Asteroids co-orbital motion classification based on Machine Learning
- URL: http://arxiv.org/abs/2309.10603v1
- Date: Tue, 19 Sep 2023 13:19:31 GMT
- Title: Asteroids co-orbital motion classification based on Machine Learning
- Authors: Giulia Ciacci and Andrea Barucci and Sara Di Ruzza and Elisa Maria
Alessi
- Abstract summary: We consider four different kinds of motion in mean motion resonance with the planet, taking the ephemerides of real asteroids from the JPL Horizons system.
The time series of the variable theta are studied with a data analysis pipeline defined ad hoc for the problem.
We show how the algorithms are able to identify and classify correctly the time series, with a high degree of performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we explore how to classify asteroids in co-orbital motion with
a given planet using Machine Learning. We consider four different kinds of
motion in mean motion resonance with the planet, nominally Tadpole, Horseshoe
and Quasi-satellite, building 3 datasets defined as Real (taking the
ephemerides of real asteroids from the JPL Horizons system), Ideal and
Perturbed (both simulated, obtained by propagating initial conditions
considering two different dynamical systems) for training and testing the
Machine Learning algorithms in different conditions.
The time series of the variable theta (angle related to the resonance) are
studied with a data analysis pipeline defined ad hoc for the problem and
composed by: data creation and annotation, time series features extraction
thanks to the tsfresh package (potentially followed by selection and
standardization) and the application of Machine Learning algorithms for
Dimensionality Reduction and Classification. Such approach, based on features
extracted from the time series, allows to work with a smaller number of data
with respect to Deep Learning algorithms, also allowing to define a ranking of
the importance of the features. Physical Interpretability of the features is
another key point of this approach. In addition, we introduce the SHapley
Additive exPlanations for Explainability technique.
Different training and test sets are used, in order to understand the power
and the limits of our approach. The results show how the algorithms are able to
identify and classify correctly the time series, with a high degree of
performance.
Related papers
- Information plane and compression-gnostic feedback in quantum machine learning [0.0]
The information plane has been proposed as an analytical tool for studying the learning dynamics of neural networks.
We study how the insight on how much the model compresses the input data can be used to improve a learning algorithm.
We benchmark the proposed learning algorithms on several classification and regression tasks using variational quantum circuits.
arXiv Detail & Related papers (2024-11-04T17:38:46Z) - Continual Learning for Multimodal Data Fusion of a Soft Gripper [1.0589208420411014]
A model trained on one data modality often fails when tested with a different modality.
We introduce a continual learning algorithm capable of incrementally learning different data modalities.
We evaluate the algorithm's effectiveness on a challenging custom multimodal dataset.
arXiv Detail & Related papers (2024-09-20T09:53:27Z) - Identifying Light-curve Signals with a Deep Learning Based Object
Detection Algorithm. II. A General Light Curve Classification Framework [0.0]
We present a novel deep learning framework for classifying light curves using a weakly supervised object detection model.
Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data.
We train our model on datasets obtained from both space-based and ground-based multi-band observations of variable stars and transients.
arXiv Detail & Related papers (2023-11-14T11:08:34Z) - TimewarpVAE: Simultaneous Time-Warping and Representation Learning of Trajectories [15.28090738928877]
TimewarpVAE is a manifold-learning algorithm that simultaneously learns timing variations and latent factors of spatial variation.
We show how the algorithm learns appropriate time alignments and meaningful representations of spatial variations in handwriting and fork manipulation datasets.
arXiv Detail & Related papers (2023-10-24T17:43:16Z) - Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs [50.25683648762602]
We introduce Koopman VAE, a new generative framework that is based on a novel design for the model prior.
Inspired by Koopman theory, we represent the latent conditional prior dynamics using a linear map.
KoVAE outperforms state-of-the-art GAN and VAE methods across several challenging synthetic and real-world time series generation benchmarks.
arXiv Detail & Related papers (2023-10-04T07:14:43Z) - Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based
Action Recognition [88.34182299496074]
Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage.
We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets.
By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
arXiv Detail & Related papers (2022-07-17T07:05:39Z) - Towards Similarity-Aware Time-Series Classification [51.2400839966489]
We study time-series classification (TSC), a fundamental task of time-series data mining.
We propose Similarity-Aware Time-Series Classification (SimTSC), a framework that models similarity information with graph neural networks (GNNs)
arXiv Detail & Related papers (2022-01-05T02:14:57Z) - Machine Learning Classification of Kuiper Belt Populations [0.0]
In the outer solar system, the Kuiper Belt contains dynamical sub-populations sculpted by a combination of planet formation and migration and gravitational perturbations from the present-day giant planet configuration.
Here we demonstrate that machine learning algorithms are a promising tool for reducing both the computational time and human effort required for this classification.
arXiv Detail & Related papers (2020-07-07T18:19:03Z) - A Trainable Optimal Transport Embedding for Feature Aggregation and its
Relationship to Attention [96.77554122595578]
We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference.
Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
arXiv Detail & Related papers (2020-06-22T08:35:58Z) - Learned Factor Graphs for Inference from Stationary Time Sequences [107.63351413549992]
We propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences.
neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence.
We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data.
arXiv Detail & Related papers (2020-06-05T07:06:19Z) - CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements.
In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data.
For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
arXiv Detail & Related papers (2020-01-08T17:37:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.