11institutetext:  INAF – Osservatorio Astrofisico di Torino, Via Osservatorio
20, 10025, Pino Torinese, Italy 22institutetext: Observatoire Astronomique de
l’Université de Genève, Chemin Pegasi 51b, 1290, Versoix, Switzerland
33institutetext: Univ. Grenoble Alpes, CNRS, IPAG, 38000 Grenoble, France
44institutetext: Département de Physique, Institut Trottier de Recherche sur
les Exoplanètes, Université de Montréal, Montréal, Québec, H3T 1J4, Canada
55institutetext: Space Telescope Science Institute, 3700 San Martin Drive,
Baltimore, MD 21218, USA 66institutetext: INAF – Osservatorio Astronomico di
Brera, Via E. Bianchi, 46, 23807 Merate (LC), Italy

# DREAM

III. A helium survey in exoplanets on the edge of the hot Neptune desert with
GIANO-B at TNG

G. Guilluy 11 V. Bourrier 22 Y. Jaziri 22 W. Dethier 33 D. Mounzer 22 P.
Giacobbe 11 O. Attia 22 R. Allart Trottier Postdoctoral Fellow44 A. S. Bonomo
11 L. A. Dos Santos 55 M. Rainer 66 A. Sozzetti 11

(Received date ; Accepted date )

###### Abstract

Context. The population of close-in exoplanets features a desert of hot
Neptunes whose origin remains uncertain. These planets may have lost their
atmosphere, eroding into mini-Neptunes and rocky super-Earths below the
desert. Direct observations of evaporating atmospheres are essential to derive
mass-loss estimates and constrain this scenario. The metastable He I triplet
at 1083.3 nm represents a powerful diagnostic of atmospheric evaporation
because it traces the hot gas in extended exoplanet atmospheres while being
observed from the ground. In addition, it is located at the bright near-
infrared stellar continuum and is very weakly affected by interstellar medium
(ISM) absorption.

Aims. We carried out a homogeneous He I transmission spectroscopy survey,
targeting a selected sample of nine planets along the different edges of the
desert, to interpret the absorption line profile with evaporation models and
to better understand the role of photoevaporation in the desert formation.

Methods. We observed one transit per planet using the high-resolution, near-
infrared spectrograph GIANO-B mounted on the Telescopio Nazionale Galileo
telescope. We focused our analysis on the He I triplet, based on a comparison
of the in-transit and out-of-transit observations, and we computed high-
resolution transmission spectra. We then employed the 1D p-winds model to
calculate the planetary thermospheric structures and to interpret the observed
transmission spectra.

Results. We found no signatures of planetary absorption in the He I triplet in
any of the investigated targets. We thus provided 3 $\sigma$ upper-limit
estimations on the thermosphere absorption, temperature and mass loss, and
combined them with past measurements to search for correlations with
parameters such as the stellar mass and XUV flux, which are thought to be key
drivers in the formation of the He I triplet.

Conclusions. These results strengthen the importance of performing homogeneous
surveys and analyses in bringing clarity to He I detections and (thereby) to
plausible Neptunian desert origins. Our findings corroborate literature
expectations that state the He I absorption signal is correlated with the
stellar mass and the received XUV flux. However, when translated in terms of
mass-loss rates, these trends seem to disappear. Thus, further studies are
essential to shed light on this aspect and to better understand the
photoevaporation process.

###### Key Words.:

planets and satellites: atmospheres – techniques: spectroscopic - methods:
observational - infrared: planetary systems

##  1 Introduction

The population of close-in exoplanets
($P\mathrel{\raisebox{3.01385pt}{\mbox{$\underset{\vbox{\hbox{$\sim$}}}{\vbox{\hbox{$<$}}}$}}}$30
days) features a dearth of Neptune-size planets on very short orbits
($P\mathrel{\raisebox{3.01385pt}{\mbox{$\underset{\vbox{\hbox{$\sim$}}}{\vbox{\hbox{$<$}}}$}}}$4
days). This so-called “Neptunian desert” (e.g., Lecavelier Des Etangs 2007;
Davis & Wheatley 2009; Szabó & Kiss 2011; Beaugé & Nesvorný 2013) is not an
observational bias, as close-in Neptunes are easy to detect via both transits
and radial-velocity measurements. Debate around the key driver mechanisms at
the origin of the desert, which are linked to the formation, migration, and
atmospheric evolution of close-in planets, is still ongoing. Photoevaporation
(e.g., Owen & Lai 2018; Owen 2019) and high-eccentricity orbital migration
followed by tidal interaction with the star (e.g., Matsakos & Königl 2016) are
the most likely explanations to date, but their interplay remains to be
explored. Among questions that need to be addressed are the range of mass and
period over which these processes are at play and whether they also shape the
Neptunian “savanna” that is represented by a lighter deficit of Neptune-size
planets at longer periods and lower irradiation, as highlighted by Bourrier et
al. (2023).

Investigating this complex puzzle is the goal of the Desert-Rim Exoplanets
Atmosphere and Migration (DREAM) program. In DREAM I (Bourrier et al. 2023),
we measured the orbital architectures of a large sample of exoplanets spanning
the borders of the Neptunian desert and savanna. This work revealed a high
fraction of misaligned orbits, strengthening the importance of high-
eccentricity orbital migration for close-in planets. Architecture measurements
from DREAM I were included in a large statistical study of spin-orbit angles
in DREAM II (Attia et al. 2023). This work confirmed the major role of tides
in shaping the overall distribution of close-in planets’ orbital
architectures, except for a substantial fraction of planets on polar orbit
that appears resilient to tidal realignment and further support the importance
of disruptive dynamical processes. A subsample of the systems in DREAM I were
observed in transit as part of a campaign our team led with the GIARPS
observing mode (GIANO-B+HARPS-N) at the Telescopio Nazionale Galileo (TNG)
telescope, to measure their Rossiter-McLaughlin effect in optical HARPS-N data
and to analyze the planetary atmospheric spectra in near-infrared GIANO-B
data. The objective of this third paper in the DREAM series is to search these
GIANO-B data for absorption by helium escaping the upper atmosphere of these
planets, bringing constraints on their mass loss and the role of atmospheric
escape in the formation of the desert.

Strong high-energy X-rays and extreme ultraviolet (XUV) stellar radiation can
lead to an expansion of the upper atmospheric layers and the substantial
escape of gas into space (e.g., Vidal-Madjar et al. 2003; Lammer et al. 2003;
Tripathi et al. 2015). While hot Jupiters are generally stable against this
photoevaporation, hot Neptunes have lower gravitational potential that makes
them more vulnerable (e.g., Lecavelier des Etangs et al. 2004; Owen & Wu
2017). The upper atmospheric layers of these planets have traditionally been
probed via transit spectroscopy in the ultraviolet (UV), by monitoring the
change in absorption during transit of the stellar Ly$\alpha$ line. The H I
exospheres of hot Jupiters yield absorption signatures in the stellar
Ly$\alpha$ that are ten times deeper than the lower atmosphere (Lecavelier des
Etangs et al. 2012; Ehrenreich et al. 2012), and this absorption level is even
higher for the exospheres of warm Neptunes (e.g., Ehrenreich et al. 2015;
Lavie et al. 2017; Bourrier et al. 2018) – and possibly even for mini-Neptunes
(e.g., dos Santos et al. 2020b; Zhang et al. 2023). However, UV observations
can only be performed from space, and the stellar Ly-$\alpha$ line is
contaminated by geocoronal emission and absorbed by the interstellar medium
(ISM) absorption. While geocoronal emission can be reliably subtracted in the
data reduction, there is nothing that can be done for the ISM absorption, so
that only the wings of the line are usable when probing escaping atmospheres.
In this way, gas dynamics in regions closer to the planet itself, at the wind-
launching radius, remains obscured with Ly-$\alpha$ observations (e.g.,
Murray-Clay et al. 2009; Owen et al. 2023) leading to low-precision mass-loss
rates, as we observe the gas when it has already escaped in the exosphere.
Additionally, Ly-$\alpha$ studies have only been performed on a few systems
due to ISM absorption, which prevents observing stellar Ly-$\alpha$ lines
beyond $\sim$50 pc. Accessing the thermosphere, the upper atmospheric layer
below the exosphere, represents a way to overcome these limitations.

As it is very weakly affected by interstellar absorption and it can be
observed from the ground the He I triplet at $\lambda\sim$1083.3 nm (vacuum
wavelength) has been recently identified as a robust alternative for tracing
atmospheric expansion and evaporation (e.g., Seager & Sasselov 2000; Oklopčić
& Hirata 2018). The first detection of a helium thermosphere was obtained by
Spake et al. 2018 with the Wilde Field Camera 3 (WFC3) of the Hubble Space
Telescope (HST). The He I feature was not spectrally resolved due to the low-
resolution of the data, however, further observations at high-resolution with
CARMENES (Allart et al. 2019) allowed for the absorption lines to be
spectrally resolved and to derive atmospheric properties, showing that the
helium tracer can probe the planet thermosphere and occasionally the
exosphere. To date, He I has been searched for in the upper atmospheres of
about 40 planets which (see Table 3).

These observations have led to He I absorption or upper limits estimations.
However, the non-homogeneity in both observing methodology, and data reduction
technique may mask possible trends in the data, thus making it difficult to
find a clear correlation with the parameters (e.g., stellar mass, XUV
irradiation) that are believed to drive the detection (or non-detection) of
the He I triplet (e.g., Fossati et al. 2022).

Despite the several helium studies, the parameters that are considered to be
important in triggering the He I detection are currently under debate. For
instance, Oklopčić & Hirata 2018 proposed that planets orbiting K-type stars
should be promising targets for showing evaporating or escaping helium
atmospheres. This is because K-type stars emit a high amount of stellar XUV
emission, which is responsible for He I atoms ionization from the ground state
(which can then recombine into the metastable state) and a low stellar mid-
ultraviolet emission, which reduces the ionization of metastable He I atoms
(Oklopčić & Hirata 2018). However, the discovery of a strong helium signature
for a gas-giant planet orbiting an F-star (Czesla et al. 2022), highlighted
that also other planets orbiting stars with different spectral energy
distribution (SED) can exhibit large helium outflow regardless of the stellar
spectral type. More studies are thus essential to shed light on which are the
mechanisms and parameters important in the He I detection.

The sample we analyze in DREAM III is part of a pilot survey of nine planets
located at the different edges of the Neptunian desert and savanna. The sample
is described in Sect. 2, and its near-infrared (nIR) observations with the
GIANO-B high-resolution spectrograph are presented in Sect. 3. We detail the
data reduction procedures in Sect. 4, and the interpretation of the helium
absorption observations in Sect. 5. We then present our findings in Sect. 7,
followed by our conclusions in Sect. 8.

Figure 1: Two-dimensional distribution of exoplanets as a function of their
radius and period from the NASA Exoplanet Archive (Akeson et al. 2013).
Magenta and pink diamonds represent DREAM III and Allart et al. (2023) -a work
that we took as a reference for our analysis- targets, respectively. The
approximate boundaries of the desert and savanna are highlighted.

