Toward an understanding of the properties of neural network approaches for supernovae light curve approximation

The modern time-domain photometric surveys collect a lot of observations of various astronomical objects, and the coming era of large-scale surveys will provide even more information. Most of the objects have never received a spectroscopic follow-up, which is especially crucial for transients e.g. supernovae. In such cases, observed light curves could present an affordable alternative. Time series are actively used for photometric classification and characterization, such as peak and luminosity decline estimation. However, the collected time series are multidimensional, irregularly sampled, contain outliers, and do not have well-defined systematic uncertainties. Machine learning methods help extract useful information from available data in the most efficient way. We consider several light curve approximation methods based on neural networks: Multilayer Perceptrons, Bayesian Neural Networks, and Normalizing Flows, to approximate observations of a single light curve. Tests using both the simulated PLAsTiCC and real Zwicky Transient Facility data samples demonstrate that even few observations are enough to fit networks and achieve better approximation quality than other state-of-the-art methods. We show that the methods described in this work have better computational complexity and work faster than Gaussian Processes. We analyze the performance of the approximation techniques aiming to fill the gaps in the observations of the light curves, and show that the use of appropriate technique increases the accuracy of peak finding and supernova classification. In addition, the study results are organized in a Fulu Python library available on GitHub, which can be easily used by the community.Comment: Submitted to MNRAS. 14 pages, 6 figures, 9 table

THE GHOST BRIDE DANCE AND THE RESPONSIVE WILD DANCE, A SCENIC DESIGN

The purpose of this thesis is to provide research, supporting paperwork, productionphotographs, and other materials that document the scenic design process for the production of The Ghost Bride Dance and The Responsive Wild Dance by the Department of Theatre and Dance. This thesis contains the following: scenic research images collected to express the visual world of the dance and the emotional landscape to the production team; preliminary sketches; digital renderings of the scenic design; a full set of drafting plates and paint elevations used to communicate the design to the technical director and paint charge; a prop list and research book to detail each hand prop, set dressing, and consumable to the prop master; and finally archival production photographs to document the completed design

Underactuated mechanical systems: Whether orbital stabilization is an adequate assignment for a controller design?

The paper contributes to developing algorithms for motion planning and motion control for mechanical systems with two and more passive degrees of freedom by exploring a challenging example in details. As shown, some of arguments of motion planning methods developed for systems of underactuation degree one can be generalized for novel demanding settings, while corresponding arguments and concepts for controller design should be substantially reconsidered and updated. Rigorous theoretical results are well supported by numerical studies