We tested two different scenarios by assuming a cloudy atmosphere model  in Table 4 and a cloud-free atmosphere model  in Table 4. Clouds were fitted assuming a grey opacity model and cloud top pressures —i. All the priors we assumed are listed in Table 4. In our analysis, the fitted parameters were the molecular abundances, the temperature, the mean molecular weight, the radius at 10 bar, and, in the cloudy scenario, the cloud top pressure.
In agreement with [ 56 ], we quantified the significance of our detections with the Atmospheric Detection Index ADI a positively defined Bayes Factor between the nominal atmospheric model and a flat-line model i. This value was then translated into a statistical significance [ 30 ] by using Table 2 of [ 6 ]. Our TauREx3 retrieval results are listed in Table 4. Figure 3 shows the best-fit models and the contribution plots for the two scenarios tested. The posterior distributions are shown in Fig.
Accordingly to the Bayesian evidences the cloudy scenario seems to be strongly, but not decisively, favorite. For both scenarios the retrieved temperature is lower than the predicted equilibrium temperature. This could be explained by the fact that we are probing the atmosphere in the terminator area, and we modeled the atmosphere in 1D using an isothermal profile e.
This is in agreement with what was highlighted by [ 49 ], who found evidence of a global trend between the equilibrium and the retrieved temperatures, with the latter almost always showing lower values. The retrieved radii are compatible with the theoretical value 1. In the cloudy-model, we note a correlation between the H 2 O abundance, the radius, and the cloud pressure. For less H 2 O, the model requires deeper clouds and a higher base planet radius.
Footnote 1 This can also be seen in the contribution plot in the bottom-left panel of Fig. The signal is theoretically blocked by this layer and nothing can be observed at higher pressures. Molecules found below this line are unconstrained.
On the contrary, HRS is able to detect molecular absorbers even if there is a high altitude clouds deck, given that it is most sensitive to the spectral lines cores that are formed above the clouds at lower pressure see [ 23 , 26 ]. Upper Panel: Best-fit models for the two different scenarios tested here: a cloudy atmosphere green , and a cloud-free atmosphere violet.
Bottom Panel: contribution plot for the cloudy-case left panel and for the cloud-free scenario right panel. The cloudy and the cloud-free scenarios are represented in green and in pink, respectively. For consistency, we do not plot the clouds pressure posterior distribution. Upcoming observatories in space and on the ground, thanks to their broader spectral coverage and higher signal-to-noise ratio, will enable the analysis of a large number of planets.
In this section, we describe a simulation we performed to explore the potential of using Ariel with the approach described above. As in the rest of the paper our benchmark object is HD b. Firstly, we simulated an high-resolution transmission spectrum of HD b by using the TauREx3 algorithm in forward mode. The planetary mass and stellar parameters i. The trace gases we considered were the same included in the HST retrieval analysis i. We performed two different simulations:.
A first experiment was run requiring molecular abundances being dictated by one of the models in thermochemical equilibrium maximised by the likelihood framework at HRS. We then tried a second experiment -a more unrealistic example- where we imposed the tracers abundances to be equal to those values that maximise the cross-correlation analysis at HRS see Table 1.
We must emphasize that this simulation is only an exercise, it does not claim to reproduce a real observed HST spectrum. We are in fact using the abundances reported in Table 1 which, as previously pointed out, do not correspond to any specific chemico-physical scenario of the atmosphere.
Next, we binned the high-resolution transmission spectrum to the resolutions of the Ariel spectrometers i. NIRSpec, 1. Finally, we performed atmospheric retrievals using TauREx3 in fitting mode. Table 5 lists the TauREx retrieval results we obtained, whilst the posterior distribution and the best-fit spectrum are plotted in turquoise in Fig. As a comparison, Figs. Figure 5 shows the retrieval results we obtained for both HST and Ariel observations for the a scenario.
The 10 bar radius correlates with the temperature distribution: the higher the temperature is, the smaller radius. The Ariel retrieval seems to prefer a slightly higher temperature -and thus a lower 10 bar radius- than the HST simulation.
The results obtained for the scenario b are instead shown in Fig. As in the previous simulation, we are not able to constrain the CO abundance distribution. These simulations highlight several aspects, which are important to discuss. We can appreciate the improvement in putting constraints on the chemical abundances that we can obtain with Ariel. This aspect has been also pointed out by [ 52 ]. On one side, thanks to its wide spectral coverage, Ariel will permit the detection of several molecular species that do not have strong absorption bands in the WFC3 wavelength range.
On the other side, Ariel observations will probe a much wider pressure range than WFC3, from approximately 1 to 10 3 mbar [ 52 ]. Posterior distributions for the simulated transmission spectrum of HD b with molecular abundances that maximise the cross-correlation analysis at HRS simulation b as observed by Ariel in turquoise , with overplotted the posterior distribution obtained for simulated HST data in orange.