##  2 Sample

Our survey consists of nine planets along the transitions defining the
Neptunian desert and savanna. Their low densities ($\rho_{\mathrm{pl}}$¡3.5 g
cm-3) and bright host stars (J¡10.7) favor observations of their atmosphere in
the nIR. The planet and star parameters adopted in this work are presented in
Table LABEL:Tab_parameters. Below, we describe the main features of interest
for these planets, which led to their inclusion in our survey. We would like
to stress that no helium studies were reported in the literature for these
investigated planets.
Hot Jupiter HAT-P-3b. Its strong irradiation is expected to induce a large
mass loss. The small radius of HAT-P-3b is indicative of a metal-enriched
composition (Chan et al. 2011), which could be the result of atmospheric
escape over the last 2.6 Gyr. Hydrogen would be lost preferentially, making
helium a particularly interesting tracer for this planet. HAT-P-3b orbits a
K-star, thus according to Oklopčić (2019), it is likely to show metastable
helium absorption. DREAM I reports a polar orbit for this planet and, if
confirmed, dynamical simulations will be needed to understand whether the
present-day architecture is a result of a disruptive dynamical history (with
partial evaporation of its volatile content) or a primordial misalignment
between the protoplanetary disk and the star.
Hot Jupiter HAT-P-33b. The extreme irradiation and very low density
(0.134${}_{-0.042}^{+0.053}$ g cm-3, Wang et al. 2017) of this highly inflated
planet are expected to induce a large mass loss. Turner et al. (2017) measured
an excess depth during transit in the R-band, which contains the H$\alpha$
transition, suggesting that the planet may be undergoing hydrodynamical
escape. The misalignment of the system, due to the inclination of the host
star, suggests that HAT-P-33 b underwent a high-eccentricity migration, and
thus it possibly migrated close to the star long after its formation, which
would change its atmospheric history compared to an early-on migration and
erosion.
Ultra-hot-Jupiter HAT-P-49b. It is a gas giant exoplanet discovered orbiting a
bright (V = 10.3) slightly evolved F-star (Bieryla et al. 2014). Its extreme
irradiation, due to its proximity to the host star (a=0.0438$\pm$0.0005 au,
Bieryla et al. 2014), is expected to induce a large mass loss. According to
the analysis presented in DREAM I, the planet is probably on a polar orbit,
supporting a disruptive dynamical origin or evolution for the system, whose
architecture was unaffected by tidal interactions with the shallow convective
envelope of the host star (DREAM II).
Warm super-Neptune HD89345b. This planet is five times more irradiated than
the evaporating super-Neptune WASP-107b, yet it survived atmospheric escape
for 9.4 Gyr (e.g., Van Eylen et al. 2018). HD 89345b stands at the transition
between stable Jupiter-mass planets and hot Neptunes that entirely lost their
atmosphere and this is thus an essential piece in the puzzle that is the
origin of the desert. HD 89345b is located on a misaligned orbit (DREAM I)
right within the savanna (see Fig. 1). The present-day misalignment could
trace both a primordial formation of the system, arising from the tilt of the
early star or protoplanetary disk, or the planet could have migrated more
recently, exiting a Kozai resonance with an outer companion (e.g., Bourrier et
al. 2018; Attia et al. 2021). This second scenario would imply that HD 89345b
arrived near the star at the end of its main-sequence lifetime, changing our
view of its irradiative history and our interpretation of its inflation (Yu et
al. 2018) and hydrodynamical escape.
Warm sub-Neptune K2-105b. It remains unclear why sub-Neptunes appear to be
more resilient than warm Neptunes to the processes that created the desert
(Owen 2019). K2-105b stands at the transition between these two populations
and is predicted to have an atmosphere accounting for up to 10% of its total
mass (Narita et al. 2017). Detecting the presence of this atmosphere and
measuring its mass loss could bring constraints on the interior of the planet;
if its evolution was controlled by atmospheric escape, it is estimated to have
retained its envelope only if its core mass is greater than 6 M⊕(Narita et al.
2017). DREAM I reported a possibly misaligned orbit which, if confirmed, might
support a turbulent dynamical history and the planet’s late arrival into its
close-in orbit. However, the presence of other targets may indicate a
primordial inclination of the star or protoplanetary disk, as K2-105b is far
away from its host stellar companion to experience tidal interactions.
Warm Neptune Kepler-25c. It is close to a resonant periodic configuration with
a companion planet, which is known to be the final state of a system that
undergoes migration within the protoplanetary disk (Migaszewski & Goździewski
2018). Kepler-25c should thus be evaporating since its formation 11 Gyr ago
(Marcy et al. 2014), yet its low density (0.588${}^{+0.053}_{-0.061}$ g cm-3,
Mills et al. 2019) indicates the presence of a H/He envelope.
Warm Neptune Kepler-63b. It is a gas giant exoplanet with a radius between
Neptune and Saturn. The orbital period is around 9.4 days, leading to an
equilibrium temperature of about 900 K (Mallonn et al. 2022). The planet is in
a polar orbit around a young Sun-like star (Sanchis-Ojeda et al. 2013, DREAM
I), thus offering the possibility to assess how evaporation shapes a Neptune’s
atmosphere in its early life. Its radius and insolation are similar to those
of the other Neptunian targets, but it is much younger (200 Myr vs 10 Gyr) and
still possibly undergoing vigorous escape.
Hot sub-Neptune Kepler-68b. With a density of 3.32${}^{+0.86}_{-0.98}$ g
cm-3(Gilliland et al. 2013), it is considered a candidate ocean planet (Zeng &
Sasselov 2014) possibly topped by a moderate H/He envelope (Howe et al. 2014).
Detecting He would offer insights on the mysterious nature of this sub-
Neptune, representative of the transition between rocky planets and gas giants
(Lopez & Fortney 2014).
Warm sub-Neptune WASP-47d. The WASP-47 planetary system is composed of at
least four planets, a hot Jupiter (WASP-47 b; P = 4.159 days, Bryant & Bayliss
2022) with an inner super-Earth (WASP-47 e; P = 0.7896 days, Bryant & Bayliss
2022), a close-orbiting outer Neptune (WASP-47 d; P = 9.031 days, Bryant &
Bayliss 2022), and a long-period giant planet (WASP-47 c; P = 588.4 days,
Vanderburg et al. 2017; Bryant & Bayliss 2022). WASP-47 d is near a 2:1
resonance with the inner Hot Jupiter WASP-47b. It has a similar radius and
insolation of K2-105b but is three times less massive. Their comparison could
provide valuable insight into evaporation processes on sub-Neptunes.

##  3 Observations

Figure 2: S/N in the region of interest (1082.2-1085.5 nm)(left panel) and
airmass (right panel) measured during the GIARPS observations. The vertical
dashed lines mark the $t_{1}$, $t_{2}$, $t_{3}$, and $t_{4}$ contact points
(from left to right). Black stars indicate the discarded AB couples for low
S/N.  Table 1: Log of TNG-GIANO-B observations.

Target | Night | S/NAVE | ammin-ammax | Nobs | Exposure time [s]
---|---|---|---|---|---
HAT-P-3b | 2020-01-30 | 34 | 1.1-2.2 | 34 | 600.0
HAT-P-33b | 2019-12-04 | 35 | 1.0-1.5 | 34 | 600.0
HAT-P-49b | 2020-07-30 | 37 | 1.0-1.9 | 72 | 300.0+
HD89345b | 2020-02-02 | 34 | 1.1-2.0 | 176 | 100.0
K2-105b | 2020-01-18 | 27 | 1.0-1.9 | 32 | 600.0
Kepler-25c | 2019-06-14 | 40 | 1.0-2.0 | 38 | 600.0
Kepler-63b | 2020-05-13 | 27 | 1.1-1.3 | 14 | 600.0
Kepler-68b | 2019-08-03 | 50 | 1.1-2.1 | 66 | 300.0
WASP-47d | 2021-07-30 | 23 | 1.3-2.4 | 34 | 600.0

111 time-averaged S/N in the spectral region containing the Hei triplet
(1082.2–1085.5 nm).
(+) First four exposures at 600s.

We observed the systems in our sample with the nIR echelle spectrograph
GIANO-B installed on the 3.6 m Telescopio Nazionale Galileo (TNG) telescope.
The observations were performed with the GIARPS configuration and were carried
out with the nodding acquisition ABAB (Claudi et al. 2017). Therefore, while
the targets were observed in one nodding position along the slit (A and B),
the sky spectra were gathered simultaneously with the other one, thus
providing an accurate reference for subtracting the thermal background and
telluric emission lines.

GIANO-B covers the Y, J, H, and K spectral bands (0.95-2.45 $\mu$m) in 50
orders at a resolving power of R$\sim$50,000. For this analysis, we focus on
order #39, where the helium triplet falls. We collected a transit observation
for each investigated target. The only exception is HAT-P-3b, as due to bad
weather conditions, we collected two nights of observation, namely UT 14 April
2019 and UT 30 January 2020, but the first visit was excluded from our
analysis since observations had to be stopped just before the transit. A log
of the observations is reported in Table 1. Figure 2 shows the variation in
the signal-to-noise ratio (S/N) for order #39 and the variation in airmass for
each exposure. Due to the lack of a sufficient number of collected images, we
had to discard Kepler-63b from our analysis. Moreover, K2-105b’s and
HD89345b’s observations were affected by GIANO-B auto-guide problems and by
the presence of clouds, so we decided to discard the AB couples of exposures
which exhibit a very low S/N.

##  4 Data analysis

Extended or evaporating atmospheres can be detected through an excess
absorption by metastable helium in the planet transmission spectrum. In the
following section, we discuss the steps we performed in order to reduce the
raw GIANO-B data, extract individual transmission spectra, and calculate
average in-transit spectra in the planet rest frame.

###  4.1 Initial data reduction

The raw spectra were dark-subtracted, flat-corrected, and extracted (without
applying the blaze function correction) using the GOFIO pipeline (Rainer et
al. 2018). In addition, GOFIO yields a preliminary wavelength calibration
(defined in vacuum) using U-Ne lamp spectra as a template. We used the ms1d
spectra, with the echelle orders separated and the Barycentric Earth Radial
Velocity (BERV) correction applied, the spectra are defined in the terrestrial
rest frame.

Since the U-Ne is acquired at the end of the night to avoid the persistence of
the saturated signal of some emission lines on the detector polluting the
scientific observations, the mechanical instability of GIANO-B makes the
wavelength solution determined by GOFIO insufficient in terms of accuracy. We
corrected for this by aligning all the GIANO-B spectra to the telluric
reference frame via spline-interpolation based on the retrieved shifts
obtained by cross-correlating with a time-averaged spectrum used as a template
(Brogi et al. 2018; Guilluy et al. 2019, 2020; Giacobbe et al. 2021). We thus
aligned the spectra to the reference frame of the Earth’s atmosphere, which is
also assumed as the frame of the observer (neglecting any $\sim$10 m s-1
differences due to winds). We then used the atmospheric transmission spectrum
generated via the ESO Sky Model
Calculator222https://www.eso.org/observing/etc/bin/gen/form?INS.MODE=swspectr+INS.NAME=SKYCALC
to refine the initial GOFIO wavelength calibration.333When the telluric lines
are not strong enough, the re-alignment into the telluric rest frame may not
work properly, as in the case of Kepler-68b. Thus, we preferred to discard
this step in the analysis for this specific target.

###  4.2 Transmission spectroscopy

We performed the transmission spectroscopy, applying the steps described below
to each transit and target independently and considering the system parameters
listed in Table LABEL:Tab_parameters.

####  4.2.1 Telluric correction

First, we performed a detailed correction for telluric contamination. We used
the Molecfit ESO software (Smette et al. 2015; Kausch et al. 2015) to correct
for the transmission telluric lines (Allart et al. 2017). As this is the first
time that Molecfit has been applied to GIANO-B data, we report the adopted
parameters in Table 5 .

