• Corpus ID: 238744443

Machine Learning applied to asteroid dynamics: an emerging research field

  title={Machine Learning applied to asteroid dynamics: an emerging research field},
  author={Valerio Carruba and Safwan Aljbaae and R. C. Domingos and M. E. Huaman and William Alphonse Barletta},
Machine Learning (ML) is the study of computer algorithms that can learn from data or data exposure to better themselves automatically. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to learn a general rule that maps inputs to outputs, and unsupervised learning, where no label is provided to the learning algorithm, leaving it alone to find structures. Deep learning is a branch of machine learning based on artificial… 


Deep Learning for Time Series Forecasting: A Survey
The most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations.
Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
  • C. Fluke, C. Jacobs
  • Computer Science, Physics
    Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
  • 2020
This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems.
DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning
We present DeepStreaks, a convolutional-neural-network, deep-learning system designed to efficiently identify streaking fast-moving near-Earth objects that are detected in the data of the Zwicky
2021 for the zwicky transient facility. Publications of the Astronomical Society of the Pacific 131(997):038002, DOI 10.1088/1538-3873/aaf3fa, URL https://doi
  • 2021
An Evaluation of Edge
  • 2021
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks
The ongoing efforts in developing high-accuracy learned machine learning models enable significantly faster evaluations of accelerators as an alternative to time-consuming cycle-accurate simulators and establish an exciting opportunity for rapid hardware/software co-design.
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model
This paper addresses the problem of unsupervised approach of credit card fraud detection in unbalanced dataset using the ARIMA model and is compared to 4 anomaly detection approaches such as K-Means, Box-Plot, Local Outlier Factor and Isolation Forest.
Artificial Neural Network classification of asteroids in the M1: 2 mean-motion resonance with Mars
This work uses for the first time ANN for the purpose of automatically identifying the behaviour of asteroid orbits affected by the M1:2 mean-motion resonance with Mars, allowing to identify the orbital type of all numbered asteroids in the region.
As - teroid spectral taxonomy using neural networks
  • A & A
  • 2021
Asteroid spectral taxonomy using neural networks
Aims. We explore the performance of neural networks in automatically classifying asteroids into their taxonomic spectral classes. We particularly focus on what the methodology could offer the ESA