left panel) is on average richer in P1 targets than the southern one (right
panel); not only is the compliant area larger for the former, but also the
count gradient as function of $|b|$ is stronger. The P5 counts (not plotted)
show an even stronger gradient (up to $\sim\\!\\!40\%$), which is not
surprising since a fainter magnitude limit translates into a longer distance
probed into the Galactic disk. On the other hand, the count dependence on
Galactic longitude is much more complex: For the P1 sample, it is mostly
negligible in the LOPN region, but it is not in the LOPS region (where its
impact is about $\sim\\!\\!20\%$ along Galactic parallels), while for the P5
sample the trend is completely reversed. The asymmetric behavior between the
north and south caps may look puzzling at first sight, but actually it is not
unexpected due to the very different distribution of Galactic dust along these
lines of sight (Lallement et al. 2019; see Section 6 for more details). We
emphasize that the impact of interstellar extinction on targets usable by
PLATO is the result of a complex interplay because its “local” component
decreases the number of available targets by increasing their apparent
magnitude and noise level, while, at larger distances, it mitigates both
photometric and astrophysical contamination by masking background sources. As
our stellar samples are magnitude-limited, this “local” threshold is of course
dependent on that limit, being $d\lesssim 300$ pc for the P1 sample (Fig. 8,
lower panels). On top of this, the faint tail of the P1 and most of P5 sample
probe even larger distances (up to $d\simeq 1000$ pc), where the spiral
structure of the Milky Way starts to be discernible (Miyachi et al., 2019;
Poggio et al., 2021) and the basic assumption of spatial homogeneity breaks.

A closer look at the stellar content of the compliant fields reveals the
following: 1) the gain of P1 targets at lower Galactic latitudes is mostly due
to the increasing fraction of F-type main-sequence and slightly evolved stars
(and, to a minor extent, to K-type subgiants), while the sky density of G and
K dwarfs is close to be isotropic; and 2) the actual impact of photometric
contamination on the P1 sample is mostly negligible even close to the Galactic
plane, where the average increase in photometric noise due to it is at most a
few percent. In other words, while there is some numerical gain of targets in
moving the LOP fields closer to the disk, this gain is due to the less
valuable stars in terms of scientific output because F dwarfs are larger and
more massive than the Sun and with a higher rotational velocity, posing
challenges to both transit detection and RV follow-up. We come back to this in
Section 6.

###  4.2 The “fine” grid: Optimizing the metric

The fine grid was built starting from an all-sky HEALPix level-4 partition,
made of 3072 points with an average spacing of $3.7^{\circ}$. Only points
within the $|\beta|>63^{\circ}$ allowed region and broadly overlapping with
the “compliant” region found in the previous section were retained (i.e., non-
crossed circles of Fig. 4). The 128 survived grid points, 87 in the north and
41 in the south, are plotted in Fig. 5. Again, for each of the 128 fields, a
stellar catalog of all the observed targets including the calculated NSR
$\sigma_{i}$ has been provided by the PPT with the same procedure described
above. Finally, for each field, we computed the
$\Pi_{\textrm{PLATO}}^{\prime}$ metric as defined in Eq. 4; the output value
is color-coded with the same scale in both panels of Fig. 5. It is worth
emphasizing that this dimensionless metric has only a relative meaning in our
optimization process, while its absolute normalization, unlike other
quantities such as the target counts, is arbitrary and not informative. For
this reason, the maximum value of $\Pi_{\textrm{PLATO}}^{\prime}$ over the
whole LOPS+LOPN grid (i.e., 121.16) has been normalized to one in the color
scale of both panels of Fig. 5.

##  5 Identification of the provisional LOP fields

A quick look at the smooth spatial distribution of
$\Pi_{\textrm{PLATO}}^{\prime}$ (Fig. 5) reveals two well-localized “sweet
spots” (in the LOPS and LOPN regions, respectively) where our prioritization
metric reaches a global maximum. In the following we discuss the
identification of the two LOP fields separately.

###  5.1 The southern LOP field: LOPS1

Within the LOPS region, the metric has its maximum value at the grid point
labeled #2188 in the HEALPix RING scheme, located at Galactic coordinates
$l\simeq 250^{\circ}.3$, $b\simeq-24^{\circ}.6$ (at the boundary between
Columba and Puppis) and marked with a magenta diamond point in the right panel
of Fig. 5. A PF centered at this line of sight (gray outline, same plot; also
Fig. 6) would nearly graze the Galactic plane in the six-telescope region,
with its northernmost border reaching $b\simeq-0^{\circ}.25$; the whole Col,
Pic, Cae, and Dor constellations and parts of Car, Pup, CMa, Lep, Ret, Hor,
and Eri are covered by this PLATO field. The central FOV region covered by 24
telescopes, where the highest photometric precision is achieved, lies in the
$-35^{\circ}\lesssim b\lesssim-15^{\circ}$ range. This is also where most of
the P1 targets are expected to be due to the 50 ppm in one hour noise
requirement. From now on, we refer to this pointing as the new provisional
location of the southern LOP field, called LOPS1; it lies only a few degrees
from the previous LOPS0 (black diamond point in the plot), while having a
higher metric value. The coordinates of its center are shown in Table 9 in
different reference frames, together with the number of corresponding
P1-P2-P4-P5 counts. As from construction, all of them fulfill the formal SRD
requirements. A discussion about the LOPS1 content and its astrophysical
properties is given in Section 6.

