High-precision transit observations of the exoplanet WASP-13b with the RISE instrument

WASP-13b is a sub-Jupiter mass exoplanet orbiting a G1V type star with a period of 4.35 d.The current uncertainty in its impact parameter (0 < b < 0.46) results in poorly definedstellar and planetary radii. To better constrain the impact parameter, we have obtained highprecisiontransit observations with the rapid imager to search for exoplanets (RISE) instrumentmounted on 2.0-m Liverpool Telescope. We present four new transits which are fitted witha Markov chain Monte Carlo routine to derive accurate system parameters. We found anorbital inclination of 85. ◦ 2 ± 0. ◦ 3 resulting in stellar and planetary radii of 1.56 ± 0.04 Rand 1.39 ± 0.05RJup, respectively. This suggests that the host star has evolved off the mainsequence and is in the hydrogen-shell-burning phase.We also discuss how the limb darkeningaffects the derived system parameters.With a density of 0.17ρJ,WASP-13b joins the group oflow-density planets whose radii are too large to be explained by standard irradiation models.We derive a new ephemeris for the system, T0 = 245 5575.5136 ± 0.0016 (HJD) and P =4.353 011 ± 0.000 013 d. The planet equilibrium temperature (Tequ = 1500 K) and the brighthost star (V = 10.4mag) make it a good candidate for follow-up atmospheric studies.

How Structural Model Variability Influences Transonic Aeroelastic Stability

This paper considers the ways in which structural model parameter variability can influence aeroelastic stability. Previous work on formulating the stability calculation (with the Euler equations providing the aerodynamic predictions) is exploited to use Monte Carlo, interval, and perturbation calculations to allow this question to be investigated. Three routes are identified. The first involves variable normal-mode frequencies only. The second involves normal-mode frequencies and shapes. Finally, the third, in addition to normal-mode frequencies and shapes, also includes their influence on the static equilibrium. Previous work has suggested only considering the first route, which allows significant gains in computational efficiency if reduced-order models can be built for the aerodynamics. However, results in the current paper show that neglecting the mode-shape variation can give misleading results for the flutter-onset prediction, complicating the development of reduced aerodynamic models for variability analysis.

The Type Ia supernova 2004s, a clone of SN 2001el, and the optimal photometric bands for extinction estimation

We present optical (UBVRI) and near-IR (YJHK) photometry of the normal Type Ia supernova (SN) 2004S. We also present eight optical spectra and one near-IR spectrum of SN 2004S. The light curves and spectra are nearly identical to those of SN 2001el. This is the first time we have seen optical and IR light curves of two Type Ia SNe match so closely. Within the one parameter family of light curves for normal Type Ia SNe, that two objects should have such similar light curves implies that they had identical intrinsic colors and produced similar amounts of Ni-56. From the similarities of the light-curve shapes we obtain a set of extinctions as a function of wavelength that allows a simultaneous solution for the distance modulus difference of the two objects, the difference of the host galaxy extinctions, and RV. Since SN 2001el had roughly an order of magnitude more host galaxy extinction than SN 2004S, the value of R-V = 2.15(-0.22)(+0.24) pertains primarily to dust in the host galaxy of SN 2001el. We have also shown via Monte Carlo simulations that adding rest-frame J-band photometry to the complement of BVRI photometry of Type Ia SNe decreases the uncertainty in the distance modulus by a factor of 2.7. A combination of rest-frame optical and near-IR photometry clearly gives more accurate distances than using rest-frame optical photometry alone.

Plasma parameter characterization of a reflex plasma source operating in oxygen

A reflex discharge plasma, obtained as a hybrid between a Penning discharge plasma (PDP) and a hollow-cathode discharge (HCD) plasma, is analysed as a possible direction-current, high-density plasma source. The experiment is run in oxygen at pressures of 10 mTorr and 1 mTorr, and for discharge currents of 100 to 200 mA. Although the gas pressure is considerably lower than those used in HCDs, the hollow-cathode effect (HCE) occurs for current levels higher than 100 mA and leads to plasma densities comparable with those obtained using inductive plasma sources. The presence of a constant magnetic field leads to the enhancement of electron emission from cathodes under ion bombardment, and to the decreasing of the ion loss by diffusion to the wall.

Risk-informed decision-making in the presence of epistemic uncertainty

An important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we, therefore, propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decisionmaker’s attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process.