Discriminating between a Stochastic Gravitational Wave Background and Instrument Noise

The detection of a stochastic background of gravitational waves could significantly impact our understanding of the physical processes that shaped the early Universe. The challenge lies in separating the cosmological signal from other stochastic processes such as instrument noise and astrophysical foregrounds. One approach is to build two or more detectors and cross correlate their output, thereby enhancing the common gravitational wave signal relative to the uncorrelated instrument noise. When only one detector is available, as will likely be the case with the Laser Interferometer Space Antenna (LISA), alternative analysis techniques must be developed. Here we show that models of the noise and signal transfer functions can be used to tease apart the gravitational and instrument noise contributions. We discuss the role of gravitational wave insensitive "null channels" formed from particular combinations of the time delay interferometry, and derive a new combination that maintains this insensitivity for unequal arm length detectors. We show that, in the absence of astrophysical foregrounds, LISA could detect signals with energy densities as low as $\Omega_{\rm gw} = 6 \times 10^{-13}$ with just one month of data. We describe an end-to-end Bayesian analysis pipeline that is able to search for, characterize and assign confidence levels for the detection of a stochastic gravitational wave background, and demonstrate the effectiveness of this approach using simulated data from the third round of Mock LISA Data Challenges.Comment: 10 Pages, 10 Figure

Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints

The self-assembly of low molecular weight gelators to form gels has enormous potential for cell culturing, optoelectronics, sensing, and for the preparation of structured materials. There is an enormous “chemical space” of gelators. Even within one class, functionalised dipeptides, there are many structures based on both natural and unnatural amino acids that can be proposed and there is a need for methods that can successfully predict the gelation propensity of such molecules. We have successfully developed computational models, based on experimental data, which are robust and are able to identify in silico dipeptide structures that can form gels. A virtual computational screen of 2025 dipeptide candidates identified 9 dipeptides that were synthesised and tested. Every one of the 9 dipeptides synthesised and tested were correctly predicted for their gelation properties. This approach and set of tools enables the “dipeptide space” to be searched effectively and efficiently in order to deliver novel gelator molecules

Astrophysical Model Selection in Gravitational Wave Astronomy

Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%

SBSI:an extensible distributed software infrastructure for parameter estimation in systems biology

Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI’s use of standard data formats

Functional tests of the competitive exclusion hypothesis for multituberculate extinction

Multituberculate mammals thrived during the Mesozoic, but their diversity declined from the mid-late Paleocene onwards, becoming extinct in the late Eocene. The radiation of superficially similar, eutherian rodents has been linked to multituberculate extinction through competitive exclusion. However, characteristics providing rodents with a supposed competitive advantage are currently unknown and comparative functional tests between the two groups are lacking. Here, a multifaceted approach to craniomandibular biomechanics was taken to test the hypothesis that superior skull function made rodents more effective competitors. Digital models of the skulls of four extant rodents and the Upper Cretaceous multituberculate Kryptobaatar were constructed and used (i) in finite-element analysis to study feeding-induced stresses, (ii) to calculate metrics of bite force production and (iii) to determine mechanical resistances to bending and torsional forces. Rodents exhibit higher craniomandibular stresses and lower resistances to bending and torsion than the multituberculate, apparently refuting the competitive exclusion hypothesis. However, rodents optimize bite force production at the expense of higher skull stress and we argue that this is likely to have been more functionally and selectively important. Our results therefore provide the first functional lines of evidence for potential reasons behind the decline of multituberculates in the changing environments of the Paleogene.Peer reviewe

Optimization of input parameters to a CN neuron model to simulate its activity during and between epileptic absence seizures

Peer reviewe