Optimization of Artificial Neural Networks models applied to the identification of images of asteroids' resonant arguments

• URL: http://arxiv.org/abs/2207.14181v1
• Date: Thu, 28 Jul 2022 15:46:39 GMT
• Title: Optimization of Artificial Neural Networks models applied to the identification of images of asteroids' resonant arguments
• Authors: Valerio Carruba, Safwan Aljbaae, Gabriel Carit\'a, Rita Cassia Domingos, Bruno Martins
• Abstract summary: Recent works used Convolutional Neural Networks (CNN) models to perform such task automatically. We compare the outcome of such models with those of some of the most advanced and publicly available CNN architectures, like the VGG, Inception and ResNet. The VGG model, with and without regularizations, proved to be the most efficient method to predict labels of large datasets.
• Score: 0.6449761153631166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The asteroidal main belt is crossed by a web of mean-motion and secular resonances, that occur when there is a commensurability between fundamental frequencies of the asteroids and planets. Traditionally, these objects were identified by visual inspection of the time evolution of their resonant argument, which is a combination of orbital elements of the asteroid and the perturbing planet(s). Since the population of asteroids affected by these resonances is, in some cases, of the order of several thousand, this has become a taxing task for a human observer. Recent works used Convolutional Neural Networks (CNN) models to perform such task automatically. In this work, we compare the outcome of such models with those of some of the most advanced and publicly available CNN architectures, like the VGG, Inception and ResNet. The performance of such models is first tested and optimized for overfitting issues, using validation sets and a series of regularization techniques like data augmentation, dropout, and batch normalization. The three best-performing models were then used to predict the labels of larger testing databases containing thousands of images. The VGG model, with and without regularizations, proved to be the most efficient method to predict labels of large datasets. Since the Vera C. Rubin observatory is likely to discover up to four million new asteroids in the next few years, the use of these models might become quite valuable to identify populations of resonant minor bodies.

Related papers

• Hazardous Asteroids Classification [0.30977113730786693]
  The aim of this project is to use machine learning and deep learning to accurately classify hazardous asteroids. A total of ten methods which consist of five machine learning algorithms and five deep learning models are trained and evaluated to find the suitable model that solves the issue.
  arXiv  Detail & Related papers  (2024-09-03T10:37:24Z)
• A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects [1.0205541448656992]
  We present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO) The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, three new input features: a drag-like coefficient (B*), the average solar index, and the area-to-mass ratio of the object.
  arXiv  Detail & Related papers  (2023-03-17T13:53:59Z)
• Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting [72.89994745876086]
  This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
  arXiv  Detail & Related papers  (2023-02-20T22:25:47Z)
• Towards Asteroid Detection in Microlensing Surveys with Deep Learning [0.0]
  Asteroids are an indelible part of most astronomical surveys though only a few surveys are dedicated to their detection. This paper presents novel deep learning-based solutions for the recovery and discovery of asteroids in the microlensing data gathered by the MOA project.
  arXiv  Detail & Related papers  (2022-11-04T03:16:23Z)
• STAR: Sparse Transformer-based Action Recognition [61.490243467748314]
  This work proposes a novel skeleton-based human action recognition model with sparse attention on the spatial dimension and segmented linear attention on the temporal dimension of data. Experiments show that our model can achieve comparable performance while utilizing much less trainable parameters and achieve high speed in training and inference.
  arXiv  Detail & Related papers  (2021-07-15T02:53:11Z)
• Contemplating real-world object classification [53.10151901863263]
  We reanalyze the ObjectNet dataset recently proposed by Barbu et al. containing objects in daily life situations. We find that applying deep models to the isolated objects, rather than the entire scene as is done in the original paper, results in around 20-30% performance improvement.
  arXiv  Detail & Related papers  (2021-03-08T23:29:59Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• A Two-Stage Deep Learning Detection Classifier for the ATLAS Asteroid Survey [0.0]
  We present a two-step neural network model to separate detections of solar system objects from optical and electronic artifacts. We show that the model reaches 99.6% accuracy on real asteroids in ATLAS data with a 0.4% false negative rate.
  arXiv  Detail & Related papers  (2021-01-22T01:35:08Z)
• NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection [19.726089445453734]
  Time series anomaly detection is a common but challenging task in many industries. It is difficult to detect anomalies in time series with high accuracy, due to noisy data collected from real world. We propose our anomaly detection model: Time series to Image VAE (T2IVAE)
  arXiv  Detail & Related papers  (2021-01-08T08:35:15Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• A Multi-Channel Neural Graphical Event Model with Negative Evidence [76.51278722190607]
  Event datasets are sequences of events of various types occurring irregularly over the time-line. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions.
  arXiv  Detail & Related papers  (2020-02-21T23:10:50Z)

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.