Figure 3: Example of a time-averaged spectrum before (in blue) and after the
telluric lines removal (in red). Dashed vertical lines highlight the position
of two H2O telluric lines, i.e., 1083.51 nm and 1083.69 nm, in the region
around the He I triplet. These two lines are corrected in the red spectrum.

Molecfit is based on a combination of two different sources: an atmospheric
standard profile (MIPAS), and a Global Data Assimilation System (GDAS)
profile. Molecfit gives the merging of these two profiles as input for a line-
by-line radiative transfer model (LBLRTM). We considered the precipitable
water vapor in our transmission model, and selected a fixed grid to merge the
two atmospheric models, namely, the variations in temperature, pressure,
humidity, and abundance of H2O from 0 to 120 km are described with a fixed
number of layers (50). Meanwhile, LBLRTM returns the telluric spectrum. We
considered one observation at a time,and we initially performed the model
fitting on selected spectral intervals inside the order #39 showing a well-
determined continuum level, a good number of telluric lines, and a few or zero
stellar lines. Based on the best-fit parameters derived by Molecfit, we then
generated a telluric spectrum for the entire spectral order and we corrected
the science spectrum. An example of telluric removal is shown in Fig. 3.

In the spectral region of interest, there are three OH emission lines that
fall near the He I triplet (at $\sim$1083.21 nm, $\sim$1083.24 nm, and
$\sim$1083.43 nm, vacuum wavelengths). As the observations were gathered with
the nodding acquisition mode that allows for the subtraction of the thermal
background and emission lines (see Sect. 4.1), there is no need to correct for
telluric emission lines. However, due to seeing variations during the
observing nights, the A-B subtraction can leave some residuals at the
wavelengths of the OH lines. We thus masked the correspondent wavelengths.

####  4.2.2 Alignment into stellar rest frame.

We then shifted the spectra in the stellar rest frame by accounting for the
stellar radial velocity, $V_{\star}$, in the telluric reference system. This
is given by:

$V_{\star/\oplus}=\sum_{i}{K_{\star
i}[cos(\nu_{i}+\omega_{i})}+e_{i}cos(\omega_{i})]+V_{\mathrm{sys}}+V_{\mathrm{bar}},$
(1)

where we account for the velocity of the observer induced by the rotation of
the Earth and by the motion of the Earth around the Sun, namely: the
barycentric Earth radial velocity, $V_{\mathrm{bar}}$, the stellar reflex
motion induced on the host star by each planet $i$ in the system (i.e.
$K_{\star
i}[cos(\nu_{\mathrm{i}}+\omega_{\mathrm{i}})+e_{\mathrm{i}}cos(\omega_{\mathrm{i}})]$,
where $\nu_{\mathrm{i}}$ is the true anomaly obtained from the eccentric
anomaly via the Kepler’s equation, in this way we directly account for the
eccentricity which is significative for some of our targets, e.g., HAT-P-33b,
HD89345b), $\omega_{\mathrm{i}}$ is the argument of periastron,
$e_{\mathrm{i}}$ is the eccentricity, and $K_{\star i}$ is the stellar radial-
velocity semi-amplitude, and the systemic velocity of the star-planets system
with respect to the barycentre of the Solar System ($V_{\mathrm{sys}}$).

Figure 4:  Transmission spectra
(Tλ,i=$\tilde{\mathrm{F}_{\mathrm{i}}(\lambda)}$/S${}_{\mathrm{out}}(\lambda)$
-1) shown in tomography in the planetary rest frame in the region of the He I
triplet, as a function of wavelength and planetary orbital phase (left). The
contact points t1, t2, t3, and t4 are marked with horizontal black lines. The
regions affected by OH- contamination are masked. Mean-transmission spectrum
for each observed transit (right). The horizontal blue line is the white-light
radius $(R_{\mathrm{pl}}/R_{\star})^{2}$. The mean transmission spectra have
an inverted sign compared to
$\tilde{\mathrm{F}_{\mathrm{i}}(\lambda)}$/S${}_{\mathrm{out}}(\lambda)$ -1 as
the radius is expressed instead of absorption. Black vertical lines indicate
the position of the He I lines. Red line marks the spectral regions affected
by OH- emission. For some planets, some residuals are left at the position of
the Si line ($\sim$1083 nm). This is due to the depth of the line which can
give rise to difficulties in the spectral extraction (see e.g., Krishnamurthy
et al. 2023)

.

####  4.2.3 Transmission spectra calculation.

For every considered target, we divided each spectrum444We did not consider
some spectra which exhibited much lower S/N compared to the other exposures or
outliers near the position of the He I triplet -see Fig.10 for details. by its
median value, thus obtaining the normalized spectra,
$\tilde{\mathrm{F}_{\mathrm{i}}}$. We then built a master stellar spectrum,
$S_{\mathrm{out}}(\lambda)$, by averaging the out-of-transit spectra (i.e.,
with an orbital phase smaller than t1 or greater than t4), and derived
individual transmission spectra, $T_{\mathrm{\lambda,i}}$, by dividing each
spectrum by $S_{\mathrm{out}}(\lambda)$, that is,
Tλ,i=$\tilde{\mathrm{F}_{\mathrm{i}}(\lambda)}$/S${}_{\mathrm{out}}(\lambda)$
-1. Finally, we linearly interpolated transmission spectra in the planet rest
frame, as follows:

$\begin{split}V_{\mathrm{pl_{j}/\star}}&=-\sum_{i}{K_{\star
i}[cos(\nu_{i}+\omega_{i})+e_{i}cos(\omega_{i})]}\\\
&-K_{\mathrm{pl_{j}}}(cos(\nu_{j}+\omega_{j})+e_{j}cos(\omega_{j})),\end{split}$
(2)

where $K_{\mathrm{pl_{j}}}$ is the computed planet radial-velocity semi-
amplitude (see Table LABEL:Tab_parameters) of the considered target $j$). The
2D maps of the transmission spectra in the planet rest frame are shown in the
left panels of Fig. 4.

The usual method to search for faint planetary atmospheric signatures is to
average in-transit transmission spectra in the planet rest frame and thus
boost the S/N. However, the naive calculation of transmission spectra
performed above neglects the change in broadband flux level of in-transit flux
spectra, due to the occultation of regions with varying flux intensity by the
opaque planetary disk. For example, limb-darkening, if unaccounted for, biases
the retrieved atmospheric absorption signal toward smaller values at phases
close to the stellar limb, as compared to the stellar disk center. We thus
followed the approach presented in Mounzer et al. (2022) that had been re-
adapted from Wyttenbach et al. (2020) to compute the in-transit transmission
spectra:

$\left(\frac{R_{\mathrm{pl}}(\lambda,t)}{R_{\star}}\right)^{2}=\frac{LD_{\mathrm{mean}}}{LD(t)}\frac{F_{\mathrm{out}}(\lambda)-(1-\delta(t))F_{\mathrm{i}}(\lambda,t)}{F_{\mathrm{local}}(\lambda,t)},$
(3)

where $F_{\mathrm{i}}(\lambda,t)$ is each observed spectrum at phase $t$,
$1-\delta(t)$ is the broadband (”white-light”) transit light curve,
$\delta(t)$ is the transit depth, $LD(t)$ and $LD_{\mathrm{mean}}$ represent
the stellar limb darkening at the position of the transiting planet and the
disk-averaged limb-darkening, respectively555The limb-darkening correction is
applied on the spectra aligned in the planet rest frame. We used the Python
$batman$ code (Kreidberg 2015) and the system parameters from Table
LABEL:Tab_parameters to calculate the white-light transit light curve and the
limb-darkening coefficients (see Fig. 9). $F_{\mathrm{local}}(\lambda)$ is the
normalized local stellar spectrum (see Sect. 6) occulted by the planet at
phase $t$. We applied Eq. 3 only to fully in-transit orbital phases (i.e.,
obtained between the t2 and t3 contact points), while ingress, and egress were
not considered here. Indeed LD, and occulted stellar surface are not well
known at the limbs. If we neglect the RM effect and center-to-limb (CLV)
variations, the occulted local stellar spectrum $F_{\mathrm{local}}(\lambda)$
is equal to the disk-integrated stellar spectrum $F_{\mathrm{out}}(\lambda)$
(see Sect. 6), so that Eq. 3 becomes:

$\left(\frac{R_{\mathrm{pl}}(\lambda,t)}{R_{\star}}\right)^{2}=\frac{LD_{\mathrm{mean}}}{LD(t)}\left(1-\frac{(1-\delta(t))F_{\mathrm{i}}(\lambda,t)}{F_{\mathrm{out}}(\lambda,t)}\right).$
(4)

All transmission spectra fully in-transit were finally averaged (Tmean) to
create one transmission spectrum for each observed transit (right panels of
Fig. 4).

####  4.2.4 Fringing correction

Our GIANO-B spectra presented a sinusoidal fringing pattern caused by the
sapphire substrate ($\sim$0.38 mm thick) placed above the sensitive part of
the detector, which behaves as a Fabry-Pérot in generating interference
fringing. Such fringing patterns must be corrected for in studying the He I
triplet. We followed and re-adapted the second and third approaches
(Method#1b-Method#2) presented in Guilluy et al. (2019). We focused on
correcting this effect at the level of the final transmission spectra in order
to have better control over fringing in the final transmission spectrum itself
and to avoid risks in ”overfitting” the data. First, for each planet, we
binned Tmean (bin size of 0.2 nm), we thus computed the Lomb-Scargle
periodogram to find the characteristic frequency of the periodic fringing
signal present in the data $f_{\mathrm{best}}$. We then selected the most
prominent frequency of the periodogram, and we fitted the fringing pattern
using a sine function $yfit=C+A\sin(2\pi\lambda f+\phi)$, where $A$ is the
amplitude, $\phi$ the phase, $f$ is the fringing frequency (where we assumed
$f_{\mathrm{best}}$ as starting point for the fit), and $C$ is the overall
offset. We finally corrected our final transmission spectra by $yfit$.

Figure 5: $\Delta\chi^{2}$ maps of mass-loss rate and temperature for the
planets of our survey. Regions of the parameter space in red are consistent
with the non-detections in our data, while models in purple are strongly
disfavored. The thermospheric model failed to converge in white regions.
Hatched regions are physically excluded.

##  5 Interpretation of the transmission spectra

Somewhat surprisingly no He I absorption signature was detected in our sample,
as can be seen in Fig. 4. This either means that the targeted planets have no
extended atmosphere, which would be surprising given the strong irradiation of
their H/He atmosphere; or that their thermosphere’s metastable helium
population is not dense enough to be detectable within the precision of the
GIANO-B observations. Under this assumption, we can still put upper limits on
the escape rate by fitting the transmission spectra with models of the
planets’ thermospheric structure.

###  5.1 Stellar modelling

Given the scarcity of stellar high-energy measurements, we calculated the
X-EUV spectral energy distribution of the eight target stars in a consistent
manner using Table 5 in Linsky et al. 2014. This formula depends on the total
X-EUV flux emitted by the star, which is calculated based on the stellar age
and Equations 3 and 4 from Sanz-Forcada et al. 2011.

