The standard noise model in gravitational wave (GW) data analysis assumes detector noise is stationary and Gaussian distributed, with a known power spectral density (PSD) that is usually estimated using clean off-source data. Real GW data often depar
t from these assumptions, and misspecified parametric models of the PSD could result in misleading inferences. We propose a Bayesian semiparametric approach to improve this. We use a nonparametric Bernstein polynomial prior on the PSD, with weights attained via a Dirichlet process distribution, and update this using the Whittle likelihood. Posterior samples are obtained using a blocked Metropolis-within-Gibbs sampler. We simultaneously estimate the reconstruction parameters of a rotating core collapse supernova GW burst that has been embedded in simulated Advanced LIGO noise. We also discuss an approach to deal with non-stationary data by breaking longer data streams into smaller and locally stationary components.
Two structures are observed close to the kinematic threshold in the $Xi_b^0 pi^-$ mass spectrum in a sample of proton-proton collision data, corresponding to an integrated luminosity of 3.0 fb$^{-1}$ recorded by the LHCb experiment. In the quark mode
l, two baryonic resonances with quark content $bds$ are expected in this mass region: the spin-parity $J^P = frac{1}{2}^+$ and $J^P=frac{3}{2}^+$ states, denoted $Xi_b^{prime -}$ and $Xi_b^{*-}$. Interpreting the structures as these resonances, we measure the mass differences and the width of the heavier state to be $m(Xi_b^{prime -}) - m(Xi_b^0) - m(pi^{-}) = 3.653 pm 0.018 pm 0.006$ MeV$/c^2$, $m(Xi_b^{*-}) - m(Xi_b^0) - m(pi^{-}) = 23.96 pm 0.12 pm 0.06$ MeV$/c^2$, $Gamma(Xi_b^{*-}) = 1.65 pm 0.31 pm 0.10$ MeV, where the first and second uncertainties are statistical and systematic, respectively. The width of the lighter state is consistent with zero, and we place an upper limit of $Gamma(Xi_b^{prime -}) < 0.08$ MeV at 95% confidence level. Relative production rates of these states are also reported.
A summary of recent progress in charm mixing and CP violation is presented, with a heavy bias towards experimental results.
Using the latest numerical simulations of rotating stellar core collapse, we present a Bayesian framework to extract the physical information encoded in noisy gravitational wave signals. We fit Bayesian principal component regression models with know
n and unknown signal arrival times to reconstruct gravitational wave signals, and subsequently fit known astrophysical parameters on the posterior means of the principal component coefficients using a linear model. We predict the ratio of rotational kinetic energy to gravitational energy of the inner core at bounce by sampling from the posterior predictive distribution, and find that these predictions are generally very close to the true parameter values, with $90%$ credible intervals $sim 0.04$ and $sim 0.06$ wide for the known and unknown arrival time models respectively. Two supervised machine learning methods are implemented to classify precollapse differential rotation, and we find that these methods discriminate rapidly rotating progenitors particularly well. We also introduce a constrained optimization approach to model selection to find an optimal number of principal components in the signal reconstruction step. Using this approach, we select 14 principal components as the most parsimonious model.
A search for the doubly charmed baryon Xi_cc^+ in the decay mode Xi_cc^+ -> Lambda_c^+ K^- pi^+ is performed with a data sample, corresponding to an integrated luminosity of 0.65/fb, of pp collisions recorded at a centre-of-mass energy of 7 TeV. No s
ignificant signal is found in the mass range 3300-3800 MeV/c^2. Upper limits at the 95% confidence level on the ratio of the Xi_cc^+ production cross-section times branching fraction to that of the Lambda_c^+, R, are given as a function of the Xi_cc^+ mass and lifetime. The largest upper limits range from R < 1.5 x 10^-2 for a lifetime of 100 fs to R < 3.9 x 10^-4 for a lifetime of 400 fs.
