Author: Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
The Applications of Machine Learning on Searching Exoplanets Through Transit and Direct-Imaging Methods
Author: Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Exoplanet Science Strategy
Author: National Academies of Sciences, Engineering, and MedicinePublisher: National Academies Press
ISBN: 030947941X
Category : Science
Languages : en
Pages : 187
Book Description
The past decade has delivered remarkable discoveries in the study of exoplanets. Hand-in-hand with these advances, a theoretical understanding of the myriad of processes that dictate the formation and evolution of planets has matured, spurred on by the avalanche of unexpected discoveries. Appreciation of the factors that make a planet hospitable to life has grown in sophistication, as has understanding of the context for biosignatures, the remotely detectable aspects of a planet's atmosphere or surface that reveal the presence of life. Exoplanet Science Strategy highlights strategic priorities for large, coordinated efforts that will support the scientific goals of the broad exoplanet science community. This report outlines a strategic plan that will answer lingering questions through a combination of large, ambitious community-supported efforts and support for diverse, creative, community-driven investigator research.
Novel Applications of Machine Learning in Astronomy and Beyond
Author: Ben HenghesPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
The field of astronomy is currently experiencing a period of unprecedented expansion, predominantly brought about by the vast amounts of data being produced by the latest telescopes and surveys. New methods will be required to have any hope of being able to analyse the data collected, the most widespread of which is machine learning. Machine learning has evolved rapidly over the past decade in an attempt to match the rate of increasing data, and aided by advancements in computer hardware, analyses that would have been impossible in the past are now common place on astronomers' laptops. However, despite machine learning becoming a favourite tool for many, there is often little consideration for which algorithms are best suited for the job. In this thesis, machine learning is implemented in a variety of different problems ranging from Solar System science and searching for Trans-Neptunian Objects (TNOs), to the cosmological problem of obtaining accurate photometric redshift (photo-z) estimations for distant galaxies. In chapter 2 I implement many different machine learning classifiers to aid the Dark Energy Survey's search for TNOs, comparing the classifiers to find the most suitable, and demonstrating how machine learning can provide significant increases in efficiency. In chapter 3 I implement machine learning algorithms to provide photo-z estimations for a million galaxies, using the method as an example for how it is possible to benchmark machine learning algorithms to provide information about the scalibility of different methods. In chapter 4 I expand upon the benchmarking of methods developed for obtaining photo-z estimates, applying them instead to deep learning algorithms which directly use image data, before discussing future work and concluding in chapter 5.
Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science
Author: Emma Torres ChicklesPublisher:
ISBN:
Category :
Languages : en
Pages : 72
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.
The Exoplanet Handbook
Author: Michael PerrymanPublisher: Cambridge University Press
ISBN: 1108329667
Category : Science
Languages : en
Pages : 973
Book Description
With the discovery of planets beyond our solar system 25 years ago, exoplanet research has expanded dramatically, with new state-of-the-art ground-based and space-based missions dedicated to their discovery and characterisation. With more than 3,500 exoplanets now known, the complexity of the discovery techniques, observations and physical characterisation have grown exponentially. This Handbook ties all these avenues of research together across a broad range of exoplanet science. Planet formation, exoplanet interiors and atmospheres, and habitability are discussed, providing in-depth coverage of our knowledge to date. Comprehensively updated from the first edition, it includes instrumental and observational developments, in-depth treatment of the new Kepler mission results and hot Jupiter atmospheric studies, and major updates on models of exoplanet formation. With extensive references to the research literature and appendices covering all individual exoplanet discoveries, it is a valuable reference to this exciting field for both incoming and established researchers.
Machine Learning for Small Bodies in the Solar System
Author: Valerio CarrubaPublisher: Elsevier
ISBN: 9780443247705
Category : Science
Languages : en
Pages : 0
Book Description
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.
