Study of CP Violating Effects in Time Dependent B0(B0ˉ)→D(∗)∓π±B^0(\bar{B^0}) \to D^{(*)\mp}\pi^{\pm} Decays

We report measurements of time dependent decay rates for $B^0(\bar{B}^0) \to D^{(*)\mp}\pi^{\pm}$ decays and extraction of CP violation parameters containing $\phi_3$. Using fully reconstructed $D^{(*)}\pi$ events from a $140 {\rm fb}^{-1}$ data sample collected at the $\Upsilon(4S)$ resonance, we obtain the CP violation parameters for $D^* \pi$ and $D \pi$ decays, $2R_{D^{(*)} \pi} \sin (2\phi_1 + \phi_3 \pm \delta_{D^{(*)} \pi})$, where $R_{D^{(*)} \pi}$ is the ratio of the magnitudes of the doubly-Cabibbo-suppressed and Cabibbo-favoured amplitudes, and $\delta_{D^{(*)} \pi}$ is the strong phase difference between them. Under the assumption of $\delta_{D^{(*)} \pi}$ being close to either 0 or $180^{\circ}$, we obtain $|2R_{D^* \pi} \sin (2\phi_1 + \phi_3)| = 0.060 \pm 0.040(\mathrm{stat}) \pm 0.019(\mathrm{sys})$ and $|2R_{D \pi} \sin (2\phi_1 + \phi_3)| = 0.061 \pm 0.037(\mathrm{stat}) \pm 0.018(\mathrm{sys})$.Comment: 9 pages, 3 figures, Submitted to Physical Review Letter

Bayesian Inference in Processing Experimental Data: Principles and Basic Applications

This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as: model comparison (including the automatic Ockham's Razor filter provided by the Bayesian approach); parametric inference; quantification of the uncertainty about the value of physical quantities, also taking into account systematic effects; role of marginalization; posterior characterization; predictive distributions; hierarchical modelling and hyperparameters; Gaussian approximation of the posterior and recovery of conventional methods, especially maximum likelihood and chi-square fits under well defined conditions; conjugate priors, transformation invariance and maximum entropy motivated priors; Monte Carlo estimates of expectation, including a short introduction to Markov Chain Monte Carlo methods.Comment: 40 pages, 2 figures, invited paper for Reports on Progress in Physic

A next generation, pilot-scale continuous sterilization system for fermentation media

A new continuous sterilization system was designed, constructed, started up, and qualified for media sterilization for secondary metabolite cultivations, bioconversions, and enzyme production. An existing Honeywell Total Distributed Control 3000-based control system was extended using redundant High performance Process Manager controllers for 98 I/O (input/output) points. This new equipment was retrofitted into an industrial research fermentation pilot plant, designed and constructed in the early 1980s. Design strategies of this new continuous sterilizer system and the expanded control system are described and compared with the literature (including dairy and bio-waste inactivation applications) and the weaknesses of the prior installation for expected effectiveness. In addition, the reasoning behind selection of some of these improved features has been incorporated. Examples of enhancements adopted include sanitary heat exchanger (HEX) design, incorporation of a “flash” cooling HEX, on-line calculation of F(o) and R(o), and use of field I/O modules located near the vessel to permit low-cost addition of new instrumentation. Sterilizer performance also was characterized over the expected range of operating conditions. Differences between design and observed temperature, pressure, and other profiles were quantified and investigated