Machine Learning For Small Bodies In The Solar System

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Machine Learning for Small Bodies in the Solar System

Author : Valerio Carruba
Publisher : Elsevier
ISBN 13 : 9780443247705
Total Pages : 0 pages
Book Rating : 4.2/5 (477 download)

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Book Synopsis Machine Learning for Small Bodies in the Solar System by : Valerio Carruba

Download or read book Machine Learning for Small Bodies in the Solar System written by Valerio Carruba and published by Elsevier. This book was released on 2025-01-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Small Bodies in the Solar System provides the latest developments and methods in applications of Machine Learning (ML) and Artificial Intelligence (AI) to different aspects of Solar System bodies, including dynamics, physical properties, detection algorithms, etc. Allowing readers to apply ML and AI to the study of asteroids, comets, moons, and Trans-Neptunian Objects. The practical approach encompasses a wide range of topics, providing both experienced and novice researchers with essential tools and insights. The inclusion of codes and links to publicly available repositories further facilitates hands-on learning, enabling readers to put their newfound knowledge into practice. Machine Learning for Small Bodies in the Solar System serves as an invaluable reference for researchers working into the broad fields of Solar System bodies; both seasoned researchers seeking to enhance their understanding of ML and AI in the context of Solar System exploration or those just stepping into the field looking for direction on Methodologies and techniques to apply ML and AI methodologies.

Solar System Small-body Demographics with the Palomar Transient Factory Survey

Author : Adam Waszczak
Publisher :
ISBN 13 :
Total Pages : 520 pages
Book Rating : 4.:/5 (919 download)

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Book Synopsis Solar System Small-body Demographics with the Palomar Transient Factory Survey by : Adam Waszczak

Download or read book Solar System Small-body Demographics with the Palomar Transient Factory Survey written by Adam Waszczak and published by . This book was released on 2015 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Observational studies of our solar system's small-body populations (asteroids and comets) offer insight into the history of our planetary system, as these minor planets represent the left-over building blocks from its formation. The Palomar Transient Factory (PTF) survey began in 2009 as the latest wide-field sky-survey program to be conducted on the 1.2-meter Samuel Oschin telescope at Palomar Observatory. Though its main science program has been the discovery of high-energy extragalactic sources (such as supernovae), during its first five years PTF has collected nearly five million observations of over half a million unique solar system small bodies. This thesis begins to analyze this vast data set to address key population-level science topics, including: the detection rates of rare main-belt comets and small near-Earth asteroids, the spin and shape properties of asteroids as inferred from their lightcurves, the applicability of this visible light data to the interpretation of ultraviolet asteroid observations, and a comparison of the physical properties of main-belt and Jovian Trojan asteroids. Future sky-surveys would benefit from application of the analytical techniques presented herein, which include novel modeling methods and unique applications of machine-learning classification. The PTF asteroid small-body data produced in the course of this thesis work should remain a fertile source of solar system science and discovery for years to come.

Advances in Machine Learning and Data Mining for Astronomy

Author : Michael J. Way
Publisher : CRC Press
ISBN 13 : 143984173X
Total Pages : 746 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Advances in Machine Learning and Data Mining for Astronomy by : Michael J. Way

Download or read book Advances in Machine Learning and Data Mining for Astronomy written by Michael J. Way and published by CRC Press. This book was released on 2012-03-29 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Efficient Algorithms for Modeling Close-encounters of the Solar System

Author : Shafiq Ur Rehman
Publisher :
ISBN 13 :
Total Pages : 262 pages
Book Rating : 4.:/5 (915 download)

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Book Synopsis Efficient Algorithms for Modeling Close-encounters of the Solar System by : Shafiq Ur Rehman

