Machine-learning prediction for mean motion resonance behaviour – The planar case

Li, Xin; Li, Jian; Xia, Zhihong Jeff; Georgakarakos, Nikolaos

doi:10.1093/mnras/stac166

Cited by 8 publications

(2 citation statements)

References 41 publications

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“…Among them, ω is called the adjustable weight vector, b is the offset value, and ω, ϕ[Z(X)] are the n dimension vector, and to find the optimal classification hyperplane, that is, to find the optimal ω and b due to the existence of the fitting error [17], introduce ξ and ξ * as a relaxation variable, and use the ε-SVR model to establish the following model optimization function with constraints:…”

Section: Establishment Of Debris Flow Early Warning Modelmentioning

confidence: 99%

Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine

Jin,

Wang,

Liu

2024

Water

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Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the collaborative Kriging spatial interpolation method based on Kriging spatial interpolation is adopted to process the rainfall data into multi-index fused surface data. The rainfall data after spatial interpolation are used as the input sample data of the support vector machine early warning model, and the optimal parameters of the support vector machine are calculated by the sea squirt algorithm, and then the debris flow early warning results are output. After experimental analysis, the model can obtain rainfall surface data. After calculation by the model, the accuracy of the early warning probability of debris flow is improved, and the early warning result is consistent with the actual result of debris flow.

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Section: Establishment Of Debris Flow Early Warning Modelmentioning

confidence: 99%

Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine

Jin,

Wang,

Liu

2024

Water

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“…The Random Forest method is very robust in processing the spectro-phohtometry data as the tool for detachment of various features of exoplanet light curves [22], exoplanetary atmospheres [9,25], exoplanet prediction [39] and resonant Koiper Belt objects search [19,20]. We deeply exploited it for galaxy morphological classification [15,37].…”

Section: Random Forestmentioning

confidence: 99%

Hunting for exocomet transits in the TESS database using the Random Forest method

DOBRYCHEVA,

VASYLENKO,

KULYK

et al. 2023

Kosm. nauka tehnol.

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This study introduces an approach to detecting exocomet transits in the dataset of the Transiting Exoplanet Survey Satellite (TESS), specifically within its Sector 1. Given the limited number of exocomet transits detected in the observed light curves, creating a sufficient training sample for the machine learning method was challenging. We developed a unique training sample by encapsulating simulated asymmetric transit profiles into observed light curves, thereby creating realistic data for the model training. To analyze these light curves, we employed the TSFresh software, which was a tool for extracting key features that were then used to refine our Random Forest model training. Considering that cometary transits typically exhibit a small depth, less than 1 % of the star’s brightness, we chose to limit our sample to the CDPP parameter. Our study focused on two target samples: light curves with a CDPP of less than 40 ppm and light curves with a CDPP of up to 150 ppm. Each sample was accompanied by a corresponding training set. This methodology achieved an accuracy of approximately 96 %, with both precision and recall rates exceeding 95 % and a balanced F1-score of around 96 %. This level of accuracy was effective in distinguishing between ‘exocomet candidate’ and ‘non-candidate’ classifications for light curves with a CDPP of less than 40 ppm, and our model identified 12 potential exocomet candidates. However, when applying machine learning to less accurate light curves (CDPP up to 150 ppm), we noticed a significant increase in curves that could not be confidently classified, but even in this case, our model identified 20 potential exocomet candidates. These promising results within Sector 1 motivate us to extend our analysis across all TESS sectors to detect and study comet-like activity in the extrasolar planetary systems.

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Machine learning applied to asteroid dynamics

Carruba

Aljbaae

Domingos

et al. 2022

Celest Mech Dyn Astron

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Machine-learning prediction for mean motion resonance behaviour – The planar case

Cited by 8 publications

References 41 publications

Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine

Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine

Hunting for exocomet transits in the TESS database using the Random Forest method

Machine learning applied to asteroid dynamics

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