###  5.2 Thermosphere modelling

We used an approximate 1D model, the p-winds (Dos Santos et al. 2022) code,
largely based on the formulations of Oklopčić & Hirata (2018) and Lampón et
al. (2020), to calculate the thermospheric structure and resulting signature
of the metastable helium triplet. The atmospheric density and velocity
profiles were calculated according to the Parker wind approximation, assuming
an isothermal planetary outflow (Parker 1958). We assumed for all targets an
atmospheric composition of 90 % H and 10 % He (a good approximation of the
Jupiter H/He ratio) and an input stellar X-EUV spectrum (calculated as
explained in Section 5.1). The code calculates only the density profiles of
hydrogen in its neutral and ionized states, as well as that of helium in its
neutral, excited, and singly ionized states. The signature of interest is the
metastable transition at 1083.3 nm of the helium excited level. A theoretical
ideal spectrum is calculated at mid-transit without taking into account
geometrical effects and inhomogeneities of the stellar surface. This
absorption signature is compared to the observed mean transmission spectrum to
estimate upper atmosphere characteristics such as temperature and the mass-
loss rates.

Since there is no clear evidence of a helium signature, we quickly explored
the input parameter space of the p-winds models by varying only the isothermal
temperature profile, $T$, and the total atmospheric escape rate, $\dot{M}$,
while all other input parameters are fixed. For instance, H/He ratio was not a
fitting parameter. We expect that this can slightly change the derived values,
but the conclusion would still remain the same. In our models, the radius at
the top of the simulated atmosphere was set to the Roche lobe (Eggleton 1983).
We note that the value chosen for this upper radius has not been discussed in
previous studies using the p-winds code or similar codes (e.g., Oklopčić &
Hirata 2018; Lampón et al. 2020; Dos Santos et al. 2022; Kirk et al. 2022),
even though it directly controls the amount of helium that contributes to the
theoretical absorption signature. The preferred approach in the literature
seems to be increasing the radius until the neutral triplet helium density no
longer contributes significantly to the absorption signal. Yet this is hardly
compatible with the change in nature of the atmosphere beyond the Roche lobe,
from a collisional thermosphere shaped by planetary gravity, which may (at
first order) still be described by a 1D vertical structure, to an asymmetrical
exosphere shaped by the stellar gravity, radiation, and wind. Our choice to
set the upper model radius at the Roche lobe is based on the reasonable
assumption that once helium atoms escape into the exosphere, they cannot be
excited into their metastable state by collisions anymore and are quickly
photo-ionized so that these layers contribute little to the observed signature
(as supported by the lack of clear detection of extended exospheric tails in
the literature).

In our simulations, high escape rates lead to an increase in the total density
of metastable helium in the thermosphere but the densest layers are shifted to
higher altitudes above the Roche lobe, where they no longer contribute to the
theoretical signature. This boundary effect is visible in Fig. 5, with the
reappearance of fit regions compatible with the non-detection of an absorption
signature in our data. Simulations at high escape rates are therefore model-
biased and should be considered cautiously. Furthermore, we note that the code
p-winds was unable to calculate the atmospheric structure in certain regions
of the parameter space (shown in white in Fig. 5). It is still unclear whether
this is a numerical issue or a truly non-physical regime for the thermosphere.

###  5.3 Parameter space exploration

We determined whether the models were compatible with the measured
transmission spectra using $\chi^{2}$ comparison. Since no absorption
signature was detected for any of the planets we took the null hypothesis (a
flat transmission spectrum) as the best-fit model and use
$\Delta\chi^{2}=\chi^{2}_{model}-\chi^{2}_{flat}$ as a criterion to determine
3-$\sigma$ upper limits on the atmospheric mass-loss rate. We constrained the
parameter space to realistic models in mass loss, using the maximum efficiency
for a photoionization-driven isothermal Parker wind (Vissapragada et al.
2022), and in temperature, using the model of Salz et al. (2016) as a function
of the gravitational potential of the planet. Below log($-\Phi_{G}$) = log
$GM_{pl}/R_{pl}$ = 13.0 erg$\cdot$g-1, their model predicts temperatures lower
than 10 000 K, while above this limit, it predicts temperatures lower than 20
000 K. $\chi^{2}$ maps as a function of mass loss and temperature are shown in
Fig. 5 for all planets in our sample. Table 10 gathers all the derived
3-$\sigma$ upper limits.

##  6 Accurate stellar line profiles

Figure 6: Spectral intensities for different $\mu$ positions on the stellar
disk of HAT-P-33 derived with Turbospectrum. The dotted spectra are obtained
after the multiplication of the intensity spectra by the He I triplet spectral
profile. $\mu=\sqrt{1-(x^{2}+y^{2})}$ with $(x,y)$ the coordinates of a point
on the stellar disk of radius equal to one in the Cartesian referential
centered on the stellar disk.

Planet-occulted line distortions (POLD, Dethier & Bourrier 2023) can bias or
even hide planetary absorption signatures in transmission spectra (Yan et al.
2017; Casasayas-Barris et al. 2020, 2021b). They appear in particular when one
uses the disk-integrated stellar spectrum (Fout) to normalize the spectrum
that is absorbed by the planet and its atmosphere. Indeed, the line profiles
of Fout are shaped by a combination of the local effects of stellar rotation
and CLV from all over the stellar disk; thus, they are not necessarily
representative of the line profiles occulted by the planet. To mitigate the
POLDs one needs to define more accurate estimates of the local stellar
spectrum occulted by the planetary disk at each exposure. However, this
quantity is complex to estimate from observations, as stars cannot be resolved
spatially.

To estimate the planet-occulted stellar spectrum, we fit a model to the
measured disk-integrated spectrum, using a combination of analytical and
simulated theoretical local spectra. The stellar disk is discretized by a 2D
uniform square grid, each cell being associated with a specific local
intensity spectrum.

The simulated component of these intensity spectra is defined using the
Turbospectrum code for spectral
synthesis666https://github.com/bertrandplez/Turbospectrum2019 (Plez 2012).
This code uses MARCS photospheric models (Gustafsson et al.
2008)777https://marcs.astro.uu.se and spectral line-lists from VALD3
database888http://vald.astro.uu.se (Ryabchikova et al. 2015) to generate
synthetic spectra under the assumption of local thermodynamic equilibrium999We
used the interpol$\\_$marcs module to derive a MARCS model for the exact
values of temperature, metallicity and log g of our target stars. Available
for downloads at https://marcs.astro.uu.se/software.php. For each star, we
used Turbospectrum  to generate high-resolution intensity spectra for a series
of positions along the stellar radius to sample broadband limb-darkening and
CLV.
These synthetic spectra, however, do not contain the He I triplet lines at
10830Å as its formation in stellar atmospheres necessitates non-local
thermodynamical equilibrium conditions that are usually met in chromospheric
layers, whereas MARCS models focus on the photospheric layers. We thus
calculate the He I triplet absorption lines analytically, assuming Gaussian
cross-sections and a common temperature and density for the metastable helium
gas. Figure 6 shows a series of intensity spectra across the stellar disk of
HAT-P-33.

The series of synthetic+analytical intensity spectra is then interpolated over
the whole stellar grid, and Doppler-shifted according to the local radial
velocity set by the projected stellar rotational velocity (Bourrier et al.
2023). Subsequently, intensity spectra are scaled into local flux spectra
using the surface of the stellar grid cells, and summed over the whole grid to
derive a simulated disk-integrated spectrum of the target star. Finally, the
disk-integrated spectra are convolved with GIANO-B instrumental response and
resampled to match its spectral resolution. The observed and simulated disk-
integrated spectra are compared using a MCMC fit with free parameters set to
the temperature and density of the metastable helium atoms.

Figure 7 shows the results of our fits for HAT-P-33 and HAT-P-49, which have
the highest $v$sin$i_{\star}$ of our sample, highlighting the local spectrum
at disk’s center that is later used in Eq. 3. We only applied this approach to
the four targets with the highest $v$sin$i_{\star}$, as POLDs are expected to
be negligible for the other targets. For these slow rotators, the rotational
broadening of the disk-integrated line profiles is small and they remain good
proxies for the local planet-occulted lines, especially for the shallow He I
triplet lines. We note that CLV is not accounted for in our analytical
estimates of the He I lines.

Figure 7: Normalized spectra of HAT-P-33 (upper panel) and HAT-P-49 (lower
panel). We show the best fit (red curve) to the observed (blue curve) disk-
integrated spectrum, and the spectrum at the disk’s center (orange curve). The
blue vertical lines show the transition wavelength of the He I triplet.

We underline that even for the four fast-rotating targets for which we used a
more accurate proxy for the planet-occulted stellar line the amplitude of the
POLD was found to be comparable to the dispersion of the data. This is partly
due to the shallowness of the He I triplet lines, and to the fact that POLDs
partially smooth out when averaging transmission spectra in the planet rest
frame over the transit window. Indeed, POLDs shift along the stellar surface
RVs, while planetary signatures shift along the planet orbital RVs (see Fig.
11). Therefore, our final interpretation is made based on the transmission
spectra calculated with the disk-integrated spectrum.

##  7 Results

Table 2: He I measurements.

Target | Roche lobe |  gP | Excess of absorption |  Heq |  $\delta_{\mathrm{R_{P}}}$/Heq |  F${}_{\mathrm{5-504\leavevmode\nobreak\ \textnormal{Å}}}$ |  $\dot{M}$ 3$\sigma$
---|---|---|---|---|---|---|---
|  [RP] |  [m s-2] |  [$\%$] | [Km] |  |  [103 erg s-1 cm-2] |  [1010 g s-1]
HAT-P-3b | 3.647 | 17.5 | ¡ 1.9 | 424 | 92 | 7.968 | 7.772
HAT-P-33b | 2.167 | 5.1 | ¡ 1.4 | 2231 | 33 | 6.195 | 8.205
HAT-P-49b | 3.207 | 21.5 | ¡ 0.6 | 629 | 61 | 14.51 | 5.702
HD89345b | 8.213 | 7.8 | ¡ 0.7 | 855 | 76 | 0.244 | 3.899
K2-105b | 11.202 | 23.6 | ¡ 2.33 | 219 | 378 | 14.69 | 6.028
Kepler-25c | 8.148 | 5.3 | ¡ 1.86 | 1192 | 82 | 1.019 | 65.26
Kepler-68b | 8.722 | 15.2 | ¡ 0.72 | 533 | 112 | 1.176 | 8.134
WASP-47d | 9.565 | 10.9 | ¡ 3.29 | 533 | 230 | 0.577 | 7.854

101010From left to right: Roche lobe (estimated following Eggleton 1983),
planet’s surface gravity, excess of absorption, atmospheric scale heights
(computed by assuming $\mu$=1.3, Sect 7), $\delta_{\mathrm{R_{P}}}$/Heq (i.e.
the 3$\sigma$ upper limits), the stellar XUV flux at the planet position, and
the mass-loss rates computed with the p-winds code at 3$\sigma$.

Figure 8: Correlation plots. Excess of absorption (top panels),
$\delta_{\mathrm{R_{P}}}$/Heq (middle panels) and mass-loss rates (bottom
panels) as a function of stellar mass and XUV irradiation at the planet
position. Upper limits are given at 3$\sigma$. Blue stars are targets reported
from Allart et al. (2023), the detections and the non-detections are plotted
in dark- and light-blue, respectively. We note that K2-105 b observations
suffered from GIANO-B’s auto-guide problems and low S/N, so the limit we set
on $\delta_{\mathrm{R_{P}}}/H_{\mathrm{eq}}$ is not constraining. We
highlighted in gray the area of the parameter space that accordingly to Allart
et al. (2023) seems to favor the detections. The errors on the XUV flux have
been calculated with the error on the stellar ages.