Download or read book Efficient Algorithms for Modeling Close-encounters of the Solar System written by Shafiq Ur Rehman and published by . This book was released on 2013 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: N-body simulations are used extensively to model the dynamical evolution of the Solar System. Many of these simulations have a small number of massive bodies representing the Sun and planets, and a large number of test particles representing massless bodies, such as asteroids and comets. Massive bodies act upon one another and the massless bodies, but the massless bodies do not interact with each other. The small bodies can come very close to a planet, an event known as a close-encounter, and even hit the planet. A small body is usually removed from the simulation if it makes a close-encounter with a massive body or is ejected from the Solar System. A numerical scheme for performing simulations of massive and massless bodies consists of at least four essential parts: the integration of the massive bodies, the evaluation of the positions of the massive bodies using an interpolating polynomial, the integration of the massless bodies, and the detection of close-encounters. We present and illustrate a general multirate algorithm. The performance of the algorithm is illustrated by numerical tests that involve comparing combinations of different integrators and interpolation schemes on a collection of test problems for short and long intervals of integration. We also investigate the growth of the global error in the positions and velocities of the bodies, and the relative error in the energy and angular momentum of the system. In addition, we investigate the close-encounter errors in time, positions and velocities of the bodies.

Machine Learning for Planetary Science

Author : Joern Helbert
Publisher : Elsevier
ISBN 13 : 0128187220
Total Pages : 234 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Machine Learning for Planetary Science by : Joern Helbert

Download or read book Machine Learning for Planetary Science written by Joern Helbert and published by Elsevier. This book was released on 2022-03-22 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

The Use of Artificial Intelligence for Space Applications

Author : Cosimo Ieracitano
Publisher : Springer Nature
ISBN 13 : 3031257553
Total Pages : 444 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis The Use of Artificial Intelligence for Space Applications by : Cosimo Ieracitano

Download or read book The Use of Artificial Intelligence for Space Applications written by Cosimo Ieracitano and published by Springer Nature. This book was released on 2023-06-16 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an ideal and practical resource on the potential impact Artificial Intelligence (AI) can have in space sciences and applications. AI for Space Application presents a hands-on approach to browse in the subject and to learning how to do. AI is not yet fully accepted as a pervasive technology in space applications because they are often mission-critical and the cost of space equipment and modules raises skepticism on any practical use and reliability. However, it is evident that its potential impact on many aspects is dramatic. Starting from either actual or experimental realizations, the book accompanies the reader through such fascinating subjects like space exploration, autonomous navigation and landing, rover control and guidance on rough surfaces, image analysis automation for planet or star classification, and for space debris avoidance without human intervention. This kind of approach may facilitate further investigations on the same or similar subjects, as the future of space explorations is going toward adopting AI. The intended audience of the book are researchers from academia and space industries and practitioners in related start-ups.

Machine Learning on Mars

Author : Hannah Rae Kerner
Publisher :
ISBN 13 :
Total Pages : 269 pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Machine Learning on Mars by : Hannah Rae Kerner

Download or read book Machine Learning on Mars written by Hannah Rae Kerner and published by . This book was released on 2019 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists to analyze these complex data at the desired pace. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and prioritize the most promising, novel, or relevant observations for scientific analysis. Machine learning methods can serve this need in a variety of ways: by uncovering patterns or features of interest in large, complex datasets that are difficult for humans to analyze; by inspiring new hypotheses based on structure and patterns revealed in data; or by automating tedious or time-consuming tasks. In this dissertation, I present machine learning solutions to enhance the tactical planning process for the Mars Science Laboratory Curiosity rover and future tactically-planned missions, as well as the science analysis process for archived and ongoing orbital imaging investigations such as the High Resolution Imaging Science Experiment (HiRISE) at Mars. These include detecting novel geology in multispectral images and active nuclear spectroscopy data, analyzing the intrinsic variability in active nuclear spectroscopy data with respect to elemental geochemistry, automating tedious image review processes, and monitoring changes in surface features such as impact craters in orbital remote sensing images. Collectively, this dissertation shows how machine learning can be a powerful tool for facilitating scientific discovery during active exploration missions and in retrospective analysis of archived data.

