Free Access
Issue
A&A
Volume 646, February 2021
Article Number A158
Number of page(s) 17
Section Planets and planetary systems
DOI https://doi.org/10.1051/0004-6361/202039511
Published online 19 February 2021

© ESO 2021

1 Introduction

Planets orbiting late-type stars are ubiquitous, as demonstrated by the thousands of planets discovered to date1. Their numbers have been steadily increasing during the last decades thanks to multiple spectroscopic and photometric surveys carried out by, for example, the space missions Kepler (Borucki et al. 2010) and TESS (Ricker 2014), and the ground-based spectrographs HARPS (Mayor et al. 2003), HARPS-N (Cosentino et al. 2012), HIRES (Vogt et al. 1994), CARMENES (Quirrenbach et al. 2016), and MARVELS (Alam et al. 2015). Moreover, their numbers and our knowledge of exoplanets will increase thanks to new and future observing facilities like ESPRESSO (Pepe et al. 2010, 2021), CHEOPS (Rando et al. 2018), HPF (Mahadevan et al. 2014), JWST (Gardner et al. 2006), PLATO (Rauer et al. 2014), NIRPS (Wildi et al. 2017), and SPIRou (Moutou et al. 2015). Exoplanetary research is now approaching the deep study and characterisation of the atmospheres of the extrasolar planets.

It is of great interest to investigate transiting exoplanets and their atmospheres, and to learn about their bulk chemical compositions. In particular, highly irradiated gaseous planets are a key target for atmospheric characterisation due to their intrinsic properties, such as their proximity to the parent host star, their transit depth, and their transit duration. Charbonneau et al. (2002) reported the first detection of an exoplanet atmosphere by means of HST transmission spectroscopy. However, Casasayas-Barris et al. (2020, 2021) raised some doubts over that detection, showing that it could be explained by the Rossiter-McLaughlin effect. In spite of this, Charbonneau et al. (2002) opened a new era in the study of the atmospheres of transiting exoplanets. The first neutral sodium detection in HD 209458 b with a ground-based telescope was made by Snellen et al. (2008) as well as for HD 189733 b by Redfield et al. (2008). Several chemical species have already been reported in the atmospheres of tens of the so-called ultra-hot Jupiters (UHJs), which are giant gaseous planets with typical equilibrium temperatures (Teq) above ≈ 2200 K (see Parmentier et al. 2018). In particular, Fe I,II and Ti I,II have been detected in the atmosphere of the UHJ Kelt-9 b (Hoeijmakers et al. 2018, 2019). In addition, Fe I,II has also been reported in other UHJs such as MASCARA-2b (Casasayas-Barris et al. 2019), WASP-121b (Gibson et al. 2020; Hoeijmakers et al. 2020), or WASP-76b (Ehrenreich et al. 2020). Other species such as He, Na, Mg, K, V, Cr, CO, CH4, or water vapor have been reported in the atmospheres of tens of highly irradiated gaseous planets (e.g. Barman et al. 2015; Wyttenbach et al. 2015; Sheppard et al. 2017; Chen et al. 2018; Nortmann et al. 2018; Parmentier et al. 2018; Allart et al. 2018, 2019; Alonso-Floriano et al. 2019; Seidel et al. 2019; Hoeijmakers et al. 2020).

Spectroscopy is currently a powerful tool able to detect atomic or molecular features in the transmission spectra of transiting exoplanets, which in turn are key to revealing their internal chemistry and surface compositions. The Echelle Spectrograph for Rocky Exoplanets and Stable Spectroscopic Observations (ESPRESSO; see Pepe et al. 2010, 2021) is the new generation high-resolution spectrograph at the 8-m Very Large Telescope (VLT) at Paranal, Chile. ESPRESSO at the VLT offers an excellent opportunity for atmospheric characterisation, given the large collecting area of the VLT. This is a key factor for achieving the necessary signal-to-noise ratio (S/N) per resolution element during the critical and limited observational windows of transiting exoplanets. In addition, studying the atmospheres of gas giant planets at optical wavelengths is a complement to future observations at much longer wavelengths by JWST (Gardner et al. 2006) and Ariel (Tinetti et al. 2016).

In this work, we present the detailed analysis of the transmission spectrum of WASP-76b using ESPRESSO data. WASP-76b orbits an F7 V star with mV ≈ 9.5 mag with an orbital period of approximately 1.8 d (West et al. 2016; Ehrenreich et al. 2020). WASP-76b is an inflated UHJ with roughly one Jupiter mass and twice its radius with an equilibrium temperature of more than 2200 K. Thus, it is a perfect target for atmospheric characterisation given its high equilibrium temperature and the high-metallicity of the host-star (see Seidel et al. 2019; Ehrenreich et al. 2020; Edwards et al. 2020; von Essen et al. 2020). We analysed the same data as in Ehrenreich et al. (2020) with the objective of exploring which spectral atomic and molecular species can be detected using ESPRESSO.

This manuscript is organised into the following sections: the ESPRESSO observations are presented in Sect. 2; the extraction of the transmission spectrum is described in Sect. 3; while the stellar characterisation of WASP-76 can be found in Sect. 4; the transmission spectrum is analysed in Sect. 5; and finally, the summary and conclusions are given in Sect. 6.

2 Observational data

We collected several high-resolution echelle spectra covering two transits of the ultra-hot Jupiter, WASP-76b, using the HR212 mode of ESPRESSO covering the optical wavelengths from 3800 to 7880 Å with R ≈ 140 000. These two transit observations were carried out as part of the ESPRESSO Guaranteed Time Observations under ESO programme 1102.C-744. The observations were reduced using the ESPRESSO reduction pipeline3. The pipeline delivers the necessary products to further process the data; for example, barycentric corrected radial velocities (Vr), the stellar fluxes together with their uncertainties, wavelengths (in vacuum), and S/N.