However, when dealing with low molecular abundances, such as those tested in simulation a , our experiments revealed that also Ariel has some limitations to well constrain the investigated trace-gas abundances and we have to use a combination of HRS results and Ariel data. Indeed, if we consider the Ariel results alone, we could not claim the presence of CO and we can only put an upper limit on the abundance of several tracers.
In the near future, Ariel will allow for detailed characterisation of thousands of exoplanetary atmospheres. In this perspective, exploiting the synergies between Ariel and current e. Moreover, it could provide a local pseudo-continuum to HRS observations. On the other hand, the contributions of multiple species are expected to overlap at the low resolution of Ariel, and high-resolution instruments like GIANO-B can help in understanding which atmospheric constituents are expected.
Furthermore, in presence of a deep cloud deck, some spectral features that could be masked with LRS, may be accessible with HRS, since it probes higher atmospheric levels. It is thus clear that only a combination of low- and high-resolution data in a consistent retrieval framework can provide absolute molecular abundances, avoiding confusion between species.
The power of having a joint retrieval of multi-spectral resolution data was shown by [ 9 ] who provided a simple framework for combining HRS data and LRS data within a unified likelihood function. However, these calculation were restricted to an extremely narrow wavelength range at high-resolution nm , and limited to emission spectroscopy data.
No such framework has ever been applied to transmission spectroscopy. In this work, we took HD b as a reference, but the same study can be performed on several exoplanets. To understand the number of potential objects on which we can perform a similar analysis, it would be useful to know how many of the future Ariel targets could be observed with ground-based HRS.
The number of suitable targets is limited by the 4-m aperture of the TNG, which restricts the observable sample to relatively bright stars. In the near future, as soon as high-resolution instrumentation at ELTs is available, this sample is expected to increase.
Even though the analysis presented in this work is focused on the nIR band, we have to highlight also the benefits that can be obtained by combining HRS and Ariel data in the VIS. Thus, HRS could be employed to place upper limits on the molecular abundances.
The HRS prior information could then be incorporated into the retrieval of the Ariel data -in a similar way to that proposed in this study- to help break the degeneracy that will be seen. This approach has recently been taken for the VO molecule.
Indeed, HST observations of WASP b seemed to show evidence of VO [ 19 ], whilst high-resolution data only succeeded in putting an upper limit on this opacity [ 44 ]. We applied different methods to analyse the two different datasets. We noted a considerable degeneracy in the posteriors distributions of many molecular species obtained at low resolution. Successively, we performed a simulation to test the ability of the Ariel space mission to give precise constraints on the atmospheric chemical abundances.
We added instrumental noise to the model by using the ArielRad simulator and we interpreted it with TauREx3 in retrieval mode. In this respect, we need to further stress that in this work we did not carry out a true combination of LRS and HRS results such as that advocated in recent literature e. However, to perform a true combination of these two techniques, work still needs to be done. The retrieved cloud-top pressure is greater than the value reported in the literature i.
This discrepancy could be due to differences in the data reduction pipeline, assumptions made during the retrieval of the real data e. Our findings are however in agreement with [ 55 ], and [ 56 ]. We used the maximum of the temperature posterior distribution and not the median value because the distribution seems to converge towards the lower edge of our priors. Abel, M. A 25 , — Google Scholar. ADS Google Scholar. Al-Refaie, A. Barber, R.
Barstow, J. RAS 4 , — Benneke, B. Birkby, J. Bonomo, A. Investigating giant planet migration history via improved eccentricity and mass determination for transiting planets. Brogi, M. Nature , — Water in the transmission spectrum of HD b. Caldas, A. Chubb, K. Spectra of acetylene. Claret, A. Astronomy and Astrophysics 3 , — Claudi, R. Plus 8 , Coles, P. A rotation-vibration line list for hot ammonia. Evans, T. RAS 1 , — J 6 , Feroz, F. Fletcher, L. Gandhi, S.
RAS 2 , — Giacobbe, P. Nature-Accepted Hargreaves, R. Hood, C. Howarth, I. RAS 3 , — Huang, X. Radiative Tran. Irwin, P. Quantitiative Spectrosc. Kass, R. Knutson, H. Kreidberg, L. Nature , 69—72 a. Nature , 69—72 b. Nature , 87—90 Kurucz, R. SAO Special Report Li, G. Line, M. A uniform analysis of nine planets and their C to O ratios. MacDonald, R. Madhusudhan, N. Astron Astrophys. Merritt, S. Companies need their legal teams to keep pace with the commercial needs of the business—while at the same time, legal departments are increasingly being asked to do more with the same or fewer resources.
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The legal world is transforming and Deloitte Legal is developing the tools to help legal business during this transformative time. When we suggested this idea to lawyers, we uncovered a considerable overlap and synergy. When we raised the possibility of LMC during research and discussions with in-house counsel, the feedback was encouraging.
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