Chaos identification through the autocorrelation function indicator $$({\textit{ACFI}})$$

Carruba, V.; Aljbaae, S.; Domingos, R. C.; Huaman, M.; Barletta, William A.

doi:10.1007/s10569-021-10036-6

Cited by 11 publications

(13 citation statements)

References 38 publications

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“…Among the possible chaos indicators we selected the Fast Lyapunov Indicators (hereafter FLI 18 ) and the frequency map analysis 19 (see references 20 and 21 for alternative chaos indicators). In this section we present these two indicators and the approach to index the time series.…”

Section: Indexing Methods For Regular and Chaotic Motionmentioning

confidence: 99%

Classification of regular and chaotic motions in Hamiltonian systems with deep learning

Celletti

Galeş

Rodríguez-Fernández

et al. 2022

Sci Rep

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This paper demonstrates the capabilities of convolutional neural networks (CNNs) at classifying types of motion starting from time series, without any prior knowledge of the underlying dynamics. The paper applies different forms of deep learning to problems of increasing complexity with the goal of testing the ability of different deep learning architectures at predicting the character of the dynamics by simply observing a time-ordered set of data. We will demonstrate that a properly trained CNN can correctly classify the types of motion on a given data set. We also demonstrate effective generalisation capabilities by using a CNN trained on one dynamic model to predict the character of the motion governed by another dynamic model. The ability to predict types of motion from observations is then verified on a model problem known as the forced pendulum and on a relevant problem in Celestial Mechanics where observational data can be used to predict the long-term evolution of the system.

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Section: Indexing Methods For Regular and Chaotic Motionmentioning

confidence: 99%

Classification of regular and chaotic motions in Hamiltonian systems with deep learning

Celletti

Galeş

Rodríguez-Fernández

et al. 2022

Sci Rep

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“…Identifying the optimal machine learning method and the combination of hyper-parameters for a given problem may be a long and time-consuming process. Here, as done in Carruba et al (2021) for the case of asteroids near the z1 and z2 secular resonances, we use an approach based on genetic algorithms (Chen et al 2004). Genetic algorithms use an approach based on genetic evolution.…”

Section: Applications Of Genetic Algorithms To M1:2 Resonant Argument...mentioning

confidence: 99%

“…Models similar to the best ones can then be created, and the process can be repeated until some conditions are satisfied. Interested readers can found more details on this procedure in (Chen et al 2004) and (Carruba et al 2021). As in the last paper, we used the Tpot Python library (Trang et al 2019;Olson et al 2016) with 5 generations, a population size (the number of models to keep after each generation) of 20, and a cross-validation cv equal to 5.…”

Section: Applications Of Genetic Algorithms To M1:2 Resonant Argument...mentioning

confidence: 99%

“…Gallardo et al (2011) found that the three asteroids families most affected by the M1:2 mean-motion resonance were those of Nysa, Massalia, and Vesta. Here, following the approach of Carruba et al (2021), we use learning Hierarchical Clustering Method (HCM), as implemented in Carruba et al (2019), on a domain of proper elements for the group of M1:2 asteroids above described. The procedure used to implement this method was the same as that applied in Carruba et al (2021): a critical distance cutoff 1 2 d0 was obtained, and groups were identified for values of d0 ±5 m/s.…”

Section: Identification Of Resonant Groupsmentioning

confidence: 99%

“…During the last five years, machine learning and deep learning have been more and more been used in the field of asteroid dynamics. Among the latest application, supervised methods of machine learning have been used to identify the population of asteroids in three-body mean-motion resonances (Smirnov & Markov 2017), new members of known asteroid families (Carruba et al 2020), and asteroids groups inside the z1 and z2 secular resonances (Carruba et al 2021), among others. Deep learning in the form of artificial neural networks has been recently used for identifying members of asteroid families (Vujičić et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network classification of asteroids in the M1:2 mean-motion resonance with Mars

Carruba,

Aljbaae,

Domingos

et al. 2021

Preprint

Self Cite

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Artificial neural networks (ANN) have been successfully used in the last years to identify patterns in astronomical images. The use of ANN in the field of asteroid dynamics has been, however, so far somewhat limited. In this work we used 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. Our model was able to perform well above 85% levels for identifying images of asteroid resonant arguments in term of standard metrics like accuracy, precision and recall, allowing to identify the orbital type of all numbered asteroids in the region. Using supervised machine learning methods, optimized through the use of genetic algorithms, we also predicted the orbital status of all multi-opposition asteroids in the area. We confirm that the M1:2 resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroid families.

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Neural networks in celestial dynamics: capabilities, advantages, and challenges in orbital dynamics around asteroids

Aljbaae

2025

Machine Learning for Small Bodies in the Solar System

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Chaos identification through the autocorrelation function indicator $$({\textit{ACFI}})$$

Cited by 11 publications

References 38 publications

Classification of regular and chaotic motions in Hamiltonian systems with deep learning

Classification of regular and chaotic motions in Hamiltonian systems with deep learning

Artificial Neural Network classification of asteroids in the M1:2 mean-motion resonance with Mars

Neural networks in celestial dynamics: capabilities, advantages, and challenges in orbital dynamics around asteroids

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