Figure 6: Location of the provisional South PLATO LOP Field (LOPS1) in a
Galactic (left panel) and equatorial (right panel) projection. The PF is
color-coded in blue shades according to the number of co-pointing “normal”
cameras, from six (light blue) to 24 (dark blue). Stars brighter than $V=3$
are plotted with green circles; their area is proportional to their $V$-band
flux. Known exoplanets are plotted with red crosses; none of them reach the
threshold of 50 bibliographic references. The position of the Large Magellanic
cloud is over-plotted with a yellow ellipse.

Figure 7: Location of the provisional North PLATO LOP Field (LOPN1) in a
Galactic (left panel) and equatorial (right panel) projection. The PF is
color-coded in blue shades according to the number of co-pointing “normal”
cameras, from six (light blue) to 24 (dark blue). Stars brighter than $V=3$
are plotted with green circles; their area is proportional to their $V$-band
flux. Known exoplanets are plotted with red crosses; those with more than 50
bibliographic references are over-plotted with red circles and their area is
proportional to the number of references.

###  5.2 The northern LOP field: LOPN1

Evaluating $\Pi_{\textrm{PLATO}}^{\prime}$ for the northern field requires
more reasoning. The region where the metric has a global maximum is larger and
flatter with respect to the LOPS1 choice, encompassing a vast area
approximately centered on the north rim of the Kepler Field (Fig. 5, left
plot). Some of those points lie at quite low Galactic latitudes,
$10^{\circ}\lesssim b\lesssim 20^{\circ}$, implying that the corresponding PFs
cross the Galactic plane and include a significant fraction of the FOV on
extremely crowded stellar fields. Selecting the grid point with the nominally
highest value of $\Pi_{\textrm{PLATO}}^{\prime}$, at $l\simeq 78^{\circ}.7$,
$b\simeq 16^{\circ}.9$ (HEALPix label: #1070; cyan diamond point on Fig. 5),
for instance, would imply having $\sim 30\%$ of the P1 targets and $\sim 42\%$
of the P5 targets at $|b|<10$, respectively. While our metric takes both
photometric and astrophysical contamination into account to some extent,
choosing such a low-latitude pointing could make additional issues arise.
Those include, not only the impact from less common blending scenarios not
considered in the Bray et al. analysis and that are much more difficult to
model, but also an increased difficulty in characterizing the target and its
contaminants at the validation and confirmation stage, when information from
external catalogs and databases have to be merged into a global modeling. With
this in mind, it makes sense to ponder what the actual scientific gain of such
a choice would be with respect to a choice at $|b|\simeq 25^{\circ}$, that is,
as for LOPS1. Focusing on the P1 sample, the gain in terms of counts is just
$\sim 7\%$. A careful comparison on how the astrophysical parameters are
distributed reveals that the number of main-sequence G and K stars in that
sample is mostly independent of the field choice, as these (nearby) targets
are isotropically distributed on the sky. Rather, the gain in counts is almost
entirely due to F stars at $d\gtrsim 200$ pc, and, to a lesser extent, to
distant cool subgiants. When it comes to the core objectives of the PLATO
mission, the latter two components are less scientifically valuable with
respect to GK dwarfs, both from a detection point of view (larger radii and
hence smaller transit depths at a given planetary radius) and at the follow-up
stage (larger masses and higher rotational velocity making ultra-high-
precision RVs more difficult and expensive in terms of observing time).

As a much safer alternative, we can select as LOPN1 (provisional North LOP
field) the best pointing located at $|b|>20^{\circ}$, identified by the
HEALPix index #878 (marked by a magenta diamond point in the left panel of
Fig. 5; also Fig. 7). The value of our prioritization metric differs by only
$1\%$ with respect to the #1070 pointing. LOPN1 is located at Galactic
coordinates $l\simeq 81^{\circ}.6$, $b\simeq 24^{\circ}.6$ (in Draco) and
marked with a magenta diamond point in the right panel of Fig. 5, with its
footprint plotted as a gray area. A large fraction of Lyr, Dra, Cyg, and Her
constellations and parts of Cep and UMi are covered by LOPN1. The coordinates
of its center are shown in Table 9 in different reference frames, together
with the number of corresponding P1-P2-P4-P5 counts. As for LOPS1, all of them
fulfill the formal SRD requirements. A discussion about the LOPN1
astrophysical properties is given in the following section.