The two transits of WASP-76b were observed on the following Universal Time (UT) dates: 2018 September 03 (first transit, hereafter T1) and 2018 October 31 (second transit, T2). On both occasions, we observed the target uninterruptedly for   ~2 h before, during, and ~2 h after the transit. In T1, we collected a total of 36 ESPRESSO spectra with an individual exposure time of 600 s; in T2, we acquired 70 spectra of 300 s each. The average S/N of each individual observation is about 120 per pixel element at ~ 5500 Å (see Ehrenreich et al. 2020). For T1, we performed longer exposure times than for T2 due to weather constraints (i.e. poor seeing). Unfortunately, the ESPRESSO atmospheric dispersion corrector (ADC) is not built to correct the atmospheric transmission above an airmass of 2.2. Thus, we discarded two spectra for both transits because they were not suitable for any meaningful transmission spectrum retrieval and later analysis. Other details regarding the observations for these two transits of WASP-76b can be found in Table 1, while information on the dates and radial velocities can be found in Ehrenreich et al. (2020).

Table 1

Summary of WASP-76b transit observations.

thumbnail Fig. 1

Example of our telluric correction procedure in the K I region. Top panel: uncorrected spectrum (black dashed line) and corrected spectrum (red dots). Bottom panel: best telluric correction solution for a WASP-76 spectrum in this particular wavelength range.

Table 2

Molecfit parameters used to correct telluric lines.

Table 3

Orbital and physical parameters for the WASP-76 system.

Table 4

Chemical abundances relative to solar value ([X/H]) for WASP-76.

thumbnail Fig. 2

Wiggle correction for a residual spectrum. Upper panel: original residuals (red) and the cubic splines used to fit the wiggles (black). Lower panel: final residual spectrum after removing the wiggles.

thumbnail Fig. 3

Transmission spectra for WASP-76b in the 5000–7000 Å range: a fewkey features are shaded in grey. The T1 is the top spectrum and T2 is the bottom one. A binning of 0.1 Å has been applied to the data for clarity. All wavelengths are given in vacuum.

3 Data analysis

3.1 Telluric correction

The telluric lines have to be removed from the data before extracting the transmission spectrum of WASP-76b, as they might contaminate the final result. To that aim, we gathered the processed 1D sky-corrected spectra provided by the ESPRESSO reduction pipeline. We corrected for the telluric absorption lines by means of the Molecfit4 software suite (Smette et al. 2015; Kausch et al. 2015). The spectra provided by the ESPRESSO pipeline were already corrected for the Barycentric Earth Radial Velocity (BERV). However, Molecfit models the telluric transmission spectrum in the terrestial reference frame (see, e.g. Allart et al. 2017). Consequently, we shifted the spectra to the terrestial reference frame before performing any telluric line fitting with Molecfit.

In order to perform the telluric line fitting, we selected three telluric regions in the spectra to fit the H2 O and O2 molecular bands (i.e. 6860–6900 Å, 7160–7340 Å, and 7590–7770 Å). Then, we used the parameters given in Table 2 and ran Molecfit to fit the telluric lines (see Fig. 1). We refined the Molecfit result until the input and output parameters were the same down to the arithmetic precision achievable using the Molecfit graphical user interface. Finally, we gathered the telluric corrected ESPRESSO data and removed the Earth motion using the BERVs already calculated by the ESPRESSO pipeline.

Table 5

Properties of atomic lines studied in this work.

3.2 Transmission spectrum extraction

The telluric line correction has provided us with a set of clean spectra that we can use to extract the planetary signature. Using these corrected ESPRESSO spectra, we extracted the planet signal by means of a procedure based on the technique described by Wyttenbach et al. (2015). Firstly, we shifted the spectra to the stellar reference frame using the radial velocities (RVs) calculated with the ephemeris and Keplerian stellar motion given by Ehrenreich et al. (2020). Secondly, each individual spectrum was flux-scaled by means of a second order polynomial (see Tabernero et al. 2020) to the lowest air-mass spectrum in its corresponding transit observations. The flux level changes from exposure to exposure, due to variations in airmass and atmospherictransparency, and in consequence each individual spectrum must be corrected for these effects. In other words, thanks to this scaling polynomial, the ESPRESSO observations of each transit have been effectively ‘re-normalized’ to the same continuum level.

At this point, we had generated a set of aligned observations (in terms of wavelength and flux) that we used to extract the planetary signature. We organised the observed spectra into two categories: in-transit and out-of-transit, according to the ephemeris given by Ehrenreich et al. (2020). Then, we computed, wavelength by wavelength, the median of the out-of-transit spectra to generate a master stellar spectrum. After this, we divided each individual spectrum by this master spectrum in order to remove the stellar flux from the data. The result of this process is a set of spectra that contain the planetary signal plus the noise left after the removal of the stellar contribution.

After removing the stellar contribution from the data, we found a sinusoidal pattern (wiggles hereafter) on each of the resulting residual spectra. These wiggles are likely caused by an interference pattern induced by the coude train optics. Qualitatively speaking, they have an amplitude of 1% with a period of ~ 30 Å at ~ 6300 Å and might affect the final transmission signature, and in consequence they had to be removed from the transit data. For that reason, we removed them by fitting a set of cubic splines to the residual spectra using the iSpec code (Blanco-Cuaresma et al. 2014, see Fig. 2). Then, the in-transit residual spectra were shifted in velocity to bring them to the planetary rest frame using the ephemeris given by Ehrenreich et al. (2020). These shifted residual spectra were later merged, wavelength by wavelength, into a single median transmission spectrum. This procedure was applied independently to T1 and T2 to produce two independent transmission spectra for WASP-76b.

4 Stellar characterisation

4.1 Stellar atmospheric parameters

The stellar atmospheric parameters of WASP-76, namely effective temperature (Teff), surface gravity (logg), microturbulence (ξ), and metallicity ([Fe/H]), were calculated using the equivalent width (EW) method by means of the stepar5 code (Tabernero et al. 2019). The atmospheric parameters were computed using the master spectrum from Sect. 3.2. The latest state-of-the-art version of stepar relies on the 2017 version of the MOOG code (via the abfind driver, see Sneden 1973) and a grid of MARCS stellar atmospheric models (Gustafsson et al. 2008). We employed the selection of Fe I and Fe II following the line list given by Tabernero et al. (2019) for metal-rich dwarf stars. The available atomic data of these iron lines were taken from the public version of the Gaia-ESO line list (Heiter et al. 2015). As a damping prescription, we used the Anstee-Barcklem-O’Mara (ABO, see Barklem et al. 1998) data (if available), through option 1 of MOOG. In addition, we used ARES6 (Sousa et al. 2015) to measure the EWs of the Fe I,II lines used.

