J/A+A/673/A98ivo://CDS.VizieR/J/A+A/673/A98doi:10.26093/cds/vizier.36730098CDSBrown-Sevilla S.B.Maire A.-L.Molliere P.Samland M.Feldt M.,Brandner W.Henning T.Gratton R.Janson M.Stolker T.Hagelberg J.,Zurlo A.Cantalloube F.Boccaletti A.Bonnefoy M.Chauvin G.,Desidera S.D'Orazi V.Lagrange A.-M.Langlois M.Menard F.Mesa D.,Meyer M.Pavlov A.Petit C.Rochat S.Rouan D.Schmidt T.Vigan A.,Weber L.2024-11-28T20:03:31Z2023-05-17T08:38:19ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frmultiple-starsexoplanetsspectroscopyinfrared-astronomyastronomical-modelsstellar-atmospheres51 Eridani b is one of the closest directly imaged exoplanets (~11au). We aim to better constrain its atmospheric properties by using a retrieval approach on higher signal-to-noise ratio data than previously reported. We present a higher signal-to-noise ratio YH spectrum of the planet and revised K1K2 photometry (M_K1_=15.11+/-0.04mag, M_K2_=17.11+/-0.38mag). We compare the results of using the atmospheric retrieval code petitRADTRANS to a self-consistent model to fit our near-infrared SPHERE observations. Additionally, we attempt to reproduce previous results with the retrieval approach and compare the results to self-consistent models using the best-fit parameters from the retrieval as priors. The best-fit parameters obtained using an atmospheric retrieval differ from previous results using self-consistent models. In general, we find that our solutions tend towards cloud-free atmospheres (e.g. log_tau_clouds=~5.20+/-1.44). For our 'nominal' model with new data, we find a lower metallicity ([Fe/H]=0.26+/-0.30dex) and C/O ratio (0.38+/-0.09), and a slightly higher effective temperature (T_eff_=807+/-45K) than previous studies. The surface gravity (logg=4.05+/-0.37) is in agreement with the reported values in the literature within uncertainties. We estimate the mass of the planet to be between 2 and 4M_Jup_. When comparing with self-consistent models, we encounter a known correlation between the presence of clouds and the shape of the P-T profiles. Our findings support the idea that results from atmospheric retrievals should not be discussed in isolation, but rather along with self-consistent temperature structures obtained using the retrieval's best-fit parameters. This, along with observations at longer wavelengths, might help to better characterise the atmospheres and determine their degree of cloudiness.2023A&A...673A..98Bhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/673/A98CatalogResearchIsServedByTAP VizieR generic servicerelated-toJ/A+A/603/A57 : 51 Eri b SPHERE/IFS spectra + atmosphere models (Samland+ 2017)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/A+A/673/A98https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/A+A/673/A98http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/A+A/673/A98https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/A+A/673/A98https://vizier.iucaa.in/viz-bin/votable?-source=J/A+A/673/A98http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/A+A/673/A98GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapInfrareddefaultJ/A+A/673/A98/spectrumNear-infrared (YH) spectrum of 51 Eri brecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintlambdaWavelengthumem.wldoubleFluxFlux (f_lambda_)W.m**-2.um**-1phot.fluxdoublee_FluxFlux uncertaintyW.m**-2.um**-1stat.error;phot.fluxdoubleJ/A+A/673/A98/correlCorrelation matrix for the YH spectrumC34Correlation coefficient for i=34stat.fit.paramdoubleC35Correlation coefficient for i=35stat.fit.paramdoubleC36Correlation coefficient for i=36stat.fit.paramdoubleC37Correlation coefficient for i=37stat.fit.paramdoubleC38Correlation coefficient for i=38stat.fit.paramdoubleC39Correlation coefficient for i=39stat.fit.paramdoublerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintC1Correlation coefficient for i=1stat.fit.paramdoubleC2Correlation coefficient for i=2stat.fit.paramdoubleC3Correlation coefficient for i=3stat.fit.paramdoubleC4Correlation coefficient for i=4stat.fit.paramdoubleC5Correlation coefficient for i=5stat.fit.paramdoubleC6Correlation coefficient for i=6stat.fit.paramdoubleC7Correlation coefficient for i=7stat.fit.paramdoubleC8Correlation coefficient for i=8stat.fit.paramdoubleC9Correlation coefficient for i=9stat.fit.paramdoubleC10Correlation coefficient for i=10stat.fit.paramdoubleC11Correlation coefficient for i=11stat.fit.paramdoubleC12Correlation coefficient for i=12stat.fit.paramdoubleC13Correlation coefficient for i=13stat.fit.paramdoubleC14Correlation coefficient for i=14stat.fit.paramdoubleC15Correlation coefficient for i=15stat.fit.paramdoubleC16Correlation coefficient for i=16stat.fit.paramdoubleC17Correlation coefficient for i=17stat.fit.paramdoubleC18Correlation coefficient for i=18stat.fit.paramdoubleC19Correlation coefficient for i=19stat.fit.paramdoubleC20Correlation coefficient for i=20stat.fit.paramdoubleC21Correlation coefficient for i=21stat.fit.paramdoubleC22Correlation coefficient for i=22stat.fit.paramdoubleC23Correlation coefficient for i=23stat.fit.paramdoubleC24Correlation coefficient for i=24stat.fit.paramdoubleC25Correlation coefficient for i=25stat.fit.paramdoubleC26Correlation coefficient for i=26stat.fit.paramdoubleC27Correlation coefficient for i=27stat.fit.paramdoubleC28Correlation coefficient for i=28stat.fit.paramdoubleC29Correlation coefficient for i=29stat.fit.paramdoubleC30Correlation coefficient for i=30stat.fit.paramdoubleC31Correlation coefficient for i=31stat.fit.paramdoubleC32Correlation coefficient for i=32stat.fit.paramdoubleC33Correlation coefficient for i=33stat.fit.paramdouble