##  6 Characterization of the provisional LOP fields

In the previous sections, we have identified LOPS1 and LOPN1 as the best
provisional PFs to be observed during the LOP phase, and demonstrated that
they both meet the mission formal requirements and optimize our scientific
prioritization metric. Now we can take a closer look at their content and its
astrophysical properties. The full footprints of LOPS1 and LOPN1, which are
color-coded to show the coverage of different regions by 24-18-12-6 cameras,
are shown in Fig. 7 and Fig. 6 in Galactic and equatorial coordinates. As
mentioned above, both fields, centered at $|b|\simeq 25^{\circ}$, span a very
wide range of Galactic latitudes, from the Galactic plane up to nearly
$|b|\simeq 50^{\circ}$. In terms of declination $\delta$, both fields avoid
the celestial poles, most targets being in the
$30^{\circ}\lesssim|\delta|\lesssim 60^{\circ}$ band, that is, they are fully
within the range which maximizes the efficiency and schedulability of the
ground-based follow-up (Sec. 2.3; Fig. 3). A non-negligible fraction of LOPS1,
in particular, is monitored by 6-12 cameras at
$-30^{\circ}\lesssim\delta\lesssim-15^{\circ}$ (Fig. 6; right plot), that is
limitedly observable even by facilities in the northern hemisphere.

###  6.1 Bright stars

A few extremely bright stars fall on silicon within our provisional fields
(green circles on Fig. 7 and 6), most notably Vega = $\alpha$ Lyr and Deneb =
$\alpha$ Cyg in LOPN1, and Sirius = $\alpha$ CMa and Canopus = $\alpha$ Car in
LOPS1. This is unavoidable when dealing with such a wide FOV. Indeed, a formal
requirement from SRD states that “stars brighter than the maximum dynamic
range shall not impede operation of the instruments, except by modifying the
number of useful pixels”. During the future fine-tuning stage of the LOP
fields, it will be investigated whether it is advisable to slightly adjust the
field position in order to move some of those ultra-bright stars out of the
FOV (as it is feasible for Sirius and Deneb) or within the CCD gaps of at
least a subset of cameras (e.g., for Vega). The impact of stray light from
these extremely bright stars should be evaluated.

###  6.2 Known exoplanets

It is of scientific interest to find out which already known (confirmed)
transiting exoplanets will be imaged during the LOP phase, as all of them will
be added in the target list regardless of their formal inclusion in the
P1-P2-P4-P5 samples. We started from the latest online version of the TEPCAT
catalog (Southworth, 2011) and cross-matched it with Simbad (Wenger et al.,
2000) to get the total number of bibliographic items referring to that target
for each entry, as a very rough proxy for its scientific “popularity”. All of
those planets (51 in the LOPS1 footprint and 192 in the LOPN1 one) are plotted
as red symbols in Fig. 7 and 6; an arbitrary threshold of 50 publications has
been highlighted by a different plotting style to make the most “popular”
targets visually stand out. Not surprisingly, most of the known LOPN1 planets
are actually Kepler objects since the Kepler Field is fully overlapped with
it. Other non-Kepler, well-studied planetary systems are TrES-1, TrES-4,
WASP-3, and HAT-P-5 (in the 12-camera regions) as well as KELT-9, HAT-P-14,
HD149026, and HAT-P-2 (in the six-camera region); the recently discovered
ultra-short-period TOI-1444, to be monitored with 12 telescopes is worth
mentioning for its small radius (1.44 $R_{\oplus}$; Dai et al. 2021). On the
LOPS1, none of the known planets cross the 50-publication threshold.
Nevertheless, at least four confirmed systems host planets smaller than 2
$R_{\oplus}$, all of them were discovered by TESS: TOI-540 (Ment et al.,
2021), LHS 1815 (Gan et al., 2020), TOI-700 (Gilbert et al., 2020), and
TOI-270 (Günther et al., 2019).