Proceedings of the 44th Annual American Astronautical Society Guidance, Navigation, and Control Conference, 2022

Author : Matt Sandnas
Publisher : Springer Nature
ISBN 13 : 3031519280
Total Pages : 1810 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Proceedings of the 44th Annual American Astronautical Society Guidance, Navigation, and Control Conference, 2022 by : Matt Sandnas

Download or read book Proceedings of the 44th Annual American Astronautical Society Guidance, Navigation, and Control Conference, 2022 written by Matt Sandnas and published by Springer Nature. This book was released on 2024 with total page 1810 pages. Available in PDF, EPUB and Kindle. Book excerpt: Zusammenfassung: This conference attracts GN&C specialists from across the globe. The 2022 Conference was the 44th Annual GN&C conference with more than 230 attendees from six different countries with 44 companies and 28 universities represented. The conference presented more than 100 presentations and 16 posters across 18 topics. This year, the planning committee wanted to continue a focus on networking and collaboration hoping to inspire innovation through the intersection of diverse ideas. These proceedings present the relevant topics of the day while keeping our more popular and well-attended sessions as cornerstones from year to year. Several new topics including "Autonomous Control of Multiple Vehicles" and "Results and Experiences from OSIRIS-REx" were directly influenced by advancements in our industry. In the end, the 44th Annual GN&C conference became a timely reflection of the current state of the GN&C ins the space industry. The annual American Astronautical Society Rocky Mountain Guidance, Navigation and Control (GN&C) Conference began 1977 as an informal exchange of ideas and reports of achievements among guidance and control specialists local to the Colorado area. Bud Gates, Don Parsons, and Bob Culp organized the first conference, and began the annual series of meetings the following winter. In March 1978, the First Annual Rocky Mountain Guidance and Control Conference met at Keystone, Colorado. It met there for eighteen years, moving to Breckenridge in 1996 where it has been for over 25 years

Orbital Dynamics in the Gravitational Field of Small Bodies

Author : Yang Yu
Publisher : Springer
ISBN 13 : 366252693X
Total Pages : 136 pages
Book Rating : 4.6/5 (625 download)

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Book Synopsis Orbital Dynamics in the Gravitational Field of Small Bodies by : Yang Yu

Download or read book Orbital Dynamics in the Gravitational Field of Small Bodies written by Yang Yu and published by Springer. This book was released on 2016-05-23 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: This prizewinning PhD thesis presents a general discussion of the orbital motion close to solar system small bodies (SSSBs), which induce non-central asymmetric gravitational fields in their neighborhoods. It introduces the methods of qualitative theory in nonlinear dynamics to the study of local/global behaviors around SSSBs. Detailed mechanical models are employed throughout this dissertation, and specific numeric techniques are developed to compensate for the difficulties of directly analyzing. Applying this method, several target systems, like asteroid 216 Kleopatra, are explored in great detail, and the results prove to be both revealing and pervasive for a large group of SSSBs.

An Astrobiology Strategy for the Search for Life in the Universe

Author : National Academies of Sciences, Engineering, and Medicine
Publisher : National Academies Press
ISBN 13 : 0309484197
Total Pages : 189 pages
Book Rating : 4.3/5 (94 download)

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Book Synopsis An Astrobiology Strategy for the Search for Life in the Universe by : National Academies of Sciences, Engineering, and Medicine

Download or read book An Astrobiology Strategy for the Search for Life in the Universe written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-03-20 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: Astrobiology is the study of the origin, evolution, distribution, and future of life in the universe. It is an inherently interdisciplinary field that encompasses astronomy, biology, geology, heliophysics, and planetary science, including complementary laboratory activities and field studies conducted in a wide range of terrestrial environments. Combining inherent scientific interest and public appeal, the search for life in the solar system and beyond provides a scientific rationale for many current and future activities carried out by the National Aeronautics and Science Administration (NASA) and other national and international agencies and organizations. Requested by NASA, this study offers a science strategy for astrobiology that outlines key scientific questions, identifies the most promising research in the field, and indicates the extent to which the mission priorities in existing decadal surveys address the search for life's origin, evolution, distribution, and future in the universe. This report makes recommendations for advancing the research, obtaining the measurements, and realizing NASA's goal to search for signs of life in the universe.

