相关论文: Machine Learning Assisted NEO Discovery and Polari…

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…

In the past two decades an increasing interest in discovering Near Earth Objects has been noted in the astronomical community. Dedicated surveys have been operated for data acquisition and processing, resulting in the present discovery of…

Studies on Near Earth Asteroids (NEAs) throw light on discoveries, identification, orbit prediction and civil alert capabilities, including potential asteroid impact hazards. Due to various new observational programs, the discovery rate of…

We propose a new method for solving an important problem of astronomy that arises in observations with ultrahigh-angular-resolution interferometers. This method is based on the application of the theory of artificial neural networks. We…

Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep learning using neural networks in order to automate the…

Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic…

The survey of the nearby space and continuous monitoring of the Near Earth Objects (NEOs) and especially Near Earth Asteroids (NEAs) are essential for the future of our planet and should represent a priority for our solar system research…

The Near-Earth Object (NEO) Surveyor mission is a NASA observatory designed to discover and characterize near-Earth asteroids and comets. The mission's primary objective is to find the majority of objects large enough to cause severe…

Large-scale photometric surveys are revolutionizing astronomy by delivering unprecedented amounts of data. The rich data sets from missions such as the NASA Kepler and TESS satellites, and the upcoming ESA PLATO mission, are a treasure…

Near Earth Objects (NEOs) are small Solar System bodies whose orbits bring them close to the Earth's orbit. We are carrying out a Warm Spitzer Cycle 11 Exploration Science program entitled NEOSurvey --- a fast and efficient flux-limited…

AI and deep learning techniques are beginning to play an increasing role in astronomy as a necessary tool to deal with the data avalanche. Here we describe an application for finding resolved Planetary Nebulae (PNe) in crowded, wide-field,…

In the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an…

Las Cumbres Observatory (LCO) has deployed a network of ten identical 1-m telescopes to four locations. The global coverage and flexibility of the LCO network makes it ideal for discovery, follow-up, and characterization of all Solar System…

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify.…

Increasing our knowledge of the orbits and compositions of Near-Earth Objects (NEOs) is important for a better understanding of the evolution of the Solar System and of life. The detection of serendipitous NEO appearances among the millions…

We have begun the ExploreNEOs project in which we observe some 700 Near Earth Objects (NEOs) at 3.6 and 4.5 microns with the Spitzer Space Telescope in its Warm Spitzer mode. From these measurements and catalog optical photometry we derive…

Near Earth Asteroids (NEAs) and dead comets comprise the vast majority of the population of Near Earth Objects (NEOs) detected to date. Less is known of their physical properties than of the much larger population of main-belt asteroids.…

Machine learning (automated processes that learn by example in order to classify, predict, discover or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require…

Machine-learning is playing an increasing role in helping the astronomical community to face data analysis challenges, in particular in the field of Galactic Archaeology and large scale spectroscopic surveys. We present recent developments…

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…