Table 2: Properties of the provisional LOP fields LOPS1 and LOPN1 (Fig. 4).

field | LOPS1 | LOPN1 | notes
---|---|---|---
HEALPix | #2188 | #0878  | level $k=4$,
index |  |  | RING scheme
$\alpha$ [deg] | 93.49134 | 277.18023 | ICRS
$\alpha$ [hms] | 06:13:57.9 | 18:28:43.2 | ICRS
$\delta$ [deg] |  $-$42.93544 | 52.85952 | ICRS
$\delta$ [hms] |  $-$42:56:08 | 52:51:34 | ICRS
$l$ [deg] | 250.31250 | 81.56250 | IAU 1958
$b$ [deg] |  $-$24.62432 | 24.62432 | IAU 1958
$\lambda$ [deg] | 96.36781 | 287.98162 | Ecliptic
$\beta$ [deg] |  $-$66.29759 | 75.85041 | Ecliptic
P1 targets | 7 806 | 8 190 | Req. 7 500
P2 targets | 699 | 705 | Req. 500
P4 targets | 17 115 | 16 833 | Req. 2 500
P5 targets | 154 639 | 158 806 | Req. 122 500
999The listed quantities are: HEALPix indexing, coordinates for the field
centers in equatorial, Galactic, and ecliptic reference frames, and the number
of targets falling into the P1-P2-P4-P5 categories. The P1-P2-P4-P5
requirements are formally specified for the sum of the LOP fields; the
quantity reported in the “notes” column is just half of that value (See
Section 2.2 for more details).

Figure 8: Astrophysical parameters of the P1 targets within the LOPN1 (left
panels) and LOPS1 (right panels) provisional fields. _Top panels:_ Number
density of P1 targets as a function of effective temperature and stellar
radius. The high-density ridge corresponds to the main sequence, where the
Solar parameters (1 $R_{\odot}$, 5778 K) are marked with a cyan diamond point
as reference. _Middle panels:_ Number density of P1 targets as a function of
Galactic latitude $|b|$ and stellar radius. A histogram of $b$ is overplotted
with a magenta line and arbitrary normalization. _Bottom panels:_ Number
density of P1 targets as a function of distance and stellar radius. The median
distance at fixed solar radius is plotted with a green line.

Figure 9: Astrophysical parameters of the P1 targets within the LOPN1 (left
panels) and LOPS1 (right panels) provisional fields. _Top panels:_
Distribution of P1 targets on the $(T_{\textrm{eff}},R_{\star})$ plane; stars
are classified into five distinct groups representing main-sequence K-type
dwarfs (dK), G dwarfs (dG), F dwarfs (dF), subgiants at
$T_{\textrm{eff}}<5500$ K (sg_cool), and $T_{\textrm{eff}}>5500$ K (sg_hot).
The labels show their relative occurrence. The gray line in both panels marks
an arbitrary boundary between main-sequence and evolved stars (see text).
_Bottom panels:_ Histograms of the distribution in distance of the same
subgroups. The best-fit lognormal distribution is also over-plotted with a
continuous line.

###  6.3 Stellar parameters

Since for each provisional field we have a catalog containing all the stars
suited for inclusion in the P1-P2-P4-P5 samples, it is natural to ask
ourselves how the stellar parameters of our targets are distributed, and what
selection effects are at work. Again, we mainly focus on the P1 sample as it
is recognized as the backbone for the PLATO mission. All the parameters
discussed here are extracted from the asPIC and are therefore homogeneously
derived on the whole sky based on Gaia DR2 astrometry and photometry and
estimated as fully described in Montalto et al. (2021). We emphasize that
while those model-dependent astrophysical parameters are prone to systematic
uncertainties, P1 targets will be thoroughly characterized by PLATO through
asteroseismic analysis, yielding stellar radii and ages with an unprecedented
degree of accuracy (see Table 1 from Aerts et al. 2019; Lebreton & Goupil
2014). Nevertheless, no reasonable systematic offset of the Montalto et al.
(2021) parameters (see their Table 4) can significantly change the results of
the following analysis.

As a crucial starting point for what concerns the detection and confirmation
of planetary transits, we can look at the distribution of our P1 targets as a
function of their effective temperature and stellar radius (Fig. 8, top
panels). The most obvious feature seen in these number density plots is the
ridge corresponding to the main sequence. A long tail of stars extending up to
4-5 $R_{\odot}$ is made of a well-defined branch of “typical” field K- and
late-G-type subgiants (at $T_{\textrm{eff}}\simeq 5000$ K) and, more evenly
spread at higher temperatures, of early-G and F subgiant stars. As expected
for a bright magnitude-limited selection, our sample is dominated by stars, on
average, larger than the Sun and hence with a larger luminosity, with a median
of 1.62 and 1.67 $R_{\odot}$ for the LOPS1 and LOPN1 respectively; this is
also a hint that the small numerical gain of P1 targets in the LOPN1 versus
LOPS1 is actually accompanied by a relative increase in the F and subgiant
fraction. As for the Galactic latitude distribution of the P1 targets (Fig. 8;
middle panels), their histograms confirm our expectations that they mostly lie
in the $15^{\circ}\lesssim|b|\lesssim 35^{\circ}$ range, where the 18- and
24-telescope coverage and the corresponding improvement in photometric
precision allows more targets to get through the noise requirements for the P1
sample. Due to the relation between $(T_{\textrm{eff}},R_{\star})$ and
absolute magnitude, the average distance of P1 targets is a strong function of
their radius (Fig. 8; bottom panels). In particular, while the median distance
of 1 $R_{\odot}$ stars is about 100 pc, the high-luminosity tail extends up to
500 pc and beyond.

