Machine learning techniques for galaxy imagery and photometry

Doctor of PhilosophyDepartment of Computer ScienceMajor Professor Not ListedIn the past two decades, autonomous digital sky surveys have enabled significant advances in astronomy by collecting massive databases of imagery and other information. The quantity of data, coupled with the variety of scientific questions that require its analysis, makes manual analysis of these data impractical. To address this challenge, machine learning algorithms have been widely adopted for data analysis and product generation in astronomy. In this dissertation I examine the efficacy of machine learning algorithms such as deep convolutional neural networks, support vector machines, and vision transformers for the purpose of astronomical data analysis, with emphasize on extra-galactic objects. These include algorithms that can annotate large datasets of galaxy images, and their application to premier digital sky surveys such as Pan-STARRS. Specifically, I address the following research question: How effective are machine learning algorithms for annotating astronomical data, and what are the downsides of using these algorithms for this purpose? Namely, biases that are typical to machine learning systems can influence the annotations, which may consequently lead to false conclusions when applying statistical analysis to data annotated using such systems. These biases are often difficult to identify. Overall, this research highlights the importance of careful consideration of machine learning algorithms and their potential biases when applying them to astronomical data analysis. Our findings have broad implications for the use of machine learning in astronomy and other scientific domains, as they demonstrate the importance of addressing potential biases in machine learning systems to avoid erroneous scientific conclusions

Applied Mathematics to Mechanisms and Machines

This book brings together all 16 articles published in the Special Issue "Applied Mathematics to Mechanisms and Machines" of the MDPI Mathematics journal, in the section “Engineering Mathematics”. The subject matter covered by these works is varied, but they all have mechanisms as the object of study and mathematics as the basis of the methodology used. In fact, the synthesis, design and optimization of mechanisms, robotics, automotives, maintenance 4.0, machine vibrations, control, biomechanics and medical devices are among the topics covered in this book. This volume may be of interest to all who work in the field of mechanism and machine science and we hope that it will contribute to the development of both mechanical engineering and applied mathematics