Culture-as-bit: Culture and Cultural Evolution

This thesis uses ideas and techniques from information theory, graph theory, dynamic systems and anthropology to approach the issues of culture and cultural evolution. It argues for a more integrated approach to anthropological theory and advocates a more universal approach to issues of both biological and cultural evolution

A Relative Orbit Determination and Navigation Strategy for Lunar Gateway

With the Artemis program seeking to reestablish a human presence in lunar orbit and on the lunar surface, the planned lunar Gateway space station is to provide habitation services for crewed operations to and from the lunar surface. The lunar Gateway is to operate in cislunar space in what is known as a southern L2 near-rectilinear halo orbit, taking it quite close to the lunar surface at closest approach. This presents an opportunity for the collection of measurements with respect to the lunar surface on each pass, which may be useful in estimating the full orbital state of lunar Gateway. Given that the Deep Space Network is already over-encumbered, finding new methods for generating orbit determination solutions and subsequent navigation solutions is a valuable venture. This analysis provides methods for finding periodic orbits in the circular-restricted three-body problem, develops means for measuring these trajectories via range and Doppler measurements and terrain-relative measurements with respect to the surface. These measurements are then processed with nonlinear and linear estimators to generate an orbit determination solution which is used to plan and construct orbit maintenance maneuvers in accordance with a risk for trajectory divergence. An x-axis crossing control methodology is used to generate an orbit maintenance solution which maintains operations about a reference trajectory

Satellite laser ranging work at the Goddard Space Flight Center

Laser ranging systems, their range and accuracy capabilities, and planned improvements for future systems are discussed, the systems include one fixed and two mobile lasers ranging systems. They have demonstrated better than 10 cm accuracy both on a carefully surveyed ground range and in regular satellite ranging operations. They are capable of ranging to all currently launched retroreflector equipped satellites with the exception of Timation III. A third mobile system is discussed which will be accurate to better than 5 cm and will be capable of ranging to distant satellites such as Timation III and LAGEOS

Multivariate classification with random forests for gravitational wave searches of black hole binary coalescence

Searches for gravitational waves produced by coalescing black hole binaries with total masses ≳25  M_⊙ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass (≳25  M_⊙) parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown of binary black holes with total mass between 25  M_⊙ and 100  M_⊙, we find sensitive volume improvements as high as 70_(±13)%–109_(±11)% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between 10  M_⊙ and 600  M_⊙, we find sensitive volume improvements as high as 61_(±4)%–241_(±12)% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves

Comparative performance of some popular ANN algorithms on benchmark and function approximation problems

We report an inter-comparison of some popular algorithms within the artificial neural network domain (viz., Local search algorithms, global search algorithms, higher order algorithms and the hybrid algorithms) by applying them to the standard benchmarking problems like the IRIS data, XOR/N-Bit parity and Two Spiral. Apart from giving a brief description of these algorithms, the results obtained for the above benchmark problems are presented in the paper. The results suggest that while Levenberg-Marquardt algorithm yields the lowest RMS error for the N-bit Parity and the Two Spiral problems, Higher Order Neurons algorithm gives the best results for the IRIS data problem. The best results for the XOR problem are obtained with the Neuro Fuzzy algorithm. The above algorithms were also applied for solving several regression problems such as cos(x) and a few special functions like the Gamma function, the complimentary Error function and the upper tail cumulative $\chi^2$-distribution function. The results of these regression problems indicate that, among all the ANN algorithms used in the present study, Levenberg-Marquardt algorithm yields the best results. Keeping in view the highly non-linear behaviour and the wide dynamic range of these functions, it is suggested that these functions can be also considered as standard benchmark problems for function approximation using artificial neural networks.Comment: 18 pages 5 figures. Accepted in Pramana- Journal of Physic