Intelligent Astrophysics

During the development of humankind and the knowledge of our world, a large amount of information was produced. This information of a scientific as well as a technical nature then again contributed to the further development of our civilization. The amount of data that man has produced during scientific development increases exponentially with time. In the last five years, humankind has produced more information and data than in all its previous existential development. With increasing data, there is a need to have methods that can effectively process them and present the data to humans. Processing here means cleaning from noise, searching for useful information, visualization and the like. In the last hundred years, with the development of computers and information science, efficient algorithms have begun to emerge, which today belong to the so-called artificial intelligence and which are able to perform just such tasks. The first has been used classical algorithms for filtering, compression, dimension reduction, and many other tasks. Later, another algorithms enrich this class, which today belong to machine learning, which is basically part of artificial intelligence, and with the help of these methods and algorithms, we can process very large data in real time, which is important for science as such. The same goes for technology and society. Areas, where these methods are most needed, include physics and astrophysics. In physics, let us mention the CERN accelerator in Geneva, Switzerland, where experiments produce an enormous amount of data in fractions of a second. All this data must be processed precisely. Another area is in astrophysics, which, thanks to high robotics and automation, has become an area that is literally flooding us with by data. Today, it is a common fact and the fact that robotic telescopes spew up petabytes of data in one night. If we realize that at the time of Johannes Kepler’s discovery, about 400 Kb of data was enough to discover the famous Kepler laws, it is clear that many discoveries can be hidden in these petabytes, which can literally flow between our fingers. Therefore, especially in astrophysics, all-important methods in the field of machine learning are very effectively applied, most notably neural networks, various filtering algorithms and recently it has been shown that evolutionary algorithms are also a very capable tool for processing such data and their possible modeling. At first glance, the title “Intelligent Astrophysics” may sound strange, because the “intelligent” term could be wrongly misrepresented, implying as the wanting to consider “unintelligent” all previous research in Astrophysics. However, this is not the case. Intelligence is the ability to adapt to the surrounding environment and conditions. In this sense, the rise of Big Data paradigm and the advent of multi-messenger astrophysics pose Astronomy & Astrophysics (A&A) in a new perspective, as becoming de facto a Big Data Science, thus requiring more efficient solutions. Traditional research in the A&A field is now objectively considered unable to allow scientists to fully exploit all information inherited within observed data in a reasonable time. In particular, astronomical data are represented in a multi-D parameter space, where hidden correlations, anomalies, and peculiarities are very complex to find and to visualize. Since two decades, Astroinformatics, or “intelligent Astrophysics”, as being the virtuous synergy between A&A and Data Science, gained always increasing popularity and interest, enclosing as main features the multi-disciplinarity approach (statistics, machine learning, data mining informatics, image analysis, visualization, and astrophysics as well), parameter space exploration and optimization, anomaly detection as well as serendipity. It pushed academic and educational institutions to open and activate dedicated positions and courses and its bibliometric parameters are exponentially increasing. The present book discusses the application of these methods to astrophysical data from different perspectives. In this publication, the reader will encounter interesting chapters that discuss data processing and pulsars, the complexity and information content of our universe, the use of tessellation in astronomy, characterization and classification of astronomical phenomena, identification of extragalactic objects, classification of pulsars and many other interesting chapters. The authors of these chapters are experts in their field and have been carefully selected to create this book so that we can present to the community a representative publication that shows a unique fusion of artificial intelligence and astrophysics. Of course, this book is not a complete cookbook covering the whole topic in the broadest sense, but rather an inspiring book. The aim of which is to motivate and inspire readers to their own experiments in the intersection of these two disciplines. We hope that this book will be exciting and beneficial for readers and that this book will enrich the issue. Editors