Measurements and Instrumentation for Machine Vision

Author : Oleg Sergiyenko
Publisher : CRC Press
ISBN 13 : 1040042988
Total Pages : 466 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Measurements and Instrumentation for Machine Vision by : Oleg Sergiyenko

Download or read book Measurements and Instrumentation for Machine Vision written by Oleg Sergiyenko and published by CRC Press. This book was released on 2024-06-26 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive reference book that addresses the field of machine vision and its significance in cyber-physical systems. It explores the multidisciplinary nature of machine vision, involving electronic and mechatronic devices, artificial intelligence algorithms, embedded systems, control systems, robotics, interconnectivity, data science, and cloud computing. The book aims to provide advanced students, early career researchers, and established scholars with state-of-the-art knowledge and novel content related to the implementation of machine vision in engineering, scientific knowledge, and technological innovation. The chapters of the book delve into various topics and applications within the realm of machine vision. They cover areas such as camera and inertial measurement unit calibration, technical vision systems for human detection, design and evaluation of support systems using neural networks, UV sensing in contemporary applications, fiber Bragg grating arrays for medical diagnosis, color model creation for terrain recognition by robots, navigation systems for aircraft, object classification in infrared images, feature selection for vehicle/non-vehicle classification, visualization of sedimentation in extreme conditions, quality estimation of tea using machine vision, image dataset augmentation techniques, machine vision for astronomical images, agricultural automation, occlusion-aware disparity-based visual servoing, machine learning approaches for single-photon imaging, and augmented visual inertial wheel odometry. Each chapter is a result of expert research and collaboration, reviewed by peers and consulted by the book's editorial board. The authors provide in-depth reviews of the state of the art and present novel proposals, contributing to the development and futurist trends in the field of machine vision. "Measurements and Instrumentation for Machine Vision" serves as a valuable resource for researchers, students, and professionals seeking to explore and implement machine vision technologies in various domains, promoting sustainability, human-centered solutions, and global problem-solving.

Artificial Intelligence For Science: A Deep Learning Revolution

Author : Alok Choudhary
Publisher : World Scientific
ISBN 13 : 9811265682
Total Pages : 803 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Artificial Intelligence For Science: A Deep Learning Revolution by : Alok Choudhary

Download or read book Artificial Intelligence For Science: A Deep Learning Revolution written by Alok Choudhary and published by World Scientific. This book was released on 2023-03-21 with total page 803 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.

Physical and Dynamical Investigations of Small Bodies of the Solar System

Author : Simone Marchi
Publisher :
ISBN 13 :
Total Pages : 143 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Physical and Dynamical Investigations of Small Bodies of the Solar System by : Simone Marchi

Download or read book Physical and Dynamical Investigations of Small Bodies of the Solar System written by Simone Marchi and published by . This book was released on 2003 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Learning in Solar Astronomy

Author : Long Xu
Publisher : Springer Nature
ISBN 13 : 9811927464
Total Pages : 103 pages
Book Rating : 4.8/5 (119 download)

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Book Synopsis Deep Learning in Solar Astronomy by : Long Xu

Download or read book Deep Learning in Solar Astronomy written by Long Xu and published by Springer Nature. This book was released on 2022-05-27 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Exploration of Small Bodies in the Solar System

Author : P. Lamy
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (593 download)

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Book Synopsis Exploration of Small Bodies in the Solar System by : P. Lamy

Download or read book Exploration of Small Bodies in the Solar System written by P. Lamy and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Planets and Satellites

Author : Gerard Peter Kuiper
Publisher :
ISBN 13 :
Total Pages : 601 pages
Book Rating : 4.:/5 (54 download)

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Book Synopsis Planets and Satellites by : Gerard Peter Kuiper

Download or read book Planets and Satellites written by Gerard Peter Kuiper and published by . This book was released on 1961 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Handbook of Evolutionary Machine Learning

Author : Wolfgang Banzhaf
Publisher : Springer Nature
ISBN 13 : 9819938147
Total Pages : 764 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Handbook of Evolutionary Machine Learning by : Wolfgang Banzhaf

Download or read book Handbook of Evolutionary Machine Learning written by Wolfgang Banzhaf and published by Springer Nature. This book was released on 2023-11-01 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.