The presence of an extended and possibly escaping helium atmosphere would
appear as an absorption feature in the transmission spectrum in the planet’s
rest frame at the position of the stellar helium triplet. Unfortunately, as
shown in Fig. 4, we did not detect significant helium absorption features for
any of our targets. We thus evaluated 3$\sigma$ upper limits from the data
itself as in Allart et al. (2023). Following an approach similar to that of
Cubillos et al. (2017), we computed Allan plots (see Fig. 12) to estimate the
noise present in the data. We assumed the white noise $\sigma_{1}$ to be the
standard deviation (hereafter, root mean square, rms) of the transmission
spectrum, excluding the helium triplet region (1083.0-1083.6 nm). Then, we
binned the transmission spectrum by bins of $N$ elements each and calculated
the rms of the binned transmission spectrum. We repeat the process for a wide
range of $N$ elements for bin (from 1 to 42). In the absence of correlated
noise, $\sigma_{1}$ scales as $\sqrt{N}$. We then fitted the rms in a log-log
space to derive the trend of the noise, the fitted rms at 0.075 nm is the 1
$\sigma$ uncertainty. We set three times this value as the 3$\sigma$ upper
limit on the signature contrast, $c$.
An alternative approach to providing more rigorous estimations of the noise
present in the data requires the use of Gaussian processes (See Appendix Sect.
B). However, for the rest of the analysis, to maintain consistency with the
results published in Allart et al. (2023), we used upper limits estimated from
Allan plots.
We then derived an upper limit on the equivalent opaque radius
$\delta_{\mathrm{R_{P}}}$, namely, the height of an opaque atmospheric layer
that would produce the observed absorption signal, as:

$\delta_{\mathrm{R_{P}}}=\sqrt{(R_{\mathrm{P}}^{2}+R_{\mathrm{\star}}^{2}\times
c)}-R_{\mathrm{P}}\,,$ (5)

where $R_{\mathrm{p}}$ and ${R_{\star}}$ are the planetary and stellar radius,
respectively.
We finally computed the quantity $\delta_{\mathrm{R_{P}}}/H_{\mathrm{eq}}$
(Nortmann et al. 2018), which expresses the number of scale heights
($H_{\mathrm{eq}}$) probed by the atmosphere in the considered spectral range,
with $H_{\mathrm{eq}}$ = $\frac{k_{\mathrm{B}}T\mathrm{eq}}{\mu g}$ and
$k_{\mathrm{B}}$ the Boltzmann constant, $T_{\mathrm{eq}}$ the planetary
equilibrium temperature (listed in Table LABEL:Tab_parameters), $g$ the
planetary gravity computed from the planetary mass and radius (reported in
Table 10), $\mu$ the mean molecular weight (for which we assumed a hydrogen-
dominated atmosphere and hence a value of 1.3 times the mass of a hydrogen
atom). Table 10 reports the derived $\delta_{\mathrm{R_{P}}}/H_{\mathrm{eq}}$
values for each investigated planet.

We explored how the derived constraints vary as a function of the stellar mass
and XUV flux between 5 and 504 Å, which are the energies mainly responsible
for the population of the metastable He I level (Sanz-Forcada et al.
2011)111111For consistency, in Appendix in Fig. 13 we reported also the same
plots as a function of the insolation level of mid-UV flux, which accordingly
to Oklopčić 2019 ionizes the Helium’s metastable state. We focused on these
two parameters because Allart et al. (2023) showed that they do yield visible
trends with $\delta_{\mathrm{R_{P}}}/H_{\mathrm{eq}}$. Trends related to the
excess of absorption and atmospheric extension are shown in the top and middle
panels of Fig. 8. All our targets are outside the area of the parameter space,
with masses between $\sim$0.6 and $\sim$0.85 M⊙, as pointed out by Allart et
al. (2023) to favor the He I detection. This range of stellar masses
corresponds to K-stars, in agreement with the predictions of Oklopčić (2019),
thus our non-detections are not entirely unexpected. However, half of our non-
detections do not agree with the XUV flux range found to favor the presence of
He I in Allart et al. (2023), 1400-17800 erg s${}^{-1}\,$cm-2. However, the
XUV flux values depend on the model and are associated with star ages, which
are typically not well-constrained. So we have to be cautious with these
values. Finally, as already highlighted in Allart et al. (2023), even if
surprising, there are no clear correlations between Ṁ and the stellar mass or
XUV flux (bottom panels of Fig. 8).

We chose to focus on the targets analyzed in this paper and in Allart et al.
(2023), rather than other results from the literature because we derived the
absorption, atmospheric extension, and mass-loss values homogeneously with the
same methodology.

##  8 Summary and conclusions

In this paper, we describe our He I survey of 9 planets at the edge of the
Neptunian desert, with the goal of understanding the role of photoevaporation
in sculpturing this feature. We analyzed observations gathered with the high-
resolution GIANO-B spectrograph mounted on the TNG, and we used the
transmission spectroscopy technique to detect a possible extended or
evaporating helium atmosphere in the investigated planets. We found no sign of
planetary absorption at the position of the stellar He I triplet in any of the
investigated targets, and we thus provided 3$\sigma$ upper limits on the He I
absorption. We underline that the GIANO-B transmission spectra are affected by
various systematics that are not fully understood and difficult to properly
remove. These systematics may be caused by low data quality (e.g., low S/N) or
instrumental effects (e.g., auto-guide problems).

We interpreted our derived transmission spectra with the p-wind code (Dos
Santos et al. 2022) and we attempted to interpret our findings by putting them
in the wider context of the measurements presented in Allart et al. 2023
submitted. We searched for correlations with the stellar mass and XUV flux
(e.g., Fossati et al. 2022), which are thought to be key drivers in the
formation of the He I triplet. Constraints from our sample support the trend
of $\delta_{\mathrm{R_{P}}}/H_{\mathrm{eq}}$, with the stellar mass proposed
by Allart et al. (2023), which remains a good indicator for the presence of
metastable helium in exoplanet atmospheres. In addition, they are not
incompatible with the trend highlighted in Allart et al. (2023) with the XUV
flux as they are not constraining enough to reach a better precision. We
stress the importance of carrying out helium surveys with the same instrument
and analyzing them with the same data reduction technique, as heterogeneity
can obscure any trends in the data (Vissapragada et al. 2022). Several
instruments are now available to perform this kind of homogeneous survey such
as NIRSPEC, SPIROU, CARMENES, and GIANO-B.

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###### Acknowledgements.

We thank the referee for the comments and suggestions. We thank V.l Andretta
for his help. G.G. acknowledge financial contributions from PRIN INAF 2019,
and from the agreement ASI-INAF number 2018-16-HH.0 (THE StellaR PAth
project). R. A. is a Trottier Postdoctoral Fellow and acknowledges support
from the Trottier Family Foundation. This work was supported in part by a
grant from the Fonds de Recherche du Québec - Nature et Technologies (FRQNT).
This work was funded by the Institut Trottier de Recherche sur les
Exoplaneètes (iREx). This work has been carried out within the framework of
the NCCR PlanetS supported by the Swiss National Sci- ence Foundation under
grants 51NF40182901 and 51NF40205606. This project has received funding from
the European Research Council (ERC) under the Eu- ropean Union’s Horizon 2020
research and innovation programme (project Spice Dune, grant agreement No
947634). This material reflects only the authors views and the Commission is
not liable for any use that may be made of the information contained therein.
This work has made use of the VALD database, operated at Uppsala University,
the Institute of Astronomy RAS in Moscow, and the University of Vienna. This
work has made use of the Turbospectrum code for spectral synthesis

##  Appendix A Additional figures and tables

Table 3: Planets with He I study reported in the literature.

Planet | Status | Reference
---|---|---
WASP-11b | ✗ | Allart et al. 2023
WASP-12b | ✗ | Kreidberg & Oklopčić 2018
WASP-39b | ✗ | Allart et al. 2023
WASP-48b | ✗ | Bennett et al. 2023
WASP-52b | $\sim$ | Vissapragada et al. 2020; Kirk et al. 2022; Allart et al. 2023
WASP-69b | ✓ | Nortmann et al. 2018; Vissapragada et al. 2020; Allart et al. 2023
WASP-80b | ✗ | Fossati et al. 2022; Vissapragada et al. 2022; Allart et al. 2023
WASP-76b | ✗ | Casasayas-Barris et al. 2021a
WASP-107b | ✓ | Spake et al. 2018; Allart et al. 2019; Kirk et al. 2020
WASP-127b | ✗ | dos Santos et al. 2020a; Allart et al. 2023
WASP-177b | ✗ | Kirk et al. 2022; Vissapragada et al. 2022
HD189733b | ✓ | Salz et al. 2018; Guilluy et al. 2020; Zhang et al. 2022a; Allart et al. 2023
HD209458b | $\sim$ | Nortmann et al. 2018; Alonso-Floriano et al. 2019
HD97658b | ✗ | Kasper et al. 2020
HD63433b | ✗ | Zhang et al. 2022b
HD63433c | ✗ | Zhang et al. 2022b
HAT-P-11b | ✓ | Allart et al. 2018; Mansfield et al. 2018; Allart et al. 2023
HAT-P-18b | ✓ | Paragas et al. 2021; Vissapragada et al. 2022
HAT-P-26b | ✓ | Vissapragada et al. 2022
HAT-P-32b | ✓ | Czesla et al. 2022
NGTS-5b | $\sim$ | Vissapragada et al. 2022
55Cnce | ✗ | Zhang et al. 2021
Kelt-9b | ✗ | Nortmann et al. 2018
GJ3470b | $\sim$ | Ninan et al. 2020; Allart et al. 2023; Palle et al. 2020
GJ436b | ✗ | Nortmann et al. 2018
GJ1214b | $\sim$ | e.g.,Orell-Miquel et al. 2022; Kasper et al. 2020; Allart et al. 2023
GJ9827b | ✗ | Carleo et al. 2021; Krishnamurthy et al. 2023
GJ9827d | ✗ | Kasper et al. 2020; Carleo et al. 2021; Krishnamurthy et al. 2023
TOI-560b | ✓ | Zhang et al. 2022a, 2023
TOI-1728b | ✗ | Kanodia et al. 2020
TOI-1430b | ✓ | Zhang et al. 2023
TOI-1683b | ✓ | Zhang et al. 2023
TOI-1807b | ✗ | Gaidos et al. 2023
TOI-2076b | ✗ | Zhang et al. 2023; Gaidos et al. 2023
TOI-3757b | ✗ | Kanodia et al. 2022
TOI-1235b | ✗ | Krishnamurthy et al. 2023
TOI-2136b | ✗ | Kawauchi et al. 2022
Trappist-1b | ✗ | Krishnamurthy et al. 2021
Trappist-1e | ✗ | Krishnamurthy et al. 2021
Trappist-1f | ✗ | Krishnamurthy et al. 2021
V1298Taub | $\sim$ | e.g., Vissapragada et al. 2021; Gaidos et al. 2022
V1298Tauc | ✗ | e.g., Vissapragada et al. 2021
V1298Taud | ✓ | e.g., Vissapragada et al. 2021
AU Mic b | ✗ | Hirano et al. 2020
K2-25b | ✗ | Gaidos et al. 2020b
K2-100b | ✗ | Gaidos et al. 2020a

121212From left to right: the investigated planet, the status of the detection
(with ✓, ✗, and $\sim$ indicating detections, non-detections, and not-clear
results respectively), and references.