Figure 10: Star clusters within the provisional PLATO Fields LOPS1 (left
panel) and LOPN1 (right panel) in Galactic coordinates. The PFs are color-
coded in blue shades according to the number of co-pointing “normal” cameras,
from six (light blue) to 24 (dark blue). The Kepler Field is plotted as a pink
translucent area. Open cluster members identified by Cantat-Gaudin et al.
(2020) are plotted as green points, and the corresponding cluster center and
radius as black circles; only for OCs older than 100 Myr and for which the
apparent visual magnitude of a solar twin is $m_{V,\odot}<15$ ($d\lesssim
1000$ pc) are the circles gray-filled with the name of the OC labeled.
Globular clusters from Harris (1996) are also plotted with red empty circles.

In order to dive a bit deeper into the behavior of those different components,
we split the LOPS1 and LOPN1 samples into five different subgroups
representing main-sequence K-type dwarfs (dK), G dwarfs (dG), F dwarfs (dF),
as well as evolved stars on the classical subgiant branch (sg_cool) and at
earlier types (sg_hot). The $T_{\textrm{eff}}$ boundaries between dK, dG, and
dF are taken from the tables by Pecaut & Mamajek (2013), the upper limit of
the main sequence is parametrized as $R_{\star}=1+2\cdot
10^{-7}(T_{\textrm{eff}}-4000)^{2}$, and the arbitrary boundary between sg_hot
and sg_cool is set at 5500 K. The position of these five groups on the
$(T_{\textrm{eff}},R_{\star})$ plane and their distance distribution are shown
in Fig. 9 with a consistent color coding. Indeed, about 60% of the whole
sample is made of main sequence stars, with approximately two-thirds of them
being F dwarfs (Fig. 9, top panels). The overall dF, dG, and dK fraction is
about 38%, 20%, and 4%, respectively. The dF and sg_hot are slightly over-
represented in the LOPN1 with respect to LOPS1 at the expense of the dG share.
In absolute terms, however, the dG counts are 3.8% larger in the LOPN1 (1632
versus 1570), which implies that the, on average, lower interstellar
extinction at $d>200$ pc in the northern region manages to get some more faint
solar analogs into the sample. As for the distance histograms of the five
subgroups (Fig. 9, bottom panels), they again reflect the luminosity effect,
with the intrinsically faint dK component dominating at $d<50$ pc and the dF
\+ sg_cool \+ sg_hot one being the vast majority of targets at $d>200$ pc.

Table 3: Properties of open clusters overlapping the provisional PLATO Fields
LOPS1 and LOPN1.

name |  $\alpha$ [2000.0] |  $\delta$ [2000.0] |  $r_{50}$ [deg] | $N_{0.7}$ | $A_{V}$ | $m_{V,\odot}$ |  $d$ [pc] | age [Myr]
---|---|---|---|---|---|---|---|---
Alessi 3 | 109.275 | -46.142 | 1.541 | 164 | 0.14 | 12.34 | 297 | 631
NGC 2516 | 119.527 | -60.8 | 0.496 | 652 | 0.11 | 13.08 | 423 | 240
ASCC 101 | 288.399 | 36.369 | 0.372 | 69 | 0.19 | 13.10 | 412 | 490
NGC 1901 | 79.561 | -68.294 | 0.652 | 69 | 0.21 | 13.19 | 427 | 891
UBC 480 | 119.954 | -50.636 | 0.094 | 13 | 0.08 | 13.32 | 481 | 209
Teutsch 35 | 294.091 | 35.742 | 0.414 | 143 | 0.00 | 13.37 | 509 | 102
NGC 6991 | 313.621 | 47.4 | 0.555 | 243 | 0.20 | 13.84 | 577 | 1549
UPK 524 | 105.304 | -54.432 | 0.957 | 58 | 0.30 | 13.91 | 569 | 107
UPK 495 | 105.863 | -40.886 | 0.939 | 39 | 0.05 | 13.98 | 660 | 437
NGC 2287 | 101.499 | -20.716 | 0.332 | 625 | 0.02 | 14.04 | 688 | 170
Gulliver 21 | 106.961 | -25.462 | 0.364 | 121 | 0.13 | 14.09 | 670 | 275
Alessi Teutsch 11 | 304.127 | 52.051 | 0.257 | 110 | 0.37 | 14.21 | 634 | 145
Pismis 4 | 128.79 | -44.407 | 0.291 | 98 | 0.18 | 14.22 | 695 | 120
UPK 537 | 126.57 | -52.113 | 0.755 | 51 | 0.54 | 14.36 | 628 | 234
Alessi Teutsch 3 | 118.228 | -53.022 | 0.491 | 87 | 0.47 | 14.50 | 693 | 105
UPK 542 | 134.258 | -54.303 | 0.544 | 33 | 0.49 | 14.86 | 808 | 631
ASCC 111 | 302.891 | 37.515 | 0.537 | 136 | 0.39 | 14.87 | 851 | 275
NGC 2447 | 116.141 | -23.853 | 0.202 | 731 | 0.05 | 14.92 | 1018 | 575
UPK 119 | 308.572 | 43.326 | 0.197 | 24 | 0.71 | 14.96 | 765 | 1549