We only considered measured lines with 10 mÅ < EW < 120 mÅ to avoid problems with line profiles of very intense lines and tentatively incorrect EW measurements of extremely weak lines. The atmospheric parameters can then be inferred from the previously measured Fe I –Fe II line list. The minimisation procedure of stepar is the downhill simplex algorithm (Press et al. 2002), which tries to minimise a quadratic form composed of the excitation and ionisation equilibrium conditions to find the best parameters of the target star. stepar iterates until logϵ(Fe I) and logϵ(Fe II) stand for the Fe abundance returned by the Fe I and Fe II lines, respectively, and log (EW∕λ) is their reduced equivalent width. stepar iterates until the slopes of χ vs. logϵ(Fe I) and log(EW∕λ) vs. logϵ(Fe I) are virtually zero, meaning excitation equilibrium is reached, and it imposes ionisation equilibrium, so that log ϵ(Fe I) = logϵ(Fe II). The stellar parameters derived for WASP-76 in this work can be found in Table 3.

We also calculated stellar atmospheric parameters using the ARES+MOOG code (Sousa et al. 2008, 2018; Santos et al. 2013) and obtained similar results to those provided by stepar: Teff = 6329 ± 24 K, log g = 4.20 ± 0.03 dex, ξ = 1.54 ± 0.03 km s−1, and [Fe/H]=0.37 ± 0.02 dex. Both ARES+MOOG and stepar are similar implementations of the EW method. ARES+MOOG employs a similar workflow to that of stepar, however it implements KURUCZ stellar atmospheric models (Kurucz 1993), a solar calibrated log gf Fe I,II line list (Sousa et al. 2008), and the damping option 0 of MOOG. In addition, West et al. (2016) calculated the following stellar parameters of Teff = 6250 ± 100 K, log g = 4.13 ± 0.02 dex, ξ = 1.4 ± 0.1 km s−1, and [Fe/H] = 0.23 ± 0.1 dex. These values are in good agreement with those calculated with STEPAR and ARES+MOOG codes.

thumbnail Fig. 4

Synthetic models generated with turbospectrum for the different atomic and molecular species. All of them have been degraded to R = 140 000.

4.2 Stellar mass and radius

We used the PARAM web interface7 (da Silva et al. 2006) to calculate the mass (M*) and radius (R*) of WASP-76. We used the stellar parameters calculated with STEPAR along with the Gaia DR2 Parallax (Gaia Collaboration 2018), the visual magnitude (mV) given by West et al. (2016), and the PARSEC stellar evolutionary tracks and isochrones (Bressan et al. 2012) to obtain a mass of 1.45 ± 0.02 M and a radius of 1.77 ± 0.07 R. Our values are consistent with those provided by Ehrenreich et al. (2020), who reported M* = 1.46 ± 0.02 M and R* = 1.76 ± 0.07 R. On the other hand, West et al. (2016) obtained 1.46 ± 0.07 M and 1.73 ± 0.04 R, which are both consistent with the values derived in this work.

4.3 Chemical abundances

We calculated the chemical abundances of WASP-76 (see Table 4) by means of the EW method for the following atomic species: Li I C I, O I, Na I, Mg I, Si I, Ca I, Ti I, Cr I, Mn I, and Ni I. Moreover, the atomic data for each atomic species under analysis were collected from the Gaia-ESO (GES) line list (Heiter et al. 2015). We measured an EW = 29.2 mÅ for the Li I line at 6709.61 Å by means of a Gaussian fit performed using the Levenberg–Marquardt algorithm (LMA) implemented in the python library SciPy (Virtanen et al. 2020). The EWs of the other elements were measured by means of the ARES code. Then, we interpolated a model atmosphere from the MARCS stellar atmospheric grid (Gustafsson et al. 2008) and we used the MOOG code (Sneden 1973) to derive the abundances for WASP-76. The O I abundance was later corrected for NLTE effects thanks to the corrections given by Sitnova et al. (2013)8. Then, we used C I and O I to derive a carbon-to-oxygen ratio (C/O) of 0.51 ± 0.03, which in turn is consistent with the solar value. Finally, the atomic parameters employed to calculate the abundances can be found in Table A.1.

thumbnail Fig. 5

Na Double tomography for transit 1 (left) and transit 2 (right). Time 0.0 h corresponds to the mid-transit according to the planetary ephemeris given in Table 3. Each tile represents a 0.1 Å bin. These plots are qualitative and have been generated only for visualisation purposes.

thumbnail Fig. 6

Transmission spectra around the Na I doublet: top (T1), bottom (T2). The grey line represents the original transmissionspectrum of WASP-76b, whereas the black dots represent a binning of 0.1 Å. The red line represents the best fit to the data. The rest-frame wavelength of each individual line is represented by a dashed green vertical line.

thumbnail Fig. 7

Center to limb variation (CLV) and Rossiter-McLaughlin (RM) correction around the Na I lines.

thumbnail Fig. 8

Same as Fig. 6 but for ii H and K lines.

5 Transmission spectrum analysis

The procedure employed in this work delivers the entire ESPRESSO spectrum of WASP-76b simultaneously. This is possible via the stable and consistent wavelength calibration of the ESPRESSO data. The extracted transmission spectra for T1 and T2 are depicted in Fig. 3. Using these two spectra, we can identify tentative spectral features by visual inspection and perform a cross-correlation using a binary mask built for a given atomic or molecular species. We explored the presence of the following atomic species by direct inspection of the transmission spectrum: H I, Li I, Na I, Mg I, Ca II, Mn I, Fe I, and K I. In addition, we explored the presence of other species via a cross-correlation function (CCF) analysis: Ti I, Cr I, Fe I, and Ni I in addition to the diatomic molecules TiO, VO, and ZrO.

