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Machine Learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to…

In recent years, machine learning (ML) algorithms have been successfully employed in Astronomy for analyzing and interpreting the data collected from various surveys. The need for new robust and efficient data analysis tools in Astronomy is…

In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmological discovery, but will also present new challenges for…

The rapid advancement of observational capabilities in astronomy has led to an exponential growth in the volume of light curve (LC) data, creating both opportunities and challenges for time-domain astronomy. Traditional analytical methods…

Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…

The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more powerful and accessible. As the field of ML enjoys a…

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…

Machine learning is a field that has been growing in importance since the early 2010s due to the increasing accuracy of classification models and hardware advances that have enabled faster training on large datasets. In the field of…

In recent decades, artificial intelligence (AI) including machine learning (ML) have become vital for space missions enabling rapid data processing, advanced pattern recognition, and enhanced insight extraction. These tools are especially…

Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from…

Machine learning techniques are utilised in several areas of astrophysical research today. This dissertation addresses the application of ML techniques to two classes of problems in astrophysics, namely, the analysis of individual…

We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…

We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early…

Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…

Astronomical observations already produce vast amounts of data through a new generation of telescopes that cannot be analyzed manually. Next-generation telescopes such as the Large Synoptic Survey Telescope and the Square Kilometer Array…

Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to…

Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…

Machine Learning methods will play a fundamental role in our ability to optimize the science output from the next generation of large scale surveys. Given the peculiarities of astronomical data, it is crucial that algorithms are adapted to…

We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from quasars. We…

The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets. Recent advances in machine learning (ML) provide…