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A machine learning approach in the dynamics of ...

Evgeny Smirnov
September 03, 2017

A machine learning approach in the dynamics of asteroids

In asteroid dynamics, many problems require numerical integration of the equations of motion. Due to the number of objects, it might require significant computational resources. Thus, one might find a better way to solve them — by using a machine learning approach.

Evgeny Smirnov

September 03, 2017
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  1. The list of numbered asteroids in the Solar system has

    grown significantly in recent years.
  2. ML in astronomy • Outlier detection techniques for Exoplanets (Goel

    & Montgomery, 2015); • Cosmological parameter estimation via neural network (Hobson et al., 2014); • Identification & classification of active galactic nuclei (Cavouti et al., 2014); • Visualize & classify a large set of Type Ia Supernova spectra (Sasdelli et al., 2016); • Filtering out a large number of false-positive streak detections of near- Earth asteroid candidates in the Palomar Transient Factory (Waszczak et al., 2017); • A Machine Learns to predict the stability of tightly packed planetary systems (Tamayo et al. 2016); • A lot of others…
  3. Types of ML • Supervised learning: example inputs and desired

    outputs are provided; the goal is to create a map that binds inputs to outputs. • Unsupervised learning: no examples are provided, the goal is to discover hidden patterns. • Reinforcement learning: the same as supervised learning but instead of a training set there is an environment that provides the rewards based on the actions
  4. MMR identification using ML Smirnov E.A., Markov A.B. Identification of

    asteroids trapped inside three- body mean motion resonances: a machine-learning approach. MNRAS. 469. 2017
  5. MMR identification using ML Smirnov E.A., Markov A.B. Identification of

    asteroids trapped inside three- body mean motion resonances: a machine-learning approach. MNRAS. 469. 2017 Recall 98,38 % Precision 91,01 % Accuracy 99,97 %
  6. � ���� ���� ���� ���� ��� ���� ���� ��� ����

    ��� ���� ��� ���� ��� � � ���� (480) Hansa family identification using ML
  7. Statistics Family Koronis Hansa All Recall 99,91 % 100,00 %

    98,01 % Precision 77,93 % 84,04 % 50,22 % Accuracy 99,56 % 99,95 % 99,67 %