Each tentative atomic feature directly seen in the transmission spectrum of WASP-76b has to be scrutinised for its significance. Thus, we modelled each individual feature with a Gaussian profile plus the continuum level. The model fitting is then performed by means of the LMA implemented in the python library SciPy (Virtanen et al. 2020). The LMA explores the parameter space that provides us with valuable information about each spectroscopy feature in consideration. In summary, our modelling provides the Gaussian parameters of the line under analysis (i.e. centre, amplitude, depth, and width) alongside their uncertainties. We also calculated the Doppler shift (Vwind) of each line to explore any tentative planetary winds (see, e.g. Hoeijmakers et al. 2018; Casasayas-Barris et al. 2019). Moreover, we derived effective planetary radii at the centre of each feature (Rλ) in units of the radius of theplanet (Rp) at the centre of each individual atomic feature. Thus, we used the following expression: Rλ = Rp, where h is the line depth of a given absorption feature in the transmission spectrum, and δ is the transit depth of the planet. All these results can be found in Table 5. Regarding these lines, we produced a series of tomography plots (see Figs. 5, and A.2A.5) in order to explore the passage of the planet with time for each transit, which allows us to confirm the presence of the planetary signal. However, some lines are perhaps too weak to be directly seen in the tomography plots.

In addition, the detection of some atomic or molecular species in the atmosphere of the planet can be achieved by cross-correlating the planetary signal with a synthetic spectrum. To that particular aim, we calculated an atmospheric structure of WASP-76b by means of the HELIOS code9 (Malik et al. 2017, 2019). In order to generate a model spectrum, we need a radiative transfer code to input the planet atmospheric structure calculated by HELIOS. The aforementioned atmospheric structure consists of a set of atmospheric layers containing the gaseous pressure (Pg), temperature (T), and geometrical depth. These quantities are necessary to solve the radiative transfer equation in order to produce a synthetic model spectrum. We employed turbospectrum10 to solve the radiative transfer problem (Plez 2012) alongside seven different line lists comprised of the following atomic and molecular species: Cr I, Ti I, Fe I, Ni I, TiO, VO, and ZrO. The atomic data were downloaded using the VALD3 interface11 (Ryabchikova et al. 2015), while the molecular data for TiO, VO, and ZrO were gathered from Plez (1998, 2003), and McKemmish et al. (2016), respectively. Furthermore, turbospectrum is a general purpose radiative transfer tool that can generate high-resolution synthetic spectra by solving the problem of radiative transfer in spherical geometry. The resulting synthetic spectra (see Fig. 4) were later converted into binary line masks that we used to perform a cross-correlation against our two transmission spectra. To perform the cross-correlation, we employed the iSpec (Blanco-Cuaresma et al. 2014) code, which in turn implements the algorithm described in Pepe et al. (2002) to calculate the CCF. The goal of the cross-correlation technique is to combine thousands of spectral features to produce an imprint that we will be able detect.

thumbnail Fig. 9

Same as Fig. 6 but for the Hα line.

thumbnail Fig. 10

Same as Fig. 6 but for the Li I line.

5.1 Na I

Firstly, we verified that our procedure was extracting the signals for lines already reported in the literature, such as the Na I doublet. The detection of the Na I Doublet in WASP-76b has already been reported by Seidel et al. (2019) and Edwards et al. (2020). We clearly detected both lines in the 2D-map moving with the planet velocity (see Fig. 5). Our final spectrum of the Na I doublet is shown in Fig. 6. Our detection is 9.2 and 7.5 σ levels for T1, and 6.5 and 7 σ for T2. These detections are more significant than those obtained by Seidel et al. (2019) using HARPS data. In addition, we verified the effect of the Rossiter-McLaughlin effect on our extraction by means of the modelling described in Casasayas-Barris et al. (2019) using the ephemeris given by Ehrenreich et al. (2020). We find that the effect is not significant (0.04%) given our error bars for the two transits of WASP-76 b (see Fig. 7).

5.2 Ca II

The Ca II H (3969.59 Å) and Ca II K (3934.78 Å) lines are visible in the transmission spectrum (see Fig. 8), our Gaussian fits shows that they are significant at the level of 8.5–7.1 σ for T1 and 5.6–8.4 σ for T2. These two lines are the most prominent features of the transmission spectrum of WASP-76, being one order of magnitude deeper than the other atomic features (~2–3%). In fact, they might be originated by photo-ionisation in the upper part of the highly irradiated planetary exosphere (Yan et al. 2019). Moreover, their intrinsic depth points towards a high formation altitude (see Table 5) as our calculated Rλ is 1.78–1.57 Rp (T1) and 1.82–1.80 Rp (T2).

5.3 Balmer lines

The Hα line at 6564.61 Å is present in T1, whereas T2 shows a little bump rather than a proper line (see Fig. 9). Thus, it is not possible to confirm the presence of atomic hydrogen in the atmosphere using only T2, and for T1 we find a depth at 0.48 ± 0.12%. We also explored other Balmer lines (Hβ, Hγ), but none of them are visible in the planetary transmission spectrum. The T2 data have a higher quality than T1, which in turn implies that if Hα absorption were present in T2 with the same intensity as in T1, we should have detected it. Interestingly enough, no study to the present date has provided an upper limit to the presence of Hα in the spectrum of WASP-76b. von Essen et al. (2020) explored the transmission of spectrum of WASP-76b using HST optical spectra and did not find any evidence of strong absorption due to Hα. In contrast, our findings at a higher resolution might be evidence for Hα variability from the planetary atmosphere, given that the line is not seen in T2. However, the available data are not sufficient to confirm this scenario.

thumbnail Fig. 11

Same as Fig. 6 but for other elements. Here, we represent the combination of several lines in velocity space to strengthen the signal. Black points represent a binning of 10 km s−1 and the green dashed line represents the 0 km s−1 mark.

