A machine learning approach in the dynamics of asteroids
•
0 gostou•80 visualizações
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.
Recomendados
Recomendados
Mais conteúdo relacionado
Semelhante a A machine learning approach in the dynamics of asteroids
Semelhante a A machine learning approach in the dynamics of asteroids (20)
Último
Último (20)
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
- 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 %
- 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 %
- 18. It works