Atmospheric Retrieval
Author: Michael D. HimesPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Atmospheric retrieval is the inverse modeling method where atmospheric properties are constrained based on measured spectra. Due to the low signal-to-noise ratios of exoplanet observations, exoplanetary retrieval codes pair a radiative transfer (RT) simulator with a Bayesian statistical framework in order to characterize the distribution of atmospheric parameters that could explain the observations (the posterior distribution). This requires on the order of 106 RT model evaluations, which requires hours to days of compute time depending on model complexity. In this work, I investigate atmospheric retrieval methods and apply them to observations of hot Jupiters. Chapter 2 presents a set of RT and retrieval tests to validate the Bayesian Atmospheric Radiative Transfer (BART) retrieval code and applies BART to the emission spectrum of HD 189733 b. Chapter 3 investigates the dayside atmosphere of WASP-12b and resolves a tension in the literature over its composition. Chapter 4 introduces a machine learning direct retrieval framework which spawns virtual machines, generates spectra, trains neural networks, and performs atmospheric retrievals using trained neural networks. Chapter 5 builds on this and presents a machine learning indirect retrieval method, where the retrieval is performed using a neural network surrogate model for RT within a Bayesian framework, and compares it with BART. Chapter 6 utilizes the neural network surrogate modeling approach for thermochemical equilibrium chemistry models and compares it with other equilibrium estimation methods. Appendices address retrieval errors induced by choice of wavenumber gridding for opacity-sampling RT schemes, neural network model selection, the effects of data set size on neural network training, and the accuracy of Bayesian frameworks used for atmospheric retrieval.
Transiting Exoplanets
Author: Carole A. HaswellPublisher: Cambridge University Press
ISBN: 9780521191838
Category : Science
Languages : en
Pages : 344
Book Description
The methods used in the detection and characterisation of exoplanets are presented in this unique textbook for advanced undergraduates.
New Worlds, New Horizons in Astronomy and Astrophysics
Author: National Research CouncilPublisher: National Academies Press
ISBN: 0309157994
Category : Science
Languages : en
Pages : 324
Book Description
Driven by discoveries, and enabled by leaps in technology and imagination, our understanding of the universe has changed dramatically during the course of the last few decades. The fields of astronomy and astrophysics are making new connections to physics, chemistry, biology, and computer science. Based on a broad and comprehensive survey of scientific opportunities, infrastructure, and organization in a national and international context, New Worlds, New Horizons in Astronomy and Astrophysics outlines a plan for ground- and space- based astronomy and astrophysics for the decade of the 2010's. Realizing these scientific opportunities is contingent upon maintaining and strengthening the foundations of the research enterprise including technological development, theory, computation and data handling, laboratory experiments, and human resources. New Worlds, New Horizons in Astronomy and Astrophysics proposes enhancing innovative but moderate-cost programs in space and on the ground that will enable the community to respond rapidly and flexibly to new scientific discoveries. The book recommends beginning construction on survey telescopes in space and on the ground to investigate the nature of dark energy, as well as the next generation of large ground-based giant optical telescopes and a new class of space-based gravitational observatory to observe the merging of distant black holes and precisely test theories of gravity. New Worlds, New Horizons in Astronomy and Astrophysics recommends a balanced and executable program that will support research surrounding the most profound questions about the cosmos. The discoveries ahead will facilitate the search for habitable planets, shed light on dark energy and dark matter, and aid our understanding of the history of the universe and how the earliest stars and galaxies formed. The book is a useful resource for agencies supporting the field of astronomy and astrophysics, the Congressional committees with jurisdiction over those agencies, the scientific community, and the public.
Applications of Machine Learning to Gravitational Waves
Author: Ondřej ZelenkaPublisher:
ISBN:
Category :
Languages : de
Pages : 0
Book Description
Gravitational waves, predicted by Albert Einstein in 1916 and first directly observed in 2015, are a powerful window into the universe, and its past. Currently, multiple detectors around the globe are in operation. While the technology has matured to a point where detections are common, there are still unsolved problems. Traditional search algorithms are only optimal under assumptions which do not hold in contemporary detectors. In addition, high data rates and latency requirements can be challenging. In this thesis, we use new methods based on recent advancements in machine learning to tackle these issues. We develop search algorithms competitive with conventional methods in a realistic setting. In doing so, we cover a mock data challenge which we have organized, and which served as a framework to obtain some of these results. Finally, we demonstrate the power of our search algorithms by applying them to data from the second half of LIGO's third observing run. We find that the events targeted by our searches are identified reliably.