5.4 Li I

The planet WASP-76b is expected to have Li in its atmosphere because sub-stellar objects with masses below ≈55 MJup do not deplete this element at any moment during their evolution (Chabrier et al. 2000; Baraffe et al. 2015). Consequently, WASP-76b should maintain the amount of lithium in its atmosphere that was present in the protoplanetary disc from which it was supposedly formed. In fact, the presence of Li was modelled by Chen et al. (2018) for another giant planet. The Li I absorption feature at 6709.61 Å is detected in the ESPRESSO spectra of the planet WASP-76b (see Fig. 10). We found that Li I is moving with the planet velocity in T2, whereas we find a small trace during T1 (see Fig. A.5). The Li I line is more significant for T2 than T1 (5.7σ vs. 4.4σ) and it can be easily explained by the cadence of the observations of T2 in contrast with T1. To the best of our knowledge, this is the first reported detection of Lithium using high-resolution spectroscopy (in Borsa et al. 2021, there is also a report on the detection of lithium in another giant planet using ESPRESSO data).

5.5 Fe I

Interestingly enough, the accuracy of the ESPRESSO data has allowed us to detect actual Fe I lines in the transmission spectra of WASP-76 b. In particular, our two transit spectra show a few tentative neutral iron lines in the range of ≈ 4377–4430 Å. The following lines are seen in the spectrum of WASP-76b: 4377.16, 4384.78, 4406.26, 4416.17, and 4428.55 Å. To increase the S/N of the profile of the Fe lines, we merged all of them into a single line in velocity space (see Fig. 11). The combination of these lines is stronger for T2 (6.7σ) than for T1 (4.7 σ). The detection of these lines is in agreement with the results of the CCF against an Fe I binary mask (see Fig. 13). In this work, we found a depth of 255 ± 20 ppm (T1) and 182 ± 12 ppm (T2). These two values correspond to central velocities of −8.27 ± 0.25 km s−1 and −8.76 ± 0.56 km s−1, respectively, and to a detection at 12.8 σ and the 18.2 σ levels for T1 and T2, respectively (see Table 6). Overall, our results reinforce those presented by Ehrenreich et al. (2020) for WASP-76b.

thumbnail Fig. 12

Same as Fig. 11 but for Mg I and K I.

5.6 Mg I, K I, and Mn I

The Mg I line at 4572.38 Å seems to be in the transmission spectrum with a significance of ~2.8 σ for T1 and 3.7 σ for T2 (see Fig. 12). In addition to this Mg I, we find that the space-velocity combined magnesium triplet lines (5168.76, 5174.13, and 5185.05 Å) are clearly seen at the 7.5 σ level for T2 (see Fig. 11). Regarding K I, the transmission maps show a small imprint leaving a partial tracethat we can barely see in our maps (see Fig. A.3). However, the line is present at the 5.7 σ level in T2. We also report the detection of the Mn I triplet lines at 4031.89, 4034.20, and 4035.62 Å. Again, we combined these three lines in velocity space and we find that their significance is in the range of 4.7-5.0 σ.

Table 6

Cross-correlation data for analysed atomic and molecular species.

5.7 Ti I, Cr I, and Ni I

We also explored the presence of Ti I, Cr I, and Ni I in our data using a cross-correlation method. For the most part, our search was unsuccessful and we could only calculate upper limits to their presence of ~60 ppm (Ti I), ~77 ppm (Cr I), and ~130 ppm (Ni I). Finally, the data regarding these elements and their respective CCFs can be found in Table 6 and in Fig. 13.

thumbnail Fig. 13

Cross-correlation function against our line masks for Ti I, Cr I, Fe I, and Ni I. The black line represents CCF, whereas the red line represents the best fit to the Fe I CCF.

5.8 Diatomic molecules: TiO, VO, and ZrO

These three diatomic molecules are important absorbers in cool stellar atmospheres with a non-negligible opacity source as already shown by Van Eck et al. (2017). The CCFs calculated in this work do not show any trace of them in our data, as shown in Fig. A.1. However, we can place a conservative upper limit on their presence of ≤ 10 ppm (see Table 6). Interestingly, the TiO atomic data were recently improved by the exomol team (see McKemmish et al. 2019). As a result, we generated a new TiO model spectrum using the exomol line list (McKemmish et al. 2019) and performed a CCF against it. We found an upper limit to the presence of TiO of 6 ppm, which in turn is in agreement with what we obtained with the Plez (1998) line list (see Table 6). In all, these state-of-the-art line lists might be not accurate enough to confidently retrieve the presence of TiO in the atmosphere of WASP-76b. Despite this, we can think of two scenarios for this particular planet: these features are either really weak or they are hidden due to other effects. However, clouds might be responsible for the weakening of the molecular bands and in the planetary spectrum (Charnay et al. 2015). In addition, another explanation might stem from the transport of molecules to the night side of the planet (Nugroho et al. 2017). In all, TiO and VO should dominate the spectrum in the optical wavelength range (Van Eck et al. 2017), given the equilibrium temperature of the planet. Other studies have explored the atmosphere of WASP-76b using HST data (Edwards et al. 2020; von Essen et al. 2020) and modelled the atmosphere of the planet. In fact, the transmission spectrum and the modelling of von Essen et al. (2020) did not present any evidence for TiO absorption in the optical wavelength range. This is consistent with our non-detections using ESPRESSO data. Interestingly, the nearly solar C/O calculated here for WASP-76 does not suggest that the atmosphere of the WASP-76b is driven by a carbon-rich chemistry. However, clouds might be responsible for the weakening of the molecular bands and in the planetary spectrum (Charnay et al. 2015). In addition, another explanation might stem from the transport of molecules to the night side of the planet (Nugroho et al. 2017).

thumbnail Fig. 14

FWHM and Vwind for the lines studied in this work vs. Rλ.

6 Summary and conclusions

We analysed two transits of the UHJ WASP-76b using ESPRESSO at the VLT. In this work, we generated two independenttransmission spectra covering the available wavelength range. Using these spectra, we have been able to detect features that were not reported in previous studies: Li I, Mg I, K I, Ca II, and Mn I. In addition, our work strengthens the previous detections of Na I (Seidel et al. 2019; von Essen et al. 2020) and Fe I (Ehrenreich et al. 2020).

