Machine learning techniques for galaxy imagery and photometry

Abstract

In 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.