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.

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

6. Google Flu case

7. Twitter &
Earthquake

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

10. k-nearest neighbours

11. Decision Tree

12. Gradient Boosting over Decision Trees,
Logistic regression, Neural Networks …

13. Smirnov, Markov, MNRAS, 2017

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 %

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(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 %

18. It works