We find that most lines are blueshifted with respect to their rest-frame wavelengths (see Fig. 14). The median is −5.6 ± 4.2 km s−1 for T1, and −5.2 ± 3.1 km s−1 for T2. These shifts are probably due to planetary winds and are frequently reported in the literature (see, e.g. Casasayas-Barris et al. 2019; Hoeijmakers et al. 2019; Gibson et al. 2020; Ehrenreich et al. 2020).

Interestingly, our calculations indicate that the Ca II lines are formed in higher layers than the other lines (~1.6−1.8 Rp, see Table 5), which points towards an extended exosphere. This has already been reported for other planets such as MASCARA-2b (Casasayas-Barris et al. 2019), WASP-33b (Yan et al. 2019), and WASP-121b (Borsa et al. 2021). In addition, their intrinsic widths are in all instances higher than ~20 km s−1 (see Fig. 14). In addition, the Hα line seen in T1 is much narrower compared to the other atomic features. Its width is ~ 6 km s−1, which is greater than the resolving power of our data (R = 140 000; FWHM = 2.1 km s−1). In all, the measured FWHM for this line is in principle physically possible, and its absence in T2 might be indicative of atmosphericvariability. However, the current data are not sufficient to fully explore this scenario.

In addition, our data show that the K I absorption line is weaker than the Li I feature, a fact that is unexpected for a near-solar composition where K is much more abundant than Li (both atoms have very similar electronic structures). The observationssuggest that Li is at least as abundant as K in the investigated planetary atmospheric layers. This requires scenarios that have not been explored in exoplanetary atmospheres so far; for example, lithium production in situ or a strongly inhomogeneous distribution of the chemical abundances within the atmosphere.

Regarding the molecular species in WASP-76b, we could not find any signs of TiO, VO, or ZrO. They are either not present in the atmosphere of WASP-76b or their intensity is well below the minimum noise level in these observations. Finally, we demonstrate that we are able to reduce the noise of the exoplanetary data using ESPRESSO to the extent that we can detect many planetary atomic features by means of a single transmission spectrum.

Acknowledgements

This work was supported by FCT – Fundação para a Ciência e a Tecnologia through national funds and by FEDER through COMPETE2020 – Programa Operacional Competitividade e Internacionalização by these grants: UID/FIS/04434/2019; UIDB/04434/2020; UIDP/04434/2020; PTDC/FIS-AST/ 32113/2017 & POCI-01-0145-FEDER-032113; PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953; PTDC/FIS-AST/28987/2017 & POCI-01- 0145-FEDER-028987. V.A., S.G.S., S.C.C.B. acknowledge support from FCT through Investigador FCT contracts ns° IF/00650/2015/CP1273/CT0001; IF/00028/2014/CP1215/CT0002; IF/01312/2014/CP1215/CT0004. S.G.S acknowledges the support from FCT through Investigador FCT contract nr. CEECIND/00826/2018 and POPH/FSE (EC). J.P.F., O.D., and J.H.C.M. acknowledge support from FCT through national funds in the form of a work contract with the references DL 57/2016/CP1364/CT0005; DL 57/2016/CP1364/CT0004; DL 57/2016/CP1364/CT0007. H.M.T. and M.R.Z.O. acknowledge financial support from the Spanish Ministerio de Ciencia, Innovación y Universidades (MICIU) through project AYA2016-79425-C3-2. A.S.M., R.R., J.I.G.H., C.A.P. acknowledge financial support from the Spanish MICIU project AYA2017-86389-P. J.I.G.H. acknowledges financial support from the Spanish MICIU under the 2013 Ramón y Cajal program RYC-2013-14875. This work has been carried out within the framework of the National Centre of Competence in Research PlanetS supported by the Swiss National Science Foundation. The authors acknowledge the financial support of the SNSF. The INAF authors acknowledge financial support of the Italian Ministry of Education, University, and Research with PRIN 201278X4FL and the “Progetti Premiali” funding scheme. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project FOUR ACES; grant agreement No 724427). N.J.N. acknowledges support from FCT through Investigador FCT contract and exploratory project IF/00852/2015, and project PTDC/FIS-OUT/29048/2017. J.V.S. acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project Four Aces; grant agreement No. 724427).

Appendix A Additional material

thumbnail Fig. A.1

Same as Fig. 13 for diatomic molecules. From top to bottom: TiO, VO, and ZrO.

thumbnail Fig. A.2

Same as Fig. 5 but for Ca II HK lines.

thumbnail Fig. A.3

Same as Fig. 5 but for K I.

thumbnail Fig. A.4

Same as Fig. 5 but for Li I.

thumbnail Fig. A.5

Same as Fig. 5 but for Hα.

Table A.1

Atomic data employed to calculate chemical abundances of WASP-76.