101010The columns show the cluster name, the equatorial coordinates $\alpha$
and $\delta$ in decimal degrees, the angular radius $r_{50}$ enclosing 50% of
the cluster members, the number $N_{0.7}$ of cluster members having a
membership probability larger than 0.7, the extinction coefficient $A_{V}$,
the $V$-band apparent magnitude $m_{V,\odot}$ of a cluster star having the
Sun’s absolute magnitude $M(V)=4.83$, the distance $d$ in parsecs, and the
best-fit age in Myr. The table is sorted by increasing $m_{V,\odot}$. All the
parameters except for $m_{V,\odot}$ are extracted from Cantat-Gaudin et al.
(2020).

###  6.4 Star clusters

Star clusters are known as excellent astrophysical laboratories because their
ensemble properties (including age, distance, and chemical composition) can be
measured or constrained more precisely than is possible with single field
stars. This holds not only for stellar astrophysics, but also in
exoplanetology, where clusters provide us with a homogeneous environment where
the physical and orbital parameters of the discovered planets can be put into
an accurate evolutionary context (Dawson & Johnson, 2018). The large pixel
scale of PLATO makes the extraction of high-precision light curves in crowded
environments very challenging; nevertheless, sophisticated difference image
analysis (DIA) and PSF-fitting techniques have been developed to overcome this
issue (Bouma et al., 2019; Montalto et al., 2020; Nardiello et al., 2020), so
it is worth asking which clusters will be imaged during the LOP phase. This is
also requested by a SRD science requirement, which discusses the need to use
PLATO to investigate planet formation in different environments, including
clusters.

We start from the work by Cantat-Gaudin et al. (2020), who list model-
dependent astrophysical parameters and membership probabilities for more than
$200\,000$ stars hosted by 1867 confirmed open clusters (OCs) and young
associations. About 300 OCs overlap at least partly with the LOPS1 and LOPN1
footprint (Fig. 10). With only a handful of exceptions, they are located quite
close to the Galactic disk at $|b|<15^{\circ}$ and simultaneously monitored by
six or 12 PLATO cameras. Discussing the single clusters is beyond the scope of
this paper, but it is indeed interesting to focus on OCs being particularly
favorable to be targeted by PLATO, that is, close enough for solar-type stars
to be below a reasonable magnitude limit, and not so young for stellar
activity to be a severe limiting factor. From cluster distance $d$ and
extinction coefficient $A_{V}$, we can easily compute the apparent visual
magnitude $m_{V,\odot}$ of a hypothetical solar twin, assuming an absolute
magnitude $M_{V,\odot}\simeq 4.83$. Then, by setting $m_{V,\odot}<15$ ($V\sim
15$ being an approximate upper limit for both the PLATO photon-noise-dominated
regime and for an effective RV follow-up of giant planet candidates; roughly
corresponding to $d\lesssim 1000$ pc) and age $>100$ Myr, we get 19 OCs, 13 in
the LOPS1 and six in the LOPN1, listed in Table 3 and over-plotted with
labeled gray circles in Fig. 10. We stress that those cluster ages, in
particular, are prone to large systematic errors and are considered here just
to set a rough boundary between very young clusters and older ones. Notably
most of the selected OCs, although their nature of bound stellar systems is
confirmed, are scarcely characterized; with the notable exceptions of NGC 2516
(Fritzewski et al., 2020; Bouma et al., 2021), NGC 1901 (Carraro et al.,
2007), NGC 2287 (Harris et al., 1993; Sun et al., 2019), and NGC 2447 (Reddy
et al., 2015; da Silveira et al., 2018), all of them have never been
investigated through high-resolution spectroscopy or by targeted photometric
surveys.