Table 4: Adopted parameters Parameter | Value | Reference
---|---|---
HAT-P-3 |  |
$\bullet$ Stellar parameters |  |
Spectral type | K1 | Grieves et al. (2018)
Stellar mass, M⋆ (M⊙) | 0.925 $\pm$ 0.046 | Mancini et al. (2018)
Stellar radius, R⋆ (M⊙) | 0.850 $\pm$ 0.021 | Mancini et al. (2018)
Stellar age, $\tau$ (Gyr) | $2.9^{+2.7}_{-4.9}$ | Mancini et al. (2018)
Effective temperature, Teff (K) | 5190 $\pm$ 80 | Mancini et al. (2018)
Metallicity (dex) | 0.24 $\pm$ 0.08 (Fe/H) | Mancini et al. (2018)
log g (log10(cm s-2)) | 4.545 $\pm$ 0.023 | Mancini et al. (2018)
Systemic velocity, vsys (km s-1) | -23.379680 | DREAM I
Limb-darkening coefficients $\mu_{1}$ | 0.216 | EXOFAST11footnotemark: 1
$\mu_{2}$ | 0.286 | EXOFAST11footnotemark: 1
Stellar projected velocity, $v$sin$i_{\star}$ (km s-1) | $0.46^{+0.22}_{-0.25}$ | DREAM 1
Magnitude (J-band) | 9.936$\pm$0.022 | Cutri et al. (2003)
$\bullet$ Planetary parameters |  |
Orbital period, P (days) | 2.89973797 $\pm$ 0.00000038 | Baluev et al. (2019)
Transit epoch, T0 (BJDTDB) | 2454218.75960 $\pm$ 0.00016 | DREAM I
Eccentricity, e | 0.0 (fixed) | Mancini et al. (2018)
Argument of periastron, $\omega_{\star}$ | 90 (fixed) | Mancini et al. (2018)
Stellar reflex velocity, K⋆ (m s-1) | 90.63 $\pm$ 0.58 | Mancini et al. (2018)
Scaled separation, a/R⋆ | 9.8105 $\pm$ 0.2667 | Mancini et al. (2018)
Orbital inclination, $i$ | 86.31 $\pm$ 0.19 deg | Mancini et al. (2018)
Planet-to-star radius ratio, RP/R⋆ | 0.11091 $\pm$ 0.00048 | Baluev et al. (2019)
Planetary mass, Mpl (Mjup) | 0.595 $\pm$0.019 | Mancini et al. (2018)
Planetary density, $\rho_{\mathrm{pl}}$ (g cm-3) | 0.9750$\pm$0.1000 | Mancini et al. (2018)
Projected spin-orbit angle, $\lambda$ (deg) | -25.3${}^{+29.4}_{-22.8}$ | DREAM I
Planet radial-velocity semi-amplitude, Kpl(km s-1) | 145.2$\pm$2.4 | This paper22footnotemark: 2
Equilibrium temperature, Teq(K) | 1170$\pm$17 | Mancini et al. (2018)
HAT-P-33 |  |
$\bullet$ Stellar parameters |  |
Spectral type | F4 | Luo et al. (2018)
Stellar mass, M⋆ (M⊙) | 1.42 ${}^{+0.16}_{-0.15}$ | Wang et al. (2017)
Stellar radius, R⋆ (M⊙) | 1.91${}^{+0.26}_{-0.20}$ | Wang et al. (2017)
Stellar age, $\tau$ (Gyr) | 2.30 $\pm$ 0.30 | Bonomo et al. (2017)
Effective temperature, teff (K) | 6460 ${}^{+300}_{-290}$ | Wang et al. (2017)
Metallicity (dex) | 0.01 $\pm$ 0.31 [Fe/H] | Wang et al. (2017)
Surface gravity, log g⋆ (cgs) | $4.030_{-0.090}^{+0.079}$ | Wang et al. (2017)
Systemic velocity, vsys (km s-1) | 23.080601 | DREAM I
Limb-darkening coefficients $\mu_{1}$ | 0.097 | EXOFAST11footnotemark: 1
$\mu_{2}$ | 0.301 | EXOFAST11footnotemark: 1
Stellar projected velocity, $v$sin$i_{\star}$ (km s-1) | 15.57 $\pm$ 0.31 | DREAM I
Magnitude (J-band) | 10.263$\pm$0.021 | Cutri et al. (2003)
$\bullet$ Planetary parameters |  |
Orbital period, P (days) | 3.47447773 $\pm$ 0.00000066 | DREAM I
Transit epoch, T0 (BJDTDB) | 2456684.86508 $\pm$ 0.00027 | DREAM I
Eccentricity, e | 0.180 ${}_{-0.096}^{+0.110}$ | DREAM I
Argument of periastron, $\omega_{\star}$ (deg) | 88${}^{+33}_{-34}$ | Wang et al. (2017)
Stellar reflex velocity, K⋆ m s-1 | 74.4$\pm$8.5 | DREAM I
Scaled separation, a/R⋆ | 5.69${}^{+0.58}_{-0.59}$ | Wang et al. (2017)
Orbital inclination, $i$ (deg) | 88.2 ${}^{+1.2}_{-1.3}$ | Wang et al. (2017)
Planet-to-star radius ratio, RP/R⋆ | 0.10097 ${}^{+0.00056}_{-0.00052}$ | Wang et al. (2017)
Planetary mass, Mpl (Mjup) | 0.72 ${}^{+0.13}_{-0.12}$ | Wang et al. (2017)
Planetary density, $\rho_{\mathrm{pl}}$ (g cm-3) | 0.134${}_{-0.042}^{+0.053}$ | Wang et al. (2017)
Projected spin-orbit angle, $\lambda$ (deg) | 5.9$\pm$4.1 deg | DREAM I
Planet radial velocity semi-amplitude, Kpl(km s-1) | 160.6${}^{+6.9}_{-6.3}$ | This paper22footnotemark: 2
Equilibrium temperature, Teq(K) | 1782$\pm$28 | Hartman et al. (2011)
HAT-P-49 |  |
$\bullet$ Stellar parameters |  |
Spectral type | F3 | DREAM I
Stellar mass, M⋆ (M⊙) | 1.543$\pm$0.051 | Bieryla et al. (2014)
Stellar radius, R⋆ (R⊙) | $1.833{+0.138}_{-0.076}$ | Bieryla et al. (2014)
Stellar age $\tau$ (Gyr) | 1.50 $\pm$ 0.20 | Bonomo et al. (2017)
Effective temperature, Teff (K) | 6820$\pm$52 | Bieryla et al. (2014)
Metallicity (dex) | 0.074$\pm$0.080 [Fe/H] | Bieryla et al. (2014)
Surface gravity, log g⋆ (cgs) | 4.10$\pm$0.04 | Bieryla et al. (2014)
Systemic velocity, vsys (km s-1) | 14.208478 | DREAM I
Limb-darkening coeffcients $\mu_{1}$ | 0.078 | EXOFAST11footnotemark: 1
$\mu_{2}$ | 0.303 | EXOFAST11footnotemark: 1
Stellar projected velocity, $v$sin$i_{\star}$ (km s-1) | 10.68${}^{+0.46}_{-0.47}$ | DREAM I
Magnitude (J-band) | 9.550$\pm$0.020 | Cutri et al. (2003)
$\bullet$ Planetary parameters |  |
Orbital period, P (days) | 2.6915539$\pm$0.0000012 | DREAM I
Transit epoch, T0 (BJDTDB) (BJDTDB) | 2456975.61736 $\pm$0.00050 | DREAM I
Eccentricity, e | 0.0 (fixed) | Bieryla et al. (2014)
Argument of periastron, $\omega_{\star}$ (deg) | 90 (fixed) | Bieryla et al. (2014)
Stellar reflex velocity, K⋆ (m s-1) | 177.6 $\pm$ 16.0 | DREAM I
Scaled separation, a/R⋆ | 5.13 ${}^{+0.19}_{-0.30}$ | Bieryla et al. (2014)
Orbital inclination, $i$ (deg) | 86.2$\pm$1.7 | Bieryla et al. (2014)
Planet-to-star radius ratio, RP/R⋆ | 0.0792 $\pm$ 0.0019 | Bieryla et al. (2014)
Planetary Mpl (Mjup) | 1.730 $\pm$ 0.205 | Bieryla et al. (2014)
Planetary density, $\rho_{\mathrm{pl}}$ (g cm-3) | 0.75$\pm$0.17 | Bieryla et al. (2014)
Projected spin-orbit angle, $\lambda$ (deg) | -97.7$\pm$1.8 | DREAM I
Planet radial velocity semi-amplitude, Kpl(km s-1) | 176.5$\pm$2.0 | This paper22footnotemark: 2
Equilibrium temperature, Teq(K) | 2131${}^{+69}_{-42}$ | Bieryla et al. (2014)
HD89345 |  |
$\bullet$ Stellar parameters |  |
Spectral type | G5 | Cannon & Pickering (1993)
Stellar mass, M⋆ (M⊙) | 1.120${}^{+0.040}_{-0.010}$ | Van Eylen et al. (2018)
Stellar radius, R⋆ (R⊙) | 1.657${}^{+0.020}_{-0.004}$ | Van Eylen et al. (2018)
Stellar age, $\tau$ (Gyr) | 9.40 ${}^{+0.40}_{-1.30}$ | Van Eylen et al. (2018)
Effective temperature, Teff (K) | 5499 $\pm$ 73 | Van Eylen et al. (2018)
Metallicity (dex) | 0.45$\pm$ 0.04 [Fe/H] | Van Eylen et al. (2018)
Surface gravity log g⋆ (log10(cm s-2)) | 4.044${}^{+0.006}_{-0.004}$ | Van Eylen et al. (2018)
Systemic velocity, vsys (km s-1) | 2.223394 | DREAM I
Limb-darkening coeffcients $\mu_{1}$ | 0.182 | EXOFAST11footnotemark: 122footnotemark: 2
$\mu_{2}$ | 0.300 | EXOFAST11footnotemark: 1
Stellar projected velocity, $v$sin$i_{\star}$ (km s-1) | 0.58$\pm$0.28 | DREAM I
Magnitude (J-band) | 8.091 $\pm$0.020 | Cutri et al. (2003)
$\bullet$ Planetary parameters |  |
Orbital period, P (Days) | 11.8144024 $\pm$0.0000066 | DREAM I
Transit epoch T0 (BJDTDB) | 2458740.81147$\pm$0.00044 | DREAM I
Eccentricity, e | 0.208 $\pm$ 0.039 | DREAM I
Argument of periastron $\omega$ (deg) | 21.7 $\pm$ 19.1 | DREAM I
Stellar reflex velocity, K⋆ (m s-1) | 9.1 $\pm$ 0.5 | DREAM I
Scaled separation, a/R⋆ | 13.625 $\pm$ 0.027 | Van Eylen et al. (2018)
Orbital inclination, $i$ (deg) | 87.68 $\pm$ 0.10 | DREAM I
Planet-to-star radius ratio, RP/R⋆ | 0.03696 $\pm$ 0.00041 | DREAM I
Planetary mass, Mpl (Mjup) | 0.112$\pm$0.010 | Van Eylen et al. (2018)
Planetary density, $\rho_{\mathrm{pl}}$ (g cm-3) | 0.609$\pm$0.067 | Van Eylen et al. (2018)
Projected spin-orbit angle, $\lambda$ (deg) | 74.2${}^{+33.6}_{-32.5}$ | DREAM 1
Planet radial velocity semi-amplitude, Kpl(km s-1) | 99.2${}^{+1.4}_{-0.9}$ | This paper22footnotemark: 2
Equilibrium temperature, Teq(K) | 1053$\pm$14 | Van Eylen et al. (2018)
K2-105 |  |
$\bullet$ Stellar parameters |  |
Spectral type | G5 | This paper55footnotemark: 5
Stellar mass, M⋆ (M⊙) | 1.05 $\pm$ 0.02 | Castro-González et al. (2022)
Stellar radius, R⋆ (R⊙) | 0.97 $\pm$ 0.01 | Castro-González et al. (2022)
Stellar age, $\tau$ (Gyr) | ¿0.6 | Narita et al. (2017)
Effective temperature, Teff (K) | 5636${}^{+49}_{-52}$ | Castro-González et al. (2022)
Metallicity (dex) | 0.23${}^{+0.04}_{-0.03}$ [Fe/H] | Castro-González et al. (2022)
Surface gravity log g⋆ (log10(cm s-2)) | 4.49 $\pm$ 0.01 | Castro-González et al. (2022)
Systemic velocity, vsys (km s-1) | -32.390637 | DREAM I
Limb-darkening coefficients $\mu_{1}$ | 0.169 | EXOFAST11footnotemark: 1
$\mu_{2}$ | 0.299 | EXOFAST11footnotemark: 1
Stellar projected velocity, $v$sin$i_{\star}$ (km s-1) | 2.13${}^{+0.96}_{-0.92}$ | DREAM I
Magnitude (J-band) | 10.541$\pm$0.02 | Cutri et al. (2003)
$\bullet$ Planetary parameters |  |
Orbital period, P (days) | 8.2669897$\pm$0.0000057 | DREAM I
Transit epoch, T0 (BJDTDB) | 2458363.2387${}^{+0.00069}_{-0.000633}$ | DREAM I
Eccentricity, e | 0 (fixed) | DREAM I
Argument of periastron, $\omega$ (deg) | 90 (fixed) | DREAM I
Stellar reflex velocity, K⋆ (m s-1) | 9.4 $\pm$ 5.8 | Narita et al. (2017)
Scaled separation, a/R⋆ | 17.39 $\pm$ 0.19 | DREAM I
Orbital inclination, $i$ (deg) | 88.62 $\pm$ 0.10 | DREAM I
Planet-to-star radius ratio, RP/R⋆ | 0.03332 $\pm$ 0.00067 | DREAM I
Planetary mass, Mpl (Mjup) | 0.094 $\pm$ 0.060 | Narita et al. (2017)
Planetary density, $\rho_{\mathrm{pl}}$ (g cm-3) | 2.3${}^{+1.7}_{-1.6}$ | This paper
Projected spin-orbit angle, (deg) $\lambda$ | -81${}^{+50}_{-47}$ | DREAM I
Planet radial-velocity semi-amplitude, Kpl(km s-1) | 107.0$\pm$0.7 | This paper22footnotemark: 2
Equilibrium temperature, Teq(K) | 814$\pm$12 | Livingston et al. (2018)
Kepler-25 |  |
$\bullet$ Stellar Parameters |  |
Spectral type | F8 | DREAM I
Stellar mass, M⋆ (M⊙) | 1.26 $\pm$ 0.03 | Benomar et al. (2014)
Stellar radius, R⋆ (R⊙) | 1.34$\pm$0.01 | Benomar et al. (2014)
Stellar age, $\tau$ (Gyr) | 2.75 $\pm$ 0.30 | Benomar et al. (2014)
Effective temperature, Teff (K) | 6354$\pm$27 | Benomar et al. (2014)
Metallicity [Fe/H] (dex) | 0.11$\pm$0.03 | Benomar et al. (2014)
Surface gravity log g (log10(cm s-2)) | 4.285$\pm$0.003 | Benomar et al. (2014)
Systemic velocity, vsys (km s-1) | -8.633258 | DREAM I
Limb-darkening coeffcients $\mu_{1}$ | 0.106 | EXOFAST11footnotemark: 1
$\mu_{2}$ | 0.304 | EXOFAST11footnotemark: 1
Stellar projected velocity, $v$sin$i_{\star}$ (km s-1) | 8.89${}^{+0.59}_{-0.63}$ | DREAM I
$\bullet$ Planetary parameters |  |
Magnitude (J-band) | 9.764 $\pm$0.020 | Cutri et al. (2003)
$\star$ Planet, $b$ |  |
