5,039 research outputs found

    Dirichlet space of multiply connected domains with Weil-Petersson class boundaries

    Full text link
    The restricted class of quasicircles sometimes called the "Weil-Petersson-class" has been a subject of interest in the last decade. In this paper we establish a Sokhotski-Plemelj jump formula for WP-class quasicircles, for boundary data in a certain conformally invariant Besov space. We show that this Besov space is precisely the set of traces on the boundary of harmonic functions of finite Dirichlet energy on the WP-class quasidisk. We apply this result to multiply connected domains, Sigma, which are the complement of n+1 WP-class quasidisks. Namely, we give a bounded isomorphism between the Dirichlet space D(Sigma) of Sigma and a direct sum of Dirichlet spaces, D-, of the unit disk. Writing the quasidisks as images of the disk under conformal maps (f_0,...,f_n), we also show that {(h \circ f_0,...,h \circ f_n) : h \in D(Sigma)} is the graph of a certain bounded Grunsky operator on D-.Comment: 24 pages. Introductory material revise

    Probabilistic Mass-Radius Relationship for Sub-Neptune-Sized Planets

    Get PDF
    The Kepler Mission has discovered thousands of planets with radii $<4\ R_\oplus,pavingthewayforthefirststatisticalstudiesofthedynamics,formation,andevolutionofthesesubβˆ’Neptunesandsuperβˆ’Earths.Planetarymassesareanimportantphysicalpropertyforthesestudies,andyetthevastmajorityofKeplerplanetcandidatesdonothavetheirsmeasured.AkeyconcernisthereforehowtomapthemeasuredradiitomassestimatesinthisEarthβˆ’toβˆ’NeptunesizerangewheretherearenoSolarSystemanalogs.Previousworkshavederiveddeterministic,oneβˆ’toβˆ’onerelationshipsbetweenradiusandmass.However,iftheseplanetsspanarangeofcompositionsasexpected,thenanintrinsicscatteraboutthisrelationshipmustexistinthepopulation.Herewepresentthefirstprobabilisticmassβˆ’radiusrelationship(Mβˆ’Rrelation)evaluatedwithinaBayesianframework,whichbothquantifiesthisintrinsicdispersionandtheuncertaintiesontheMβˆ’Rrelationparameters.Weanalyzehowtheresultsdependontheradiusrangeofthesample,andonhowthemassesweremeasured.AssumingthattheMβˆ’Rrelationcanbedescribedasapowerlawwithadispersionthatisconstantandnormallydistributed,wefindthat, paving the way for the first statistical studies of the dynamics, formation, and evolution of these sub-Neptunes and super-Earths. Planetary masses are an important physical property for these studies, and yet the vast majority of Kepler planet candidates do not have theirs measured. A key concern is therefore how to map the measured radii to mass estimates in this Earth-to-Neptune size range where there are no Solar System analogs. Previous works have derived deterministic, one-to-one relationships between radius and mass. However, if these planets span a range of compositions as expected, then an intrinsic scatter about this relationship must exist in the population. Here we present the first probabilistic mass-radius relationship (M-R relation) evaluated within a Bayesian framework, which both quantifies this intrinsic dispersion and the uncertainties on the M-R relation parameters. We analyze how the results depend on the radius range of the sample, and on how the masses were measured. Assuming that the M-R relation can be described as a power law with a dispersion that is constant and normally distributed, we find that M/M_\oplus=2.7(R/R_\oplus)^{1.3},ascatterinmassof, a scatter in mass of 1.9\ M_\oplus,andamassconstrainttophysicallyplausibledensities,isthe"bestβˆ’fit"probabilisticMβˆ’RrelationforthesampleofRVβˆ’measuredtransitingsubβˆ’Neptunes(, and a mass constraint to physically plausible densities, is the "best-fit" probabilistic M-R relation for the sample of RV-measured transiting sub-Neptunes (R_{pl}<4\ R_\oplus$). More broadly, this work provides a framework for further analyses of the M-R relation and its probable dependencies on period and stellar properties.Comment: 14 pages, 5 figures, 2 tables. Accepted to the Astrophysical Journal on April 28, 2016. Select posterior samples and code to use them to compute the posterior predictive mass distribution are available at https://github.com/dawolfgang/MRrelatio
    • …
    corecore