An even harder challenge for PLATO is the extraction of light curves of stars
belonging to globular clusters (GCs) due to their larger distance and extreme
crowding. While the PSF-based approach has been demonstrated to be effective
in this regime even with smaller telescopes and larger pixel scales, such as
with TESS (Nardiello et al., 2019), clearly the limiting magnitude prevents us
from reaching the lower main sequence. Rather than for exoplanet discovering,
such data could be very useful to carry out rotational and asteroseismological
studies of their giant stellar population. Both LOPS1 and LOPN1 are angularly
quite far from the Galactic bulge, where the vast majority of GCs are. Still,
by cross-matching the Harris (1996) catalog with the field footprints, we get
eight GCs imaged during the LOP phase (labeled red points in Fig. 10), four in
the LOPS1 (AM 1=E 1, M 79, NGC 1851, and NGC 2298), and four in the LOPN1 (M
13, M 56, M 92, and NGC 6229). Three of them will be monitored by 18 or 24
telescopes: M 92, NGC 1851, and NGC 2298. As an additional note, we mention
that about half of the Large Magellanic Cloud (LMC; yellow circle in Fig. 6)
will be observed in LOPS1 by six PLATO cameras, complementing and extending
the ongoing monitoring by TESS (Sharma et al., 2018; Mackereth et al., 2021).

###  6.5 Eclipsing binaries

Detached eclipsing binaries (DEBs) have long been studied since the masses and
radii of both stellar components can be determined to good accuracy, which can
then be used to test stellar models (Popper, 1967, 1980; Torres et al., 2010;
Serenelli et al., 2021). However, as the binary orbit has to be sufficiently
wide so that the stars evolve as if they were single stars, this restricts
binaries that have an orbital period of a day or more. The latest version
(September 6, 2021) of DEBCAT (Southworth, 2015), which is a continuation of
the catalog of Andersen (1991), lists 271 DEBs.

Since the ephemerides of DEBs are typically well known, they can be used as
verification sources for the systematic accuracy of the PLATO time stamps. In
addition, as Maxted (2018) argues, they can also be used to validate the PLATO
asteroseismic age scale. Doing a cross-match of DEBCAT with the footprint of
fields LOPS1 and LOPN1 yields 21 and 45 DEBs, respectively. Among these, eight
and 12 (respectively) meet the magnitude, spectral type, and noise
requirements to be considered for inclusion in the P1-P2-P4-P5 stellar
samples.

###  6.6 Synergies

There is a considerable overlap between LOPS1, LOPN1, and other past and
future space-based missions. Notably, the Kepler Field is fully within the
footprint of LOPN1 (Fig. 10, right panel). About 35% of its targets will be
imaged by either 24 or 18 PLATO cameras (in particular at $b\gtrsim
13^{\circ}$), 58% by 12, and 7% by six (that is, by a single group of
telescopes). Among the $200\,036$ targets listed in the Kepler Stellar
Properties Catalog DR25 (Brown et al., 2011), $12\,418$ stars are formally
included in the PLATO P1-P2-P5 samples (FGK dwarfs subgiants), with
$\sim{}700$ M dwarfs meeting the requirements of the P4 sample. This is mostly
due to the magnitude constraint of PLATO which is much brighter than the
median magnitude of the Kepler sample at $\textrm{Kepmag}\simeq 14.6$.
Nevertheless, as mentioned in Section 6.2, all the stars with confirmed
planets for which meaningful photometry can be extracted will be included in
the PIC irrespective of their formal compliance with the requirements.

TESS finished its two-year nominal mission in July 2020, during which its
scanning law defined two CVZs at $|\beta|>78^{\circ}$ each covered by 13
sectors, or one year of mostly continuous photometry. During the first
extended mission, Year 3 has followed a similar pattern as Year 1 (thus re-
observing the southern ecliptic hemisphere) while, since August 2021, Year-4
TESS observations started mapping part of the ecliptic region for the first
time. While the actual observing strategy of the next extended missions has
not been officially chosen yet, it is clear that the ecliptic caps will
continue to retain a crucial importance in terms of phase coverage given the
mission design. Even more important, the ecliptic polar regions are in strong
synergy with other follow-up and characterization missions that will be flying
during or after the PLATO nominal mission. In addition to JWST, another
satellite will be extremely effective in characterizing the atmospheres of
planets discovered by PLATO, especially those hosted by bright and nearby P1
stars: ARIEL (Tinetti et al., 2018), whose CVZ is sligthly larger than the
TESS one ($|\beta|\gtrsim 70^{\circ}$; Tinetti et al. 2021). The north TESS
CVZ is fully within LOPN1 (Fig. 10, right panel), and about 75% of it is
covered by 12 or more telescopes; among the 361,196 unique candidate target
list (CTL) targets observed by TESS from sector 1 to 42, 16,511 meet the
requirements to be considered for inclusion in the PLATO sample P1 or P5 (4
882 for P1 alone); in other words, $\sim 60\%$ of the LOPN1 P1 targets already
have TESS short-cadence data. As for LOPS1 (Fig. 10, left panel), more than
half of the $\beta<-78^{\circ}$ CVZ is secured, mostly in the six- and
12-telescope region. Including a larger fraction of it would imply moving
LOPS1 too close to the crowded Galactic plane, dramatically decreasing our
prioritization metric (Fig. 5, right plot). Nevertheless, among the same TESS
CTL targets mentioned above, 16,881 meet the requirements to be considered for
inclusion in the PLATO sample P1 or P5 (5 195 for P1 alone, or $\sim 67\%$ of
the whole LOPS1 P1 sample). The mean number of available TESS sectors is
currently 14.1 for LOPN1 P1 targets and 13.8 for LOPS1 P1, with the shorter
time spent by TESS on the north Ecliptic cap almost exactly balancing the
smaller overlap between LOPS1 and the TESS CVZ.

