Approximation du calcul de la taille échantillonnale pour les tests à hypothèses multiples lorsque r parmis m hypothèses doivent être significatives

Généralement, dans les situations d’hypothèses multiples on cherche à rejeter toutes les hypothèses ou bien une seule d’entre d’elles. Depuis quelques temps on voit apparaître le besoin de répondre à la question : « Peut-on rejeter au moins r hypothèses ? ». Toutefois, les outils statisques pour répondre à cette question sont rares dans la littérature. Nous avons donc entrepris de développer les formules générales de puissance pour les procédures les plus utilisées, soit celles de Bonferroni, de Hochberg et de Holm. Nous avons développé un package R pour le calcul de la taille échantilonnalle pour les tests à hypothèses multiples (multiple endpoints), où l’on désire qu’au moins r des m hypothèses soient significatives. Nous nous limitons au cas où toutes les variables sont continues et nous présentons quatre situations différentes qui dépendent de la structure de la matrice de variance-covariance des données.Generally, in multiple endpoints situations we want to reject all hypotheses or at least only one of them. For some time now, we see emerge the need to answer the question : "Can we reject at least r hypotheses ?" However, the statistical tools to answer this new problem are rare in the litterature. We decide to develop general power formulas for the principals procedures : Bonferroni’s, Hochberg’s and Holm’s procedures. We also develop an R package for the sample size calculation for multiple endpoints, when we want to reject at least r hypotheses. We limit ourselves in the case where all the variables are continuous and we present four different situations depending on the structure of the data’s variance-covariance matrix

Spectral cube extraction for the VLT/SPHERE IFS: Open-source pipeline with full forward modeling and improved sensitivity

We present a new open-source data-reduction pipeline to reconstruct spectral data cubes from raw SPHERE integral-field spectrograph (IFS) data. The pipeline is written in Python and based on the pipeline that was developed for the CHARIS IFS. It introduces several improvements to SPHERE data analysis that ultimately produce significant improvements in postprocessing sensitivity. We first used new data to measure SPHERE lenslet point spread functions (PSFs) at the four laser calibration wavelengths. These lenslet PSFs enabled us to forward-model SPHERE data, to extract spectra using a least-squares fit, and to remove spectral crosstalk using the measured lenslet PSFs. Our approach also reduces the number of required interpolations, both spectral and spatial, and can preserve the original hexagonal lenslet geometry in the SPHERE IFS. In the case of least-squares extraction, no interpolation of the data is performed. We demonstrate this new pipeline on the directly imaged exoplanet 51 Eri b and on observations of the hot white dwarf companion to HD 2133. The extracted spectrum of HD 2133B matches theoretical models, demonstrating spectrophotometric calibration that is good to a few percent. Postprocessing on two 51 Eri b data sets demonstrates a median improvement in sensitivity of 80% and 30% for the 2015 and 2017 data, respectively, compared to the use of cubes reconstructed by the SPHERE Data Center. The largest improvements are seen for poorer observing conditions. The new SPHERE pipeline takes less than three minutes to produce a data cube on a modern laptop, making it practical to reprocess all SPHERE IFS data.Comment: 17 pages, 11 figures. Software available at: https://github.com/PrincetonUniversity/charis-de

WEIRD: Wide-orbit Exoplanet search with InfraRed Direct imaging

We report results from the Wide-orbit Exoplanet search with InfraRed Direct imaging (WEIRD), a survey designed to search for Jupiter-like companions on very wide orbits (1000 to 5000 AU) around young stars ($<$120 Myr) that are known members of moving groups in the solar neighborhood ($<$70 pc). Sharing the same age, distance, and metallicity as their host while being on large enough orbits to be studied as "isolated" objects make such companions prime targets for spectroscopic observations and valuable benchmark objects for exoplanet atmosphere models. The search strategy is based on deep imaging in multiple bands across the near-infrared domain. For all 177 objects of our sample, $z_{ab}^\prime$, $J$, [3.6] and [4.5] images were obtained with CFHT/MegaCam, GEMINI/GMOS, CFHT/WIRCam, GEMINI/Flamingos-2, and $Spitzer$/IRAC. Using this set of 4 images per target, we searched for sources with red $z_{ab}^\prime$ and $[3.6]-[4.5]$ colors, typically reaching good completeness down to 2Mjup companions, while going down to 1Mjup for some targets, at separations of $1000-5000$ AU. The search yielded 4 candidate companions with the expected colors, but they were all rejected through follow-up proper motion observations. Our results constrain the occurrence of 1-13 Mjup planetary-mass companions on orbits with a semi-major axis between 1000 and 5000 AU at less than 0.03, with a 95\% confidence level.Comment: 55 pages, 16 figures, accepted to A

Approche expérimentale du bang sonique des aéronefs

La présence d'un mobile supersonique dans l'atmosphère crée une onde de choc, c'est-à-dire une perturbation atmosphérique caractérisée par des discontinuités de pression. Cette perturbation évolue pour former à quelques kilomètres l'onde en N caractéristique du bang sonique. Des campagnes de mesure visant à évaluer les nuisances sonores liées au bang ont été effectuées avant la mise en service du supersonique Concorde. A partir des années 80, l'ONERA a exploité des données acoustiques issues des vols commerciaux transatlantiques de l'appareil pour approfondir la connaissance du phénomène de bang, notamment sous l'angle de sa propagation à grande distance. Nous présentons les résultats de simulations avec un code de rayons 3D en atmosphère réelle à partir des données de vol, visant à obtenir les limites des zones de retombées acoustiques. L'analyse fréquentielle des signaux enregistrés en Bretagne a permis de trouver une nette corrélation entre la fréquence pic et la distance à la source

Plum pudding random medium model of biological tissue toward remote microscopy from spectroscopic light scattering

Biological tissue has a complex structure and exhibits rich spectroscopic behavior. There is \emph{no} tissue model up to now able to account for the observed spectroscopy of tissue light scattering and its anisotropy. Here we present, \emph{for the first time}, a plum pudding random medium (PPRM) model for biological tissue which succinctly describes tissue as a superposition of distinctive scattering structures (plum) embedded inside a fractal continuous medium of background refractive index fluctuation (pudding). PPRM faithfully reproduces the wavelength dependence of tissue light scattering and attributes the "anomalous" trend in the anisotropy to the plum and the powerlaw dependence of the reduced scattering coefficient to the fractal scattering pudding. Most importantly, PPRM opens up a novel venue of quantifying the tissue architecture and microscopic structures on average from macroscopic probing of the bulk with scattered light alone without tissue excision. We demonstrate this potential by visualizing the fine microscopic structural alterations in breast tissue (adipose, glandular, fibrocystic, fibroadenoma, and ductal carcinoma) deduced from noncontact spectroscopic measurement