SPOCK 2.0: Update to the FeatureClassifier in the Stability of Planetary Orbital Configurations Klassifier

• URL: http://arxiv.org/abs/2501.15017v1
• Date: Sat, 25 Jan 2025 01:48:46 GMT
• Title: SPOCK 2.0: Update to the FeatureClassifier in the Stability of Planetary Orbital Configurations Klassifier
• Authors: Elio Thadhani, Yolanda Ba, Hanno Rein, Daniel Tamayo,
• Abstract summary: Stability of Planetary Orbital configurations Klassifier (SPOCK) package collects machine learning models for predicting the stability and collisional evolution of compact planetary systems. We improve SPOCK's binary stability classifier (FeatureClassifier), which predicts orbital stability by collecting data over a short N-body integration of a system. We provide a cleaned dataset of 100,000+ unique integrations, release a newly trained stability classification model, and make minor updates to the API.
• Score: 0.0
• License:
• Abstract: The Stability of Planetary Orbital Configurations Klassifier (SPOCK) package collects machine learning models for predicting the stability and collisional evolution of compact planetary systems. In this paper we explore improvements to SPOCK's binary stability classifier (FeatureClassifier), which predicts orbital stability by collecting data over a short N-body integration of a system. We find that by using a system-specific timescale (rather than a fixed $10^4$ orbits) for the integration, and by using this timescale as an additional feature, we modestly improve the model's AUC metric from 0.943 to 0.950 (AUC=1 for a perfect model). We additionally discovered that $\approx 10\%$ of N-body integrations in SPOCK's original training dataset were duplicated by accident, and that $<1\%$ were misclassified as stable when they in fact led to ejections. We provide a cleaned dataset of 100,000+ unique integrations, release a newly trained stability classification model, and make minor updates to the API.

Related papers

• An extended asymmetric sigmoid with Perceptron (SIGTRON) for imbalanced linear classification [0.0]
  This article presents a new parameterized sigmoid called SIGTRON, and its companion convex model called SIGTRON-imbalanced classification (SIC) model. In contrast to the conventional $pi$-weighted cost-sensitive learning model, the SIC model does not have an external $pi$-weight on the loss function. We show that the proposed SIC model is more adaptive to variations of the dataset.
  arXiv  Detail & Related papers  (2023-12-26T13:14:17Z)
• Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels [0.7499722271664144]
  We introduce Sequential Feature Detachment (SFD) to identify and prune non-essential features in ROCKET-based models. SFD can produce models with better test accuracy using only 10% of the original features. We also present an end-to-end procedure for determining an optimal balance between the number of features and model accuracy.
  arXiv  Detail & Related papers  (2023-09-25T20:24:36Z)
• Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees [57.67528738886731]
  We study the numerical stability of scalable sparse approximations based on inducing points. For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.
  arXiv  Detail & Related papers  (2022-10-14T15:20:17Z)
• Algebraic and machine learning approach to hierarchical triple-star stability [0.0]
  We present two approaches to determine the stability of a hierarchical triple-star system. The first is an improvement on the semi-analytical stability criterion of Mardling & Aarseth (2001), where we introduce a dependence on inner orbital eccentricity. The second involves a machine learning approach, where we use a multilayer perceptron (MLP) to classify triple-star systems as stable' and unstable'
  arXiv  Detail & Related papers  (2022-07-07T08:29:17Z)
• Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks [68.8204255655161]
  We propose a convolutional neural network model to predict the stability of hierarchical triples. All trained models are made publicly available, allowing to predict the stability of hierarchical triple systems $200$ times faster than pure $N$-body methods.
  arXiv  Detail & Related papers  (2022-06-24T17:58:13Z)
• Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique [0.3973560285628012]
  We present a simple yet novel time series imputation technique with the goal of constructing an irregular time series that is uniform across every sample in a data set. We fix a grid defined by the midpoints of non-overlapping bins (dubbed "slices") of observation times and ensure that each sample has values for all of the features at that given time. This allows one to both impute fully missing observations to allow uniform time series classification across the entire data and, in special cases, to impute individually missing features.
  arXiv  Detail & Related papers  (2022-01-14T19:23:24Z)
• Self-Supervised Class Incremental Learning [51.62542103481908]
  Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time.
  arXiv  Detail & Related papers  (2021-11-18T06:58:19Z)
• Stability and Identification of Random Asynchronous Linear Time-Invariant Systems [81.02274958043883]
  We show the additional benefits of randomization and asynchrony on the stability of linear dynamical systems. For unknown randomized LTI systems, we propose a systematic identification method to recover the underlying dynamics.
  arXiv  Detail & Related papers  (2020-12-08T02:00:04Z)
• Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction [58.98112070128482]
  We propose a lightweight solution for series prediction based on historic observations. It consists of a heterogeneous ensemble method composed of two models - a neural network and a mean predictor. It achieves an overall $R2$ score of 0.10 on the available FedCSIS 2020 challenge dataset.
  arXiv  Detail & Related papers  (2020-07-07T15:44:16Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)

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.