As a closing remark, we note that LOPS1 (but not LOPN1) crosses a large sector
of the $40^{\circ}\lesssim|\beta|\lesssim 50^{\circ}$ band where the scanning
law of Gaia (Gaia Collaboration et al., 2016) will result in the largest
number of scanning transits and hence much better astrometric solutions, more
reliable astrophysical parameters, and also more densely sampled time-resolved
measurements. For the same reason, this is also the sweet spot where Gaia is
supposed to discover a large number of planetary systems through the
astrometric technique (Sozzetti et al., 2014). Although only a tiny fraction
of them will be on transiting configurations, there is a chance that PLATO
will be able to catch some of them. In any case, PLATO will contribute to
better characterize the architecture of some of these systems providing
transits of internal planets (when orbit inclination will be favorable).

##  7 Conclusions

The PLATO launch date is slowly approaching, being currently planned by the
end of 2026. Two years before the launch, a formal proposal for the first
field to be observed (likely a LOP field) has to be delivered, and the final
PIC for the first field shall be frozen nine months before launch to allow,
among other things, the kick-off of the guest observer (GO) call for
proposals.

In this work, we have outlined the complex problem concerning the PLATO field
selection in general, discussed the many different (and sometimes competing)
scientific criteria involved, and developed a prioritization metric to help
the process, which unavoidably is a multi-staged one: The current provisional
LOP fields we presented, named LOPS1 and LOPN1, and of which we described
content and synergies, will be fine-tuned in the next years. It is likely that
the final choice will not move from the current proposal by more than a few
degrees (cf. the $3.7^{\circ}$ average grid spacing in Fig. 5). For such a
huge FOV of $>2000$ $\textrm{deg}^{2}$, that would translate into just a few
percent of re-allocated targets. There is still time for the final choices of
the fields PLATO will observe, and one of the purposes of this paper is to
stimulate the scientific community to provide inputs to help converging into
the final choice of LOPN and LOPS.

###### Acknowledgements.

The authors are grateful to the referee, Keivan Stassun, for reading the
manuscript and providing constructive comments and suggestions. This work
presents results from the European Space Agency (ESA) space mission PLATO. The
PLATO payload, the PLATO Ground Segment and PLATO data processing are joint
developments of ESA and the PLATO Mission Consortium (PMC). Funding for the
PMC is provided at national levels, in particular by countries participating
in the PLATO Multilateral Agreement (Austria, Belgium, Czech Republic,
Denmark, France, Germany, Italy, Netherlands, Portugal, Spain, Sweden,
Switzerland, Norway, and United Kingdom) and institutions from Brazil. Members
of the PLATO Consortium can be found at https://platomission.com/. The ESA
PLATO mission website is https://www.cosmos.esa.int/plato. We thank the teams
working for PLATO for all their work. MM, GP, VN, VG, LP, SD, SO, SB, RC, LM,
IP acknowledge support from PLATO ASI-INAF agreements n.2015-019-R0-2015 and
n. 2015-019-R.1-2018. We would like to acknowledge the financial support of
INAF (Istituto Nazionale di Astrofisica), Osservatorio Astronomico di Roma,
ASI (Agenzia Spaziale Italiana) under contract to INAF: ASI 2014-049-R.0
dedicated to SSDC. CA acknowledges funding received from the KU Leuven
Research Council (grant C16/18/005: PARADISE) and from the BELgian federal
Science Policy Office (BELSPO) through PRODEX grants for Gaia and PLATO. This
work has made use of data from the European Space Agency (ESA) mission Gaia
(https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and
Analysis Consortium (DPAC,
https://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 Multilateral Agreement. This research has made use of the
SIMBAD database (operated at CDS, Strasbourg, France), TOPCAT and STILTS
(Taylor, 2005, 2006).

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