Earth-like planet predictor: A machine learning approach

• URL: http://arxiv.org/abs/2504.07235v1
• Date: Wed, 09 Apr 2025 19:21:46 GMT
• Title: Earth-like planet predictor: A machine learning approach
• Authors: Jeanne Davoult, Romain Eltschinger, Yann Alibert,
• Abstract summary: The aim of this work is to predict which stars are most likely to host an Earth-like planet (ELP) to avoid blind searches.<n>We trained a Random Forest to recognise and classify systems as 'hosting an ELP' or 'not hosting an ELP'<n>The tests conducted on the machine learning (ML) model yield precision scores of up to 0.99, indicating that 99% of the systems identified as having ELPs possess at least one.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Searching for planets analogous to Earth in terms of mass and equilibrium temperature is currently the first step in the quest for habitable conditions outside our Solar System and, ultimately, the search for life in the universe. Future missions such as PLATO or LIFE will begin to detect and characterise these small, cold planets, dedicating significant observation time to them. The aim of this work is to predict which stars are most likely to host an Earth-like planet (ELP) to avoid blind searches, minimises detection times, and thus maximises the number of detections. Using a previous study on correlations between the presence of an ELP and the properties of its system, we trained a Random Forest to recognise and classify systems as 'hosting an ELP' or 'not hosting an ELP'. The Random Forest was trained and tested on populations of synthetic planetary systems derived from the Bern model, and then applied to real observed systems. The tests conducted on the machine learning (ML) model yield precision scores of up to 0.99, indicating that 99% of the systems identified by the model as having ELPs possess at least one. Among the few real observed systems that have been tested, 44 have been selected as having a high probability of hosting an ELP, and a quick study of the stability of these systems confirms that the presence of an Earth-like planet within them would leave them stable. The excellent results obtained from the tests conducted on the ML model demonstrate its ability to recognise the typical architectures of systems with or without ELPs within populations derived from the Bern model. If we assume that the Bern model adequately describes the architecture of real systems, then such a tool can prove indispensable in the search for Earth-like planets. A similar approach could be applied to other planetary system formation models to validate those predictions.

Related papers

• Machine learning approach for mapping the stable orbits around planets [0.0]
  This study aims to utilize Machine Learning (ML) techniques to generate predictive maps of stable regions surrounding a hypothetical planet. The approach can also be extended to planet-satellite systems, planetary ring systems, and other similar configurations.
  arXiv  Detail & Related papers  (2024-12-05T19:23:05Z)
• A Foundation Model for the Earth System [82.73624748093333]
  We introduce Aurora, a large-scale foundation model for the Earth system trained on over a million hours of diverse data. Aurora outperforms operational forecasts for air quality, ocean waves, tropical cyclone tracks, and high-resolution weather forecasting at orders of magnitude smaller computational expense than dedicated existing systems.
  arXiv  Detail & Related papers  (2024-05-20T14:45:18Z)
• 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)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix [59.55173022987071]
  We study the potential of semi-supervised learning for class-agnostic motion prediction. Our framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data. Our method exhibits comparable performance to weakly and some fully supervised methods.
  arXiv  Detail & Related papers  (2023-12-13T09:32:50Z)
• Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs [75.7104463046767]
  This paper proposes a novel learning based simulation model that characterizes the varying spatial and temporal dependencies in particle systems. We empirically evaluate GNSTODE's simulation performance on two real-world particle systems, Gravity and Coulomb.
  arXiv  Detail & Related papers  (2023-05-21T03:51:03Z)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• Predictive World Models from Real-World Partial Observations [66.80340484148931]
  We present a framework for learning a probabilistic predictive world model for real-world road environments. While prior methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only.
  arXiv  Detail & Related papers  (2023-01-12T02:07:26Z)
• Locating Hidden Exoplanets in ALMA Data Using Machine Learning [10.316742952272394]
  We demonstrate that machine learning can quickly and accurately detect the presence of planets. We train our model on synthetic images generated from simulations and apply it to real observations to identify forming planets in real systems.
  arXiv  Detail & Related papers  (2022-11-17T14:02:16Z)
• Automation Of Transiting Exoplanet Detection, Identification and Habitability Assessment Using Machine Learning Approaches [0.0]
  We analyze the light intensity curves from stars captured by the Kepler telescope to detect the potential curves that exhibit the characteristics of an existence of a possible planetary system. We address the automation of exoplanet identification and habitability determination by leveraging several state-of-art machine learning and ensemble approaches.
  arXiv  Detail & Related papers  (2021-12-06T19:00:12Z)
• On planetary systems as ordered sequences [7.216830424040808]
  We consider what information belongs to the configuration, or ordering, of 4286 Kepler planets in their 3277 planetary systems. We train a neural network model to predict the radius and period of a planet based on the properties of its host star. We adapt a model used for unsupervised part-of-speech tagging in computational linguistics to investigate whether planets or planetary systems fall into natural categories with physically interpretable "grammatical rules"
  arXiv  Detail & Related papers  (2021-05-20T18:00:29Z)

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.