Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science PDF Download
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Author: Emma Torres Chickles
Publisher:
ISBN:
Category :
Languages : en
Pages : 72
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Book Description
Large-scale astronomical surveys and planetary missions have produced huge amounts of data. The exponential growth in data volume has allowed the application of novel data science techniques, including machine learning. We use machine learning methods to analyze and extract new information from two enormous datasets: images of impact craters captured by the Mars Reconnaissance Orbiter (MRO) and time-series data collected by the Transiting Exoplanet Survey Satellite (TESS). Using images of impact craters captured by the MRO, we infer the spatial variation in the retention of ejecta deposits on Mars. We do this by training a convolutional neural network (CNN) to detect the presence of ejecta deposits around small craters. Our machine learning method to detect pre- served ejecta deposits will enable the study of the processes driving landscape evolution on Mars. In a methodologically relevant but independent study, we conduct a census of different types of variability of nearby stars using photometric time-series data produced by TESS. We do this by extracting representational features from light curves using a convolutional autoencoder and clustering these features. Our unsupervised machine learning method will accelerate the augmentation of variable star catalogues, which are essential for studies of stellar magnetism, stellar evolution, and the habitability of hosted exoplanets.
Author: Emma Torres Chickles
Publisher:
ISBN:
Category :
Languages : en
Pages : 72
Get Book Here
Book Description
Large-scale astronomical surveys and planetary missions have produced huge amounts of data. The exponential growth in data volume has allowed the application of novel data science techniques, including machine learning. We use machine learning methods to analyze and extract new information from two enormous datasets: images of impact craters captured by the Mars Reconnaissance Orbiter (MRO) and time-series data collected by the Transiting Exoplanet Survey Satellite (TESS). Using images of impact craters captured by the MRO, we infer the spatial variation in the retention of ejecta deposits on Mars. We do this by training a convolutional neural network (CNN) to detect the presence of ejecta deposits around small craters. Our machine learning method to detect pre- served ejecta deposits will enable the study of the processes driving landscape evolution on Mars. In a methodologically relevant but independent study, we conduct a census of different types of variability of nearby stars using photometric time-series data produced by TESS. We do this by extracting representational features from light curves using a convolutional autoencoder and clustering these features. Our unsupervised machine learning method will accelerate the augmentation of variable star catalogues, which are essential for studies of stellar magnetism, stellar evolution, and the habitability of hosted exoplanets.
Author: Joern Helbert
Publisher: Elsevier
ISBN: 0128187220
Category : Science
Languages : en
Pages : 234
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Book Description
Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. - Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials - Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets - Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems - Utilizes case studies to illustrate how machine learning methods can be employed in practice
Author: Petr Skoda
Publisher: Elsevier
ISBN: 0128191546
Category : Science
Languages : en
Pages : 472
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Book Description
Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields
Author: Long Xu
Publisher: Springer Nature
ISBN: 9811927464
Category : Science
Languages : en
Pages : 103
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Book Description
The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.
Author: Filomena Bufano
Publisher: Springer Nature
ISBN: 3031341678
Category : Science
Languages : en
Pages : 206
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Book Description
This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.
Author: Michael J. Way
Publisher: CRC Press
ISBN: 143984173X
Category : Computers
Languages : en
Pages : 746
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Book Description
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.
Author: Eija Laurikainen
Publisher: Springer
ISBN: 3319193783
Category : Science
Languages : en
Pages : 480
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Book Description
This book consists of invited reviews on Galactic Bulges written by experts in the field. A central point of the book is that, while in the standard picture of galaxy formation a significant amount of the baryonic mass is expected to reside in classical bulges, the question what is the fraction of galaxies with no classical bulges in the local Universe has remained open. The most spectacular example of a galaxy with no significant classical bulge is the Milky Way. The reviews of this book attempt to clarify the role of the various types of bulges during the mass build-up of galaxies, based on morphology, kinematics and stellar populations and connecting their properties at low and high redshifts. The observed properties are compared with the predictions of the theoretical models, accounting for the many physical processes leading to the central mass concentration and their destruction in galaxies. This book serves as an entry point for PhD students and non-specialists and as a reference work for researchers in the field.
Author: James R. Bates
Publisher:
ISBN:
Category : Apollo Lunar Surface Experiments Package
Languages : en
Pages : 184
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Book Description
Author: Bala Poduval
Publisher: Frontiers Media SA
ISBN: 2832520588
Category : Science
Languages : en
Pages : 203
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Book Description
Author: Phillip Chamberlin
Publisher: Springer Science & Business Media
ISBN: 1461436737
Category : Science
Languages : en
Pages : 405
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Book Description
This volume is dedicated to the Solar Dynamics Observatory (SDO), which was launched 11 February 2010. The articles focus on the spacecraft and its instruments: the Atmospheric Imaging Assembly (AIA), the Extreme Ultraviolet Variability Experiment (EVE), and the Helioseismic and Magnetic Imager (HMI). Articles within also describe calibration results and data processing pipelines that are critical to understanding the data and products, concluding with a description of the successful Education and Public Outreach activities. This book is geared towards anyone interested in using the unprecedented data from SDO, whether for fundamental heliophysics research, space weather modeling and forecasting, or educational purposes. Previously published in Solar Physics journal, Vol. 275/1-2, 2012. Selected articles in this book are published open access under a CC BY-NC 2.5 license at link.springer.com. For further details, please see the license information in the chapters.