Orbital period, P (days) | 6.2385347882 | Battley et al. (2021)
Transit epoch, T0 (BJDTDB) | 2458648.00807${}^{+0.00057}_{-0.00051}$ | DREAM I
Eccentricity, e | 0.0029 ${}^{0.0023}_{-0.0017}$ | Mills et al. (2019)
$\sqrt{e}\,\cos{\omega}$ | 0.042 ${}^{0.017}_{-0.036}$ | Mills et al. (2019)
$\sqrt{e}\,\sin{\omega}$ | 0.007 ${}^{0.038}_{-0.035}$ | Mills et al. (2019)
Stellar reflex velocity K⋆ (m s-1) | 2.6$\pm$0.7 | This paper33footnotemark: 3
Orbital inclination $i$ (deg) | 87.173${}^{0.084}_{-0.083}$ | Mills et al. (2019)
Planet-to-star radius ratio RP/R⋆ | 0.019160 ${}^{+5.1e-5}_{-4.8e-5}$ | Mills et al. (2019)
Planetary Mpl | 0.0275 ${}^{0.0079}_{-0.0073}$ | Mills et al. (2019)
$\star$ Planet $c$ |  |
Orbital period P (days) | 12.720370495 $\pm$ 0.000001703 | Battley et al. (2021)
Transit epoch T0 (BJDTDB) | 2458649.55482 ${}_{-0.00051}^{+0.00057}$ BJDTDB | DREAM I
Eccentricity e | 0.0061 ${}^{+0.0049}_{-0.0041}$ | Mills et al. (2019)
$\sqrt{e}\,\cos{\omega}$ | -0.024 ${}^{0.067}_{-0.053}$ | Mills et al. (2019)
$\sqrt{e}\,\sin{\omega}$ | 0.004 ${}^{0.065}_{-0.062}$ | Mills et al. (2019)
Stellar reflex velocity, K⋆ (m s-1) | 3.6${}^{+0.3}_{-0.4}$ | This paper33footnotemark: 3
Scaled separation, a/R⋆ | 18.336 $\pm$ 0.27 | Mills et al. (2019)
Orbital inclination, $i$ (deg) | 87.236 ${}_{-0.039}^{+0.042}$ | Mills et al. (2019)
Planet-to-star radius ratio, RP/R⋆ | 0.03637 $\pm$ 0.00012 | Mills et al. (2019)
Planetary mass, Mpl (Mjup) | 0.0479 ${}^{+0.0041}_{-0.0051}$ | Mills et al. (2019)
Planetary density | 0.588${}^{+0.053}_{-0.061}$ g cm-3 | Mills et al. (2019)
Projected spin-orbit angle, $\lambda$ | -0.9${}^{+7.7}_{-6.4}$ | DREAM I
Planet radial-velocity semi-amplitude, Kpl(km s-1) | 98.4$\pm$0.8 | This EXOFAST22footnotemark: 2
Equilibrium temperature, Teq(K) | 992$\pm$8 | This work44footnotemark: 4
$\star$ Planet, $d$ |  |
Orbital period, P (days) | 122.40${}^{+0.80}_{-0.71}$ d | Mills et al. (2019)
Transit epoch, T0 (BJDTDB) | 2455715.0${}^{+6.8}_{-7.2}$ | DREAM I
Eccentricity, e | 0.13${}^{+0.13}_{-0.09}$ | Mills et al. (2019)
$\sqrt{e}\,\cos{\omega}$ | 0.07 ${}^{0.027}_{-0.029}$ | Mills et al. (2019)
$\sqrt{e}\,\sin{\omega}$ | 0.16 ${}^{0.23}_{-0.28}$ | Mills et al. (2019)
Stellar reflex velocity, K⋆ (m s-1) | 8.0$\pm$0.2 | This paper33footnotemark: 3
Minimum mass, M${}_{\mathrm{pl}}\,sini$ (Mjup) | 0.226${}^{0.032}_{-0.031}$ | Mills et al. (2019)
Kepler-68 |  |
$\bullet$ Stellar parameters |  |
Spectral type | G1 | Grieves et al. (2018)
Stellar mass, M⋆ (M⊙) | 1.079$\pm$0.051 | Gilliland et al. (2013)
Stellar radius, R⋆ (R⊙) | 1.243 $\pm$ 0.019 | Gilliland et al. (2013)
Stellar age, $\tau$ (Gyr) | 6.3 $\pm$ 1.7 | Gilliland et al. (2013)
Effective temperature, Teff (K) | 5793$\pm$74 | Gilliland et al. (2013)
Metallicity (dex) | 0.12$\pm$0.074 [Fe/H] | Gilliland et al. (2013)
Surface gravity, log g (log10(cm s-2)) | 4.281$\pm$0.008 | Gilliland et al. (2013)
Systemic velocity, vsys (km s-1) | -20.762823 | DREAM I
Limb-darkening coefficients $\mu_{1}$ | 0.148 | EXOFAST11footnotemark: 1
$\mu_{2}$ | 0.301 | EXOFAST11footnotemark: 1
Stellar projected velocity, $v$sin$i_{\star}$ (km s-1) | 0.5$\pm$0.5 | DREAM I
Magnitude (J-band) | 8.975 $\pm$0.046 | Cutri et al. (2003)
$\bullet$ Planetary parameters |  |
$\star$ Planet $b$ |  |
Orbital period, P (days) | 5.3987525913 $\pm$ 0.0000005231 | Gajdoš et al. (2019)
Transit epoch, T0 (BJDTDB) | 2455006.85878000 $\pm$ 0.00007639 | Gajdoš et al. (2019)
Eccentricity, e | 0.0 (fixed) | Mills et al. (2019)
Argument of periastron, $\omega$ (deg) | 90 (fixed) | Mills et al. (2019)
Stellar reflex velocity, K⋆ | 2.70${}^{+0.48}_{-0.49}$ m s-1 | Mills et al. (2019)
Scaled separation, a/R⋆ | 10.68 $\pm$ 0.14 | Gilliland et al. (2013)
Orbital inclination, $i$ (deg) | 87.60 $\pm$ 0.90 | Gilliland et al. (2013)
Planet-to-star radius, ratio RP/R⋆ | 0.01700 $\pm$ 0.00046 | Gilliland et al. (2013)
Planetary mass, Mpl (Mjup) | 0.026 ${}^{+0.007}_{-0.008}$ | Gilliland et al. (2013)
Planetary density, $\rho_{\mathrm{pl}}$ (g cm-3) | 3.32 ${}^{+0.86}_{-0.98}$ | Gilliland et al. (2013)
Projected spin-orbit angle, $\lambda$ (deg) | non-detection | DREAM I
Planet radial-velocity semi-amplitude, Kpl(km s-1) | 124.4$\pm$2.0 | This paper22footnotemark: 2
Equilibrium temperature, Teq(K) | 1280$\pm$90 | Gilliland et al. (2013)
$\star$ Planet $c$ |  |
Orbital period, P (days) | 9.60502738150$\pm$ 0.0000132365 d | Gajdoš et al. (2019)
Transit epoch, T0 (BJDTDB) | 2454969.38207000 $\pm$ 0.00110495 | Gajdoš et al. (2019)
Eccentricity, e | 0.0 (fixed) | Mills et al. (2019)
Argument of periastron, $\omega$ (deg) | 90 (fixed) | Mills et al. (2019)
Stellar reflex velocity, K⋆ (m s-1) | 0.59 ${}^{+0.50}_{-0.52}$ | Mills et al. (2019)
$\star$ Planet $d$ |  |
Orbital period, P (days) | 634.6 ${}^{+4.1}_{-3.7}$ | Mills et al. (2019)
Transit epoch, T0 (BJDTDB) | 2455878 $\pm$ 11 | Mills et al. (2019)
Eccentricity, e | 0.112 ${}_{-0.034}^{+0.035}$ | Mills et al. (2019)
Argument of periastron, $\omega$ (deg) | -64.74${}_{-20.63}^{+25.78}$ | Mills et al. (2019)
Stellar reflex velocity, K⋆ (m s-1) | 17.75${}^{+0.50}_{-0.49}$ | Mills et al. (2019)
WASP-47 |  |
$\bullet$ Stellar parameters |  |
Spectral type | G9 | Hellier et al. (2012)
Stellar mass, M⋆ (M⊙) | 1.040 $\pm$0.031 | Vanderburg et al. (2017)
Stellar radius, R⋆ (R⊙) | 1.137 $\pm$ 0.013 | Vanderburg et al. (2017)
Stellar age, $\tau$ (Gyr) | 6.5 ${}^{+2.6}_{-1.2}$ | Almenara et al. (2016)
Effective temperature, Teff (K) | 5552$\pm$75 | Vanderburg et al. (2017)
Metallicity (dex) | 0.38$\pm$0.05 [Fe/H] | Vanderburg et al. (2017)
Surface gravity, log g (log10(cm s-2)) | 4.3437$\pm$0.0063 | Vanderburg et al. (2017)
Systemic velocity, vsys (km s-1) | -25.847809453 | Vanderburg et al. (2017)
Limb-darkening coefficients $\mu_{1}$ | 0.179 | EXOFAST11footnotemark: 1
$\mu_{2}$ | 0.299 | EXOFAST11footnotemark: 1
Stellar projected velocity, $v$sin$i_{\star}$ (km s-1) | 1.80${}^{+0.24}_{-0.16}$ | DREAM I
Magnitude (J-band) | 10.613$\pm$0.022 | Cutri et al. (2003)
$\bullet$ Planetary parameters |  |
$\star$ Planet $b$ |  |
Orbital period, P (days) | 4.1591492 $\pm$0.000006 | Bryant & Bayliss (2022)
Transit epoch, T0 (BJDTDB) | 2457007.932103 $\pm$ 0.000019 | Bryant & Bayliss (2022)
Eccentricity, e | 0 (fixed) | Bryant & Bayliss (2022)
Argument of periastron, $\omega$ (deg) | 90 (fixed) | Bryant & Bayliss (2022)
Stellar reflex velocity, K⋆ (m s-1) | 140.84 $\pm$0.40 | Bryant & Bayliss (2022)
$\star$ Planet $c$ |  |
Orbital period, P (days) | 588.8 $\pm$ 2.0 | Bryant & Bayliss (2022)
Transit epoch, T0 (BJDTDB) | 2457763.1 $\pm$ 4.3 | Bryant & Bayliss (2022)
Eccentricity, e | 0.296 $\pm$ 0.016 | Bryant & Bayliss (2022)
Argument of periastron, $\omega$ (deg) | 112\. $\pm$ 4.3 | Bryant & Bayliss (2022)
Stellar reflex velocity, K⋆ (m s-1) | 31.04 $\pm$ 0.40 | Bryant & Bayliss (2022)
$\star$ Planet $d$ |  |
Orbital period, P (days) | 9.03052118 $\pm$0.00000753 | DREAM I
Transit epoch, T0 (BJDTDB) | 2459426.5437 $\pm$0.0028 | DREAM I
Eccentricity, e | 0.010${}^{+0.011}_{-0.007}$ | Bryant & Bayliss (2022)
Argument of periastron, $\omega$ (deg) | 16.5 ${}^{+84.2}_{-98.6}$ | Bryant & Bayliss (2022)
Stellar reflex velocity, K⋆ (m s-1) | 4.26 $\pm$0.37 | Bryant & Bayliss (2022)
Scaled separation, a/R⋆ | 16.34 ${}^{+0.08}_{-0.11}$ | Bryant & Bayliss (2022)
Orbital inclination, $i$ (deg) | 89.55 ${}^{+0.30}_{-0.27}$ | Bryant & Bayliss (2022)
Planet-to-star radius ratio, RP/R⋆ | 0.02876 $\pm$ 0.00017 | Bryant & Bayliss (2022)
Planet mass, Mpl (M⊕) | 14.2$\pm$1.3 | Bryant & Bayliss (2022)
Planetary density, $\rho_{\mathrm{pl}}$ (g cm-3) | 1.72$\pm$0.17 | Bryant & Bayliss (2022)
Projected spin-orbit angle, $\lambda$ (deg) | 0$\pm$24 | DREAM I
Planet radial-velocity semi-amplitude, Kpl(km s-1) | 103.6$\pm$1.0 | This paper22footnotemark: 2
Equilibrium temperature, Teq(K) | 919$\pm$13 | This paper44footnotemark: 4
$\star$ Planet $e$ |  |
Orbital period, P (days) | 0.7895933 $\pm$ 0.0000044 | Bryant & Bayliss (2022)
Transit epoch, T0 (BJDTDB) | 2457011.34862 $\pm$ 0.00030 | Bryant & Bayliss (2022)
Eccentricity, e | 0 (fixed) | Bryant & Bayliss (2022)
Argument of periastron, $\omega$ (deg) | 90 (fixed) | Bryant & Bayliss (2022)
Stellar reflex velocity, K⋆ (m s-1) | 4.55 $\pm$ 0.37 | Bryant & Bayliss (2022)
Table 4: continued 131313 1. For a homogeneous analysis we used quadratic limb-darkening coefficients derived using the EXOFAST calculator https://astroutils.astronomy.osu.edu/exofast/limbdark.shtml (Eastman et al. 2013) in the J-band. 2. K${}_{\mathrm{pl}}=\frac{2\pi a}{P}\frac{\sin{i}}{\sqrt{1-e^{2}}}=(\frac{2\pi G}{P})^{\frac{1}{3}}\frac{(M_{\star}+M_{\mathrm{pl}})^{\frac{1}{3}}\sin{i}}{\sqrt{1-e^{2}}}$. 3. K${}_{\mathrm{\star}}=(\frac{2\pi G}{P})^{\frac{1}{3}}\frac{M_{\mathrm{pl}}*\sin{i}}{M_{\star}^{2/3}\sqrt{1-e^{2}}}$. 4. $\rm T_{\rm{eq}}=\rm T_{\star}\left(\frac{\rm R_{\star}}{2\,a}\right)^{1/2}(1-\rm{A})^{1/4}$, where $R_{\star}$ is the stellar radius, $a$ is the semi-major axis, $A$ is the geometric albedo, we assumed an albedo of 0.2 (Crossfield & Kreidberg 2017). 5. Derived from http://www.pas.rochester.edu/~emamajek/EEM_dwarf_UBVIJHK_colors_Teff.txt Figure 9: Light curves (left panels) and the limb-darkening ratio, namely, $\frac{LD_{\mathrm{j}}}{LD_{\mathrm{mean}}}$ with $j$ the orbital phase (right panels) computed with the Python $batman$ code (Kreidberg 2015) and the system parameters from Table LABEL:Tab_parameters. The contact points t1, t2, t3, and t4 are marked with vertical black lines. Blue dots are the light curves and limb-darkening ratio at the observed phases. Table 5: Initial parameters adopted for Molecfit. Parameter | Value | Significance
---|---|---
ftol | 10-5 |  $\chi^{2}$ convergence criterion
xtol | 10-5 | Parameter convergence criterion
molecules | H2O |
ncont | 4 | Degree of coefficient for continuum fit
a0 | 1 | Constant term for continuum fit
$\omega_{\mathrm{gaussian}}$ | 2.25 | FWHM of Gaussian in pixels
kernel size | 3 | Size of Gaussian kernel in FWHM
slit width | 0.5 arcsec | Slit width
MIPAS profile | equ | Equatorial profile
Atmospheric profile | True | Fixed grid
PWV | -1 | Input for water vapor profile