References

  1. Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12 [NASA ADS] [CrossRef] [Google Scholar]
  2. Allart, R., Lovis, C., Pino, L., et al. 2017, A&A, 606, A144 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Allart, R., Bourrier, V., Lovis, C., et al. 2018, Science, 362, 1384 [Google Scholar]
  4. Allart, R., Bourrier, V., Lovis, C., et al. 2019, A&A, 623, A58 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Alonso-Floriano, F. J., Snellen, I. A. G., Czesla, S., et al. 2019, A&A, 629, A110 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Baraffe, I., Homeier, D., Allard, F., & Chabrier, G. 2015, A&A, 577, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Barklem, P. S., Anstee, S. D., & O’Mara, B. J. 1998, PASA, 15, 336 [Google Scholar]
  8. Barman, T. S., Konopacky, Q. M., Macintosh, B., & Marois, C. 2015, ApJ, 804, 61 [NASA ADS] [CrossRef] [Google Scholar]
  9. Blanco-Cuaresma, S., Soubiran, C., Heiter, U., & Jofré, P. 2014, A&A, 569, A111 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Borsa, F., Allart, R., Casasayas-Barris, N., et al. 2021, A&A, 645, A24 [EDP Sciences] [Google Scholar]
  11. Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977 [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  12. Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127 [Google Scholar]
  13. Casasayas-Barris, N., Pallé, E., Yan, F., et al. 2019, A&A, 628, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  14. Casasayas-Barris, N., Pallé, E., Yan, F., et al. 2020, A&A, 635, A206 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Casasayas-Barris, N., Pallé, E., Stranget, M., et al. 2021, A&A, in press, https://doi.org/10.1051/0004-6361/202039539 [Google Scholar]
  16. Chabrier, G., Baraffe, I., Allard, F., & Hauschildt, P. 2000, ApJ, 542, 464 [Google Scholar]
  17. Charbonneau, D., Brown, T. M., Noyes, R. W., & Gilliland, R. L. 2002, ApJ, 568, 377 [Google Scholar]
  18. Charnay, B., Meadows, V., & Leconte, J. 2015, ApJ, 813, 15 [Google Scholar]
  19. Chen, G., Pallé, E., Welbanks, L., et al. 2018, A&A, 616, A145 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  20. Cosentino, R., Lovis, C., Pepe, F., et al. 2012, SPIE Conf. Ser., 8446, 84461V [Google Scholar]
  21. da Silva, L., Girardi, L., Pasquini, L., et al. 2006, A&A, 458, 609 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  22. Edwards, B., Changeat, Q., Baeyens, R., et al. 2020, AJ, 160, 8 [Google Scholar]
  23. Ehrenreich, D., Lovis, C., Allart, R., et al. 2020, Nature, 580, 597 [Google Scholar]
  24. Gaia Collaboration (Brown, A. G. A., et al.) 2018, A&A, 616, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Gardner, J. P., Mather, J. C., Clampin, M., et al. 2006, Space Sci. Rev., 123, 485 [Google Scholar]
  26. Gibson, N. P., Merritt, S., Nugroho, S. K., et al. 2020, MNRAS, 493, 2215 [Google Scholar]
  27. Gustafsson, B., Edvardsson, B., Eriksson, K., et al. 2008, A&A, 486, 951 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Heiter, U., Lind, K., Asplund, M., et al. 2015, Phys. Scr., 90, 054010 [Google Scholar]
  29. Hoeijmakers, H. J., Ehrenreich, D., Heng, K., et al. 2018, Nature, 560, 453 [NASA ADS] [CrossRef] [Google Scholar]
  30. Hoeijmakers, H. J., Ehrenreich, D., Kitzmann, D., et al. 2019, A&A, 627, A165 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Hoeijmakers, H. J., Seidel, J. V., Pino, L., et al. 2020, A&A, 641, A123 [CrossRef] [Google Scholar]
  32. Kausch, W., Noll, S., Smette, A., et al. 2015, A&A, 576, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Kurucz, R. 1993, ATLAS9 Stellar Atmosphere Programs and 2 km/s grid. Kurucz CD-ROM No. 13. Cambridge, 13 [Google Scholar]
  34. Mahadevan, S., Ramsey, L. W., Terrien, R., et al. 2014, SPIE, 543, 552 [Google Scholar]
  35. Malik, M., Grosheintz, L., Mendonça, J. M., et al. 2017, AJ, 153, 56 [Google Scholar]
  36. Malik, M., Kitzmann, D., Mendonça, J. M., et al. 2019, AJ, 157, 170 [Google Scholar]
  37. Mayor, M., Pepe, F., Queloz, D., et al. 2003, The Messenger, 114, 20 [NASA ADS] [Google Scholar]
  38. McKemmish, L. K., Yurchenko, S. N., & Tennyson, J. 2016, MNRAS, 463, 771 [NASA ADS] [CrossRef] [Google Scholar]
  39. McKemmish, L. K., Masseron, T., Hoeijmakers, H. J., et al. 2019, MNRAS, 488, 2836 [Google Scholar]
  40. Moutou, C., Boisse, I., Hébrard, G., et al. 2015, SF2A-2015: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics, 205 [Google Scholar]
  41. Nortmann, L., Pallé, E., Salz, M., et al. 2018, Science, 362, 1388 [Google Scholar]
  42. Nugroho, S. K., Kawahara, H., Masuda, K., et al. 2017, AJ, 154, 221 [Google Scholar]
  43. Parmentier, V., Line, M. R., Bean, J. L., et al. 2018, A&A, 617, A110 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  44. Pepe, F., Mayor, M., Galland, F., et al. 2002, A&A, 388, 632 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Pepe, F. A., Cristiani, S., Rebolo Lopez, R., et al. 2010, Proc. SPIE, 7735, 77350F [CrossRef] [Google Scholar]
  46. Pepe, F., Cristiani, S., Rebolo, R., et al. 2021, A&A, 645, A96 [CrossRef] [EDP Sciences] [Google Scholar]
  47. Plez, B. 1998, A&A, 337, 495 [NASA ADS] [Google Scholar]
  48. Plez, B. 2003, ASP Conf. Ser., 298, 189 [Google Scholar]
  49. Plez, B. 2012, Astrophysics Source Code Library [record ascl:1205.004] [Google Scholar]
  50. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 2002, Numerical recipes in C++ : the art of scientific computing (Cambridge: Cambridge University Press) [Google Scholar]
  51. Quirrenbach, A., Amado, P. J., Caballero, J. A., et al. 2016, Proc. SPIE, 9908, 990812 [Google Scholar]
  52. Rando, N., Asquier, J., Corral Van Damme, C., et al. 2018, SPIE Conf. Ser., 10698, 106980K [Google Scholar]
  53. Rauer, H., Catala, C., Aerts, C., et al. 2014, Exp. Astron., 38, 249 [Google Scholar]
  54. Redfield, S., Endl, M., Cochran, W. D., & Koesterke, L. 2008, ApJ, 673, L87 [Google Scholar]
  55. Ricker, G. R. 2014, JAAVSO, 42, 234 [NASA ADS] [Google Scholar]
  56. Ryabchikova, T., Piskunov, N., Kurucz, R. L., et al. 2015, Phys. Scr., 90, 054005 [Google Scholar]
  57. Santos, N. C., Sousa, S. G., Mortier, A., et al. 2013, A&A, 556, A150 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Seidel, J. V., Ehrenreich, D., Wyttenbach, A., et al. 2019, A&A, 623, A166 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Sheppard, K. B., Mandell, A. M., Tamburo, P., et al. 2017, ApJ, 850, L32 [Google Scholar]
  60. Sitnova, T. M., Mashonkina, L. I., & Ryabchikova, T. A. 2013, Astron. Lett., 39, 126 [Google Scholar]
  61. Smette, A., Sana, H., Noll, S., et al. 2015, A&A, 576, A77 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Sneden, C. 1973, ApJ, 184, 839 [Google Scholar]
  63. Snellen, I. A. G., Albrecht, S., de Mooij, E. J. W., & Le Poole, R. S. 2008, A&A, 487, 357 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Sousa, S. G., Santos, N. C., Mayor, M., et al. 2008, A&A, 487, 373 [NASA ADS] [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  65. Sousa, S. G., Santos, N. C., Adibekyan, V., Delgado-Mena, E., & Israelian, G. 2015, A&A, 577, A67 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Sousa, S. G., Adibekyan, V., Delgado-Mena, E., et al. 2018, A&A, 620, A58 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  67. Tabernero, H. M., Marfil, E., Montes, D., & González Hernández, J. I. 2019, A&A, 628, A131 [CrossRef] [EDP Sciences] [Google Scholar]
  68. Tabernero, H. M., Allende Prieto, C., Zapatero Osorio, M. R., et al. 2020, MNRAS, 498, 4222 [Google Scholar]
  69. Tinetti, G., Drossart, P., Eccleston, P., et al. 2016, SPIE, 658 667 [Google Scholar]
  70. Van Eck, S., Neyskens, P., Jorissen, A., et al. 2017, A&A, 601, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  71. Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nat. Methods, 17, 261 [Google Scholar]
  72. Vogt, S. S., Allen, S. L., Bigelow, B. C., et al. 1994, SPIE Conf. Ser., 2198, 362 [NASA ADS] [Google Scholar]
  73. von Essen, C., Mallonn, M., Hermansen, S., et al. 2020, A&A, 637, A76 [CrossRef] [EDP Sciences] [Google Scholar]
  74. West, R. G., Hellier, C., Almenara, J.-M., et al. 2016, A&A, 585, A126 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  75. Wildi, F., Blind, N., Reshetov, V., et al. 2017, SPIE, 321, 335 [Google Scholar]
  76. Wyttenbach, A., Ehrenreich, D., Lovis, C., Udry, S., & Pepe, F. 2015, A&A, 577, A62 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Yan, F., Casasayas-Barris, N., Molaverdikhani, K., et al. 2019, A&A, 632, A69 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]

