About this Research Topic
With the development and application of space- and ground-based telescopes, astronomical data experience rapid growth in size and complexity. They are characterized by the large volume, high dimensionality, multi-wavelength, default value, time series, high velocity, different venues, and so on. Astronomy enters the era of Big Data. How to collect, save, transfer, handle, mine, analyze such huge data measured by TB, PB, even larger is a hot issue, which depends on the newly developed technologies (databases, cloud storage, cloud computation, high-performance computation, machine learning, deep learning, artificial intelligence, etc.). How to extract useful information and knowledge from Big Data is a big challenge. In these situations, new disciplines of astrostatistics and astroinformatics appear to solve big data problems. Astronomers never stop developing automated and effective tools to suit the requirement of Big Data. In recent years, machine learning has become popular among astronomers and is now used for solving various tasks, for example, classification, regression, clustering, outlier detection, time series analysis, association rule, etc.
One of the aims of this Research Topic is to discuss recent developments of astrostatistics. We also aim to critically review the most promising research advances in machine learning technologies, which may have a significant impact on the scientific output of future ground and space projects.
This Research Topic invites both Review and Original Research articles which address different aspects of Machine Learning in Astronomy such as:
• Data integration from different databases
• Machine learning
• Deep learning
• Classification and regression.
Keywords: Machine learning, Deep learning, Photometric redshift, Classification, Regression, Data mining, Big Data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.