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The machine-learning methods
in the asteroids dynamics
Evgeny Smirnov, smirik@gmail.com 

FB/Telegram: @smirik

Pulkovo observatory, Russia
The list of numbered asteroids in the Solar
system has grown significantly in recent years.
In asteroid dynamics, many problems
require numerical integration 

of the equations of motion
This approach is 

computationally expensive
Therefore, fast, novel methods can be
useful to work with big data
AI & ML methods have
become popular among the IT
Google Flu case
Twitter &
Earthquake
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…
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
k-nearest neighbours
Decision Tree
Gradient Boosting over Decision Trees,
Logistic regression, Neural Networks …
Smirnov, Markov, MNRAS, 2017
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
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 %
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��� ���� ��� ���� ��� ���� ���
�
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(480) Hansa family identification using ML
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 %
It works

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A machine learning approach in the dynamics of asteroids

  • 1. The machine-learning methods in the asteroids dynamics Evgeny Smirnov, smirik@gmail.com FB/Telegram: @smirik Pulkovo observatory, Russia
  • 2. The list of numbered asteroids in the Solar system has grown significantly in recent years.
  • 3. In asteroid dynamics, many problems require numerical integration 
 of the equations of motion
  • 4. This approach is 
 computationally expensive Therefore, fast, novel methods can be useful to work with big data
  • 5. AI & ML methods have become popular among the IT
  • 8. 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…
  • 9. 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
  • 12. Gradient Boosting over Decision Trees, Logistic regression, Neural Networks …
  • 14. 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
  • 15. 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 %
  • 16. � ���� ���� ���� ���� ��� ���� ���� ��� ���� ��� ���� ��� ���� ��� � � ���� (480) Hansa family identification using ML
  • 17. 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 %