2

HR21 uses a binning of a factor of 2 in the direction perpendicular to wavelength.

8

Using the web interface at http://nlte.mpia.de

All Tables

Table 1

Summary of WASP-76b transit observations.

Table 2

Molecfit parameters used to correct telluric lines.

Table 3

Orbital and physical parameters for the WASP-76 system.

Table 4

Chemical abundances relative to solar value ([X/H]) for WASP-76.

Table 5

Properties of atomic lines studied in this work.

Table 6

Cross-correlation data for analysed atomic and molecular species.

Table A.1

Atomic data employed to calculate chemical abundances of WASP-76.

All Figures

thumbnail Fig. 1

Example of our telluric correction procedure in the K I region. Top panel: uncorrected spectrum (black dashed line) and corrected spectrum (red dots). Bottom panel: best telluric correction solution for a WASP-76 spectrum in this particular wavelength range.

In the text
thumbnail Fig. 2

Wiggle correction for a residual spectrum. Upper panel: original residuals (red) and the cubic splines used to fit the wiggles (black). Lower panel: final residual spectrum after removing the wiggles.

In the text
thumbnail Fig. 3

Transmission spectra for WASP-76b in the 5000–7000 Å range: a fewkey features are shaded in grey. The T1 is the top spectrum and T2 is the bottom one. A binning of 0.1 Å has been applied to the data for clarity. All wavelengths are given in vacuum.

In the text
thumbnail Fig. 4

Synthetic models generated with turbospectrum for the different atomic and molecular species. All of them have been degraded to R = 140 000.

In the text
thumbnail Fig. 5

Na Double tomography for transit 1 (left) and transit 2 (right). Time 0.0 h corresponds to the mid-transit according to the planetary ephemeris given in Table 3. Each tile represents a 0.1 Å bin. These plots are qualitative and have been generated only for visualisation purposes.

In the text
thumbnail Fig. 6

Transmission spectra around the Na I doublet: top (T1), bottom (T2). The grey line represents the original transmissionspectrum of WASP-76b, whereas the black dots represent a binning of 0.1 Å. The red line represents the best fit to the data. The rest-frame wavelength of each individual line is represented by a dashed green vertical line.

In the text
thumbnail Fig. 7

Center to limb variation (CLV) and Rossiter-McLaughlin (RM) correction around the Na I lines.

In the text
thumbnail Fig. 8

Same as Fig. 6 but for ii H and K lines.

In the text
thumbnail Fig. 9

Same as Fig. 6 but for the Hα line.

In the text
thumbnail Fig. 10

Same as Fig. 6 but for the Li I line.

In the text
thumbnail Fig. 11

Same as Fig. 6 but for other elements. Here, we represent the combination of several lines in velocity space to strengthen the signal. Black points represent a binning of 10 km s−1 and the green dashed line represents the 0 km s−1 mark.

In the text
thumbnail Fig. 12

Same as Fig. 11 but for Mg I and K I.

In the text
thumbnail Fig. 13

Cross-correlation function against our line masks for Ti I, Cr I, Fe I, and Ni I. The black line represents CCF, whereas the red line represents the best fit to the Fe I CCF.

In the text
thumbnail Fig. 14

FWHM and Vwind for the lines studied in this work vs. Rλ.

In the text
thumbnail Fig. A.1

Same as Fig. 13 for diatomic molecules. From top to bottom: TiO, VO, and ZrO.

In the text
thumbnail Fig. A.2

Same as Fig. 5 but for Ca II HK lines.

In the text
thumbnail Fig. A.3

Same as Fig. 5 but for K I.

In the text
thumbnail Fig. A.4

Same as Fig. 5 but for Li I.

In the text
thumbnail Fig. A.5

Same as Fig. 5 but for Hα.

In the text

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