Figure 10: Exposures not considered in the analysis for the presence of
outliers or low counts. Different colors correspond to different observing
nights

.

Figure 11: Difference between the full in-transit transmission spectra in
stellar rest frame when both accounting and not accounting for the RM effects
(left panel). Average full in-transit transmission spectrum in the planet rest
frame when both accounting (blue line) and not accounting (black line) for the
RM effect (right panel). Left panels also show the difference between the
transmission spectra calculated with formula 3, with the RM correction, and 4,
no RM correction applied, in the stellar rest frame. The RM signal creates a
signature that is comparable to the dispersion in a given transmission
spectrum and furthermore smoothes out when averaging over the transit because
the RM signature shifts along the transit chord radial velocities. Figure 12:
Allan plot computed on the average transmission spectra. The back lines are
the expected rms for white noise (which scales with the number of points for
bin). The red dotted curves are the standard deviation of the transmission
spectrum after various binning of different bin size. The dashed blue lines
are the best fit for the red curves (computed in log-log scale). The vertical
gray lines are the derived 1$\sigma$ uncertainty at 0.075 nm. The error bars
denote this 1$\sigma$uncertainty of the rms (Cubillos et al. 2017). Figure 13:
Correlation plots with the insolation level of mid-UV flux (200-300 Å). Same
plot as in Fig. 8. As for the XUV flux between 5 and 504 Å, the He I
absorption signal correlates with the received mid-UV flux; whereas when they
are translated in terms of mass-loss rates, these trends seem to disappear.

##  Appendix B Gaussian processes to derive upper limits on the helium
absorption

To have a better description of the correlated noise present in the data, we
performed a Differential Evolution Markov chain Monte Carlo (DEMCMC) fit of a
Gaussian profile fixing the position at 1083.326 nm and the FWHM at 0.07 nm
and varying the peak value, an offset for the continuum, an uncorrelated
jitter, and a correlated noise modeled with a Gaussian process (GP) and a
squared exponential kernel. From the posterior distribution, we were therefore
able to derive the 3$\sigma$ upper limits (the value to which 95% of the peak
distribution is subject) at the position of the helium triplet marginalized
over an uncorrelated jitter and the presence of correlated noise. The values
are reported in Table 6.

Table 6: Upper limits on the excess of absorption.

Parameter | 3$\sigma$[%]
---|---
HAT-P-3b | 1.48
HAT-P-33b | 1.17
HAT-P-49b | 0.95
HD89345b | 0.63
K2-105b | 2.57
Kepler-25c | 1.29
Kepler-68b | 0.64
WASP-47d | 3.11
141414 3$\sigma$ upper limits calculated with the Gaussian processes.