Effects of dissociation/recombination on the day-night temperature contrasts of ultra-hot Jupiters

Secondary eclipse observations of ultra-hot Jupiters have found evidence that hydrogen is dissociated on their daysides. Additionally, full-phase light curve observations of ultra-hot Jupiters show a smaller day-night emitted flux contrast than that expected from previous theory. Recently, it was proposed by Bell & Cowan (2018) that the heat intake to dissociate hydrogen and heat release due to recombination of dissociated hydrogen can affect the atmospheric circulation of ultra-hot Jupiters. In this work, we add cooling/heating due to dissociation/recombination into the analytic theory of Komacek & Showman (2016) and Zhang & Showman (2017) for the dayside-nightside temperature contrasts of hot Jupiters. We find that at high values of incident stellar flux, the day-night temperature contrast of ultra-hot Jupiters may decrease with increasing incident stellar flux due to dissociation/recombination, the opposite of that expected without including the effects of dissociation/recombination. We propose that a combination of a greater number of full-phase light curve observations of ultra-hot Jupiters and future General Circulation Models that include the effects of dissociation/recombination could determine in detail how the atmospheric circulation of ultra-hot Jupiters differs from that of cooler planets.Comment: Accepted at Research Notes of the AA

An empirical comparison of supervised machine learning techniques in bioinformatics

Research in bioinformatics is driven by the experimental data. Current biological databases are populated by vast amounts of experimental data. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. At present, with various learning algorithms available in the literature, researchers are facing difficulties in choosing the best method that can apply to their data. We performed an empirical study on 7 individual learning systems and 9 different combined methods on 4 different biological data sets, and provide some suggested issues to be considered when answering the following questions: (i) How does one choose which algorithm is best suitable for their data set? (ii) Are combined methods better than a single approach? (iii) How does one compare the effectiveness of a particular algorithm to the others

Imaging interstitial iron concentrations in boron-doped crystalline silicon using photoluminescence

Imaging the band-to-band photoluminescence of silicon wafers is known to provide rapid and high-resolution images of the carrier lifetime. Here, we show that such photoluminescence images, taken before and after dissociation of iron-boron pairs, allow an accurate image of the interstitial iron concentration across a boron-doped p-type silicon wafer to be generated. Such iron images can be obtained more rapidly than with existing point-by-point iron mapping techniques. However, because the technique is best used at moderate illumination intensities, it is important to adopt a generalized analysis that takes account of different injection levels across a wafer. The technique has been verified via measurement of a deliberately contaminated single-crystal silicon wafer with a range of known iron concentrations. It has also been applied to directionally solidified ingot-grown multicrystalline silicon wafers made for solar cell production, which contain a detectible amount of unwanted iron. The iron images on these wafers reveal internal gettering of iron to grain boundaries and dislocated regions during ingot growth.D.M. is supported by an Australian Research Council QEII Fellowship. The Centre of Excellence for Advanced Silicon Photovoltaics and Photonics at UNSW is funded by the Australian Research Council

Multi-class protein fold classification using a new ensemble machine learning approach.

Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recent structural genomics initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. The amount of structural data has made traditional methods such as manual inspection of the protein structure become impossible. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. This work proposes a novel ensemble machine learning method that improves the coverage of the classifiers under the multi-class imbalanced sample sets by integrating knowledge induced from different base classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have compared our approach with PART and show that our method improves the sensitivity of the classifier in protein fold classification. Furthermore, we have extended this method to learning over multiple data types, preserving the independence of their corresponding data sources, and show that our new approach performs at least as well as the traditional technique over a single joined data source. These experimental results are encouraging, and can be applied to other bioinformatics problems similarly characterised by multi-class imbalanced data sets held in multiple data sources

Periodic subvarieties of a projective variety under the action of a maximal rank abelian group of positive entropy

We determine positive-dimensional G-periodic proper subvarieties of an n-dimensional normal projective variety X under the action of an abelian group G of maximal rank n-1 and of positive entropy. The motivation of the paper is to understand the obstruction for X to be G-equivariant birational to the quotient variety of an abelian variety modulo the action of a finite group.Comment: Asian Journal of Mathematics (to appear), Special issue on the occasion of Prof N. Mok's 60th birthda

Seismological support for the metastable superplume model, sharp features, and phase changes within the lower mantle

Recently, a metastable thermal-chemical convection model was proposed to explain the African Superplume. Its bulk tabular shape remains relatively stable while its interior undergoes significant stirring with low-velocity conduits along its edges and down-welling near the middle. Here, we perform a mapping of chemistry and temperature into P and S velocity variations and replace a seismically derived structure with this hybrid model. Synthetic seismogram sections generated for this 2D model are then compared directly with corresponding seismic observations of P (P, PCP, and PKP) and S (S, SCS, and SKS) phases. These results explain the anticorrelation between the bulk velocity and shear velocity and the sharpness and level of SKS travel time delays. In addition, we present evidence for the existence of a D" triplication (a putative phase change) beneath the down-welling structure

A new art code for tomographic interferometry

A new algebraic reconstruction technique (ART) code based on the iterative refinement method of least squares solution for tomographic reconstruction is presented. Accuracy and the convergence of the technique is evaluated through the application of numerically generated interferometric data. It was found that, in general, the accuracy of the results was superior to other reported techniques. The iterative method unconditionally converged to a solution for which the residual was minimum. The effects of increased data were studied. The inversion error was found to be a function of the input data error only. The convergence rate, on the other hand, was affected by all three parameters. Finally, the technique was applied to experimental data, and the results are reported