Identification and Classification of Exoplanets Using Machine Learning Techniques

• URL: http://arxiv.org/abs/2305.09596v1
• Date: Tue, 16 May 2023 16:51:07 GMT
• Title: Identification and Classification of Exoplanets Using Machine Learning Techniques
• Authors: Prithivraj G and Alka Kumari
• Abstract summary: We consider building upon some existing work on exoplanet identification using residual networks for the data of the Kepler space telescope and its extended mission K2. This paper aims to explore how deep learning algorithms can help in classifying the presence of exoplanets with less amount of data in one case and a more extensive variety of data in another.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: NASA's Kepler Space Telescope has been instrumental in the task of finding the presence of exoplanets in our galaxy. This search has been supported by computational data analysis to identify exoplanets from the signals received by the Kepler telescope. In this paper, we consider building upon some existing work on exoplanet identification using residual networks for the data of the Kepler space telescope and its extended mission K2. This paper aims to explore how deep learning algorithms can help in classifying the presence of exoplanets with less amount of data in one case and a more extensive variety of data in another. In addition to the standard CNN-based method, we propose a Siamese architecture that is particularly useful in addressing classification in a low-data scenario. The CNN and ResNet algorithms achieved an average accuracy of 68% for three classes and 86% for two-class classification. However, for both the three and two classes, the Siamese algorithm achieved 99% accuracy.

Related papers

• DBNets: A publicly available deep learning tool to measure the masses of young planets in dusty protoplanetary discs [49.1574468325115]
  We develop DBNets, a tool to quickly infer the mass of allegedly embedded planets from protoplanetary discs. We extensively tested our tool on out-of-distribution data. DBNets can identify inputs strongly outside its training scope returning an uncertainty above a specific threshold. It can be reliably applied only on discs observed with inclinations below approximately 60deg, in the optically thin regime.
  arXiv  Detail & Related papers  (2024-02-19T19:00:09Z)
• Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions [73.60788465154572]
  Current 2D and 3D location encoders are designed to model point distances in Euclidean space. We propose a multi-scale location encoder called Sphere2Vec which can preserve spherical distances when encoding point coordinates on a spherical surface.
  arXiv  Detail & Related papers  (2023-06-30T12:55:02Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms [0.0]
  We use two variations of generative adversarial networks to detect transiting exoplanets in K2 data. Our techniques are able to categorize the light curves with a recall and precision of 1.00 on the test data.
  arXiv  Detail & Related papers  (2022-07-20T05:45:36Z)
• Satellite Image Time Series Analysis for Big Earth Observation Data [50.591267188664666]
  This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome.
  arXiv  Detail & Related papers  (2022-04-24T15:23:25Z)
• Identifying Exoplanets with Machine Learning Methods: A Preliminary Study [1.553390835237685]
  We propose the idea of using machine learning methods to identify exoplanets. We used the Kepler dataset collected by NASA from the Kepler Space Observatory to conduct supervised learning. We also conducted unsupervised learning, which divides confirmed exoplanets into different clusters, using k-means clustering.
  arXiv  Detail & Related papers  (2022-04-01T23:48:26Z)
• Identifying Potential Exomoon Signals with Convolutional Neural Networks [0.0]
  We train an ensemble of convolutional neural networks (CNNs) to identify candidate exomoon signals in single-transit events observed by Kepler. We find a small fraction of these transits contain moon-like signals, though we caution against strong inferences of the exomoon occurrence rate from this result.
  arXiv  Detail & Related papers  (2021-09-22T03:37:09Z)
• DeepSatData: Building large scale datasets of satellite images for training machine learning models [77.17638664503215]
  This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models. We discuss issues faced from the point of view of deep neural network training and evaluation.
  arXiv  Detail & Related papers  (2021-04-28T15:13:12Z)
• AutoSpace: Neural Architecture Search with Less Human Interference [84.42680793945007]
  Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. We propose a novel differentiable evolutionary framework named AutoSpace, which evolves the search space to an optimal one. With the learned search space, the performance of recent NAS algorithms can be improved significantly compared with using previously manually designed spaces.
  arXiv  Detail & Related papers  (2021-03-22T13:28:56Z)
• Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods [1.9491825010518622]
  The AI/ML ThetaRay system is trained initially with Kepler exoplanetary data and validated with confirmed exoplanets. By the application of ThetaRay to 10,803 light curves of threshold crossing events (TCEs) produced by the TESS mission, we uncover 39 new exoplanetary candidates.
  arXiv  Detail & Related papers  (2021-02-20T12:28:39Z)
• Exoplanet Detection using Machine Learning [0.0]
  We introduce a new machine learning based technique to detect exoplanets using the transit method. For Kepler data, the method is able to predict a planet with an AUC of 0.948, so that 94.8 per cent of the true planet signals are ranked higher than non-planet signals. For the Transiting Exoplanet Survey Satellite (TESS) data, we found our method can classify light curves with an accuracy of 0.98, and is able to identify planets with a recall of 0.82 at a precision of 0.63.
  arXiv  Detail & Related papers  (2020-11-28T14:06:39Z)

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.