The Anisoplanatic Point Spread Function in Adaptive Optics

The effects of anisoplanatism on the adaptive optics point spread function are investigated. A model is derived that combines observations of the guide star with an analytic formulation of anisoplanatism to generate predictions for the adaptive optics point spread function at arbitrary locations within the field of view. The analytic formulation captures the dependencies of anisoplanatism on aperture diameter, observing wavelength, angular offset, zenith angle and turbulence profile. The predictions of this model are compared to narrowband 2.12 um and 1.65 um images of a 21 arcsec binary (mV=7.3, 7.6) acquired with the Palomar Adaptive Optics System on the Hale 5 meter telescope. Contemporaneous measurements of the turbulence profile made with a DIMM/MASS unit are used together with images of the primary to predict the point spread function of the binary companion. Predicted companion Strehl ratios are shown to match measurements to within a few percent, whereas predictions based on the isoplanatic angle approximation are highly discrepant. The predicted companion point spread functions are shown to agree with observations to 10%. These predictions are used to measure the differential photometry between binary members to an accuracy of 1 part in 10^{3}, and the differential astrometry to an accuracy of 1 mas. Errors in the differential astrometry are shown to be dominated by differential atmospheric tilt jitter. These results are compared to other techniques that have been employed for photometry, astrometry, and high contrast imaging.Comment: 26 pages, 7 figure

Understanding/unravelling carotenoid excited singlet states.

Carotenoids are essential light-harvesting pigments in natural photosynthesis. They absorb in the blue–green region of the solar spectrum and transfer the absorbed energy to (bacterio-)chlorophylls, and thus expand the wavelength range of light that is able to drive photosynthesis. This process is an example of singlet–singlet excitation energy transfer, and carotenoids serve to enhance the overall efficiency of photosynthetic light reactions. The photochemistry and photophysics of carotenoids have often been interpreted by referring to those of simple polyene molecules that do not possess any functional groups. However, this may not always be wise because carotenoids usually have a number of functional groups that induce the variety of photochemical behaviours in them. These differences can also make the interpretation of the singlet excited states of carotenoids very complicated. In this article, we review the properties of the singlet excited states of carotenoids with the aim of producing as coherent a picture as possible of what is currently known and what needs to be learned

Cluster optimisation using Cgroups at a tier-2

The Linux kernel feature Control Groups (cgroups) has been used to gather metrics on the resource usage of single and eight-core ATLAS workloads. It has been used to study the effects on performance of a reduction in the amount of physical memory. The results were used to optimise cluster performance, and consequently increase cluster throughput by up to 10%

Household epidemic models with varying infection response

This paper is concerned with SIR (susceptible--infected--removed) household epidemic models in which the infection response may be either mild or severe, with the type of response also affecting the infectiousness of an individual. Two different models are analysed. In the first model, the infection status of an individual is predetermined, perhaps due to partial immunity, and in the second, the infection status of an individual depends on the infection status of its infector and on whether the individual was infected by a within- or between-household contact. The first scenario may be modelled using a multitype household epidemic model, and the second scenario by a model we denote by the infector-dependent-severity household epidemic model. Large population results of the two models are derived, with the focus being on the distribution of the total numbers of mild and severe cases in a typical household, of any given size, in the event that the epidemic becomes established. The aim of the paper is to investigate whether it is possible to determine which of the two underlying explanations is causing the varying response when given final size household outbreak data containing mild and severe cases. We conduct numerical studies which show that, given data on sufficiently many households, it is generally possible to discriminate between the two models by comparing the Kullback-Leibler divergence for the two fitted models to these data