Related papers: Machine Learning in Astronomy: a practical overvie…
Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised,…
We summarize our exploratory investigation into whether Machine Learning (ML) techniques applied to publicly available professional text can substantially augment strategic planning for astronomy. We find that an approach based on Latent…
In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To…
Astronomy is entering a new era of discovery, coincident with the establishment of new facilities for observation and simulation that will routinely generate petabytes of data. While an increasing reliance on automated data analysis is…
This article reviews recent advances in the application of machine learning to weak-lensing cosmology. Weak gravitational lensing provides a unique and powerful probe of the total matter distribution in the Universe, independent of its…
It is developed a very complex system (hardware/software) to detect luminosity variations connected with the discovery of new planets outside the Solar System. Traditional imaging approaches are very demanding in terms of computing time;…
High-volume feature-rich data sets are becoming the bread-and-butter of 21st century astronomy but present significant challenges to scientific discovery. In particular, identifying scientifically significant relationships between sets of…
In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in…
Deep learning has recently become one of the most popular sub-fields of machine learning owing to its distributed data representation with multiple levels of abstraction. A diverse range of deep learning algorithms are being employed to…
In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial…
Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive…
Machine Learning for aviation weather is a growing area of research for providing low-cost alternatives for traditional, expensive weather sensors; however, in the area of atmospheric visibility estimation, publicly available datasets,…
This paper summarizes my thoughts, given in an invited review at the IAU symposium 341 "Challenges in Panchromatic Galaxy Modelling with Next Generation Facilities", about how machine learning methods can help us solve some of the big data…
Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are…
Agricultural research is essential for increasing food production to meet the requirements of an increasing population in the coming decades. Recently, satellite technology has been improving rapidly and deep learning has seen much success…
Rapid progress in the capabilities of machine learning approaches in natural language processing has culminated in the rise of large language models over the last two years. Recent works have shown unprecedented adoption of these for…
This paper outlines some of the possible advancements for the technosignatures searches using the new methods currently rapidly developing in computer science, such as machine learning and deep learning. It also showcases a couple of case…
This chapter reviews the application of Artificial Intelligence (AI) techniques to the study of galaxy clusters, covering both theoretical developments and their use as tools to infer cluster properties from a variety of observational…
The exploration and study of exoplanets remain at the frontier of astronomical research, challenging scientists to continuously innovate and refine methodologies to navigate the vast, complex data these celestial bodies produce. This…