Speakers: Ben Kalus
We consider the shape of the posterior distribution to be used when fitting cosmological models to power spectra measured from galaxy surveys. At very large scales, Gaussian posterior distributions in the power do not approximate the posterior distribution PR we expect for a Gaussian density field δk , even if we vary the covariance matrix according to the model to be tested. We compare alternative posterior distributions with PR , both mode-by-mode and in terms of expected fNL -measurements.Marginalising over a Gaussian posterior distribution Pf with fixed covariance matrix yields a best fit value of fNL which, for a data set with the characteristics of Euclid, will be underestimated by △fNL = 0.4 , while for the data release 9 (DR9) of the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) it will be underestimated by △fNL = 19.1 . The inverse cubic normal distribution (PICN ) agrees very well with PR at all scales and for all data sets, hence providing the same marginalised value. Adopting this likelihood function means that we do not require a different covariance matrix for each model to be tested: this dependence is absorbed into the functional form of the posterior. Thus, the computational burden of analysis is significantly reduced.
Speaker: Ben Bose
Scalar-tensor theory (STT) is one of the alternative theories of gravitation to Einstein’s general relativity, which includes a scalar field in the gravitational framework in addition to the usual metric tensor. We have investigated the effects a scalar field and its coupling to gravity have on neutron star structure, particularly on the neutron star radius and its maximum mass in STT. Three different equations of state for the neutron star were considered. In this short seminar I give a brief mention to the scalar fields present in modern physical theories, an introduction to STT and finally a presentation of our results and possible implications.