[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123032-en":3,"doc-seo-123032-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123032,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Accelerating Giant-impact Simulations with Machine Learning","Constraining planet-formation models with observed exoplanet demographics demands generating large sets of synthetic planetary systems, often limited by the computational cost of modeling the giant-impact phase. During this stage, embryos evolve gravitationally and merge into planets, followed by potential later collisions. The work introduces a machine-learning approach that predicts collisional outcomes in multiplanet systems from short integrations. Trained on 500,000+ N-body three-planet simulations, it identifies which planet pair collides and the postcollision planetary states, outperforming non-ML baselines and enabling a giant-impact emulator with up to four orders of magnitude speedup.","The Astrophysical Journal, 975:228 (12pp), 2024 November 10 © 2024 . The Author(s) . Published by the American Astronomical Society.  \n[https:](https://doi.org/10.3847/1538-4357/ad7fe5)[//](https://doi.org/10.3847/1538-4357/ad7fe5)[doi.org](https://doi.org/10.3847/1538-4357/ad7fe5)[/](https://doi.org/10.3847/1538-4357/ad7fe5)[10.3847](https://doi.org/10.3847/1538-4357/ad7fe5)[/](https://doi.org/10.3847/1538-4357/ad7fe5)[1538-4357](https://doi.org/10.3847/1538-4357/ad7fe5)[/](https://doi.org/10.3847/1538-4357/ad7fe5)[ad7fe5](https://doi.org/10.3847/1538-4357/ad7fe5)  \nAccelerating Giant-impact Simulations with Machine Learning  \nCaleb Lammers 1 , Miles Cranmer2,3,4 , Sam Hadden5 , Shirley Ho 1,6,7 , Norman Murray5,8 , and Daniel Tamayo9   \n1 Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544, USA  \n2 Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, UK  \n3 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK  \n4 Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge, CB3 0HA, UK  \n5 Canadian Institute for Theoretical Astrophysics, University of Toronto, 60 St. George Street, Toronto, ON M5S 3H8, Canada  \n6 Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, USA  \n7 Department of Physics & Center for Data Science, New York University, 726 Broadway, New York, NY 10003, USA  \n8 Department of Physics, University of Toronto, 60 St. George Street, Toronto, ON M5S 1A7, Canada  \n9 Department of Physics, Harvey Mudd College, Claremont, CA 91711, USA  \nReceived 2024 April 19; revised 2024 September 14; accepted 2024 September 23; published 2024 November 6  \nAbstract  \nConstraining planet-formation models based on the observed exoplanet population requires generating large samples of synthetic planetary systems, which can be computationally prohibitive. A significant bottleneck is simulating the giant-impact phase, during which planetary embryos evolve gravitationally and combine to form planets, which may themselves experience later collisions. To accelerate giant-impact simulations, we present a machine learning (ML) approach to predicting collisional outcomes in multiplanet systems. Trained on more than  \n500,000 N-body simulations of three-planet systems, we develop an ML model that can accurately predict which two planets will experience a collision, along with the state of the postcollision planets, from a short integration of the system’s initial conditions. Our model greatly improves on non-ML baselines that rely on metrics from dynamics theory, which struggle to accurately predict which pair of planets will experience a collision. By combining with a model for predicting long-term stability, we create an ML-based giant-impact emulator, which can predict the outcomes of giant-impact simulations with reasonable accuracy and a speedup of up to 4 orders of magnitude. We expect our model to enable analyses that would not otherwise be computationally feasible. As such, we release our training code, along with an easy-to-use user interface for our collision-outcome model and giantimpact emulator ([https:](https://github.com/dtamayo/spock)[//](https://github.com/dtamayo/spock)[github.com](https://github.com/dtamayo/spock)[/](https://github.com/dtamayo/spock)[dtamayo](https://github.com/dtamayo/spock)[/](https://github.com/dtamayo/spock)[spock](https://github.com/dtamayo/spock)).  \nUnified Astronomy Thesaurus concepts: Exoplanets (498); Extrasolar rocky planets (511); Planet formation (1241); Planetary dynamics (2173)  \n1. Introduction  \nThe nebular hypothesis, initially proposed by I. Kant (1755) and later built upon by P.-S. Laplace (1796), remains the leading explanation for the formation of planetary systems. In the modern picture, terrestrial planets form out of a protoplanetary disk before experiencing a phase of giant impacts in which ","cbCaioaLDziWLT5B","https://ap.wps.com/l/cbCaioaLDziWLT5B","pdf",1604603,1,12,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why are giant-impact simulations a computational bottleneck in planet-formation studies?\",\"answer\":\"Because modeling the giant-impact phase requires large samples of synthetic planetary systems, and the giant-impact evolution involves costly N-body gravitational interactions and potential subsequent collisions.\"},{\"question\":\"What does the proposed machine-learning model predict?\",\"answer\":\"It predicts which two planets in a multiplanet system will undergo a collision and the state of the planets after the collision, using only a short integration of the initial conditions.\"},{\"question\":\"How much faster is the machine-learning giant-impact emulator compared with direct simulations?\",\"answer\":\"By combining the collision-outcome model with a long-term stability predictor, the resulting emulator achieves a speedup of up to four orders of magnitude while maintaining reasonable accuracy.\"}]","Accelerating Giant-impact Simulations with Machine Learning | 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are giant-impact simulations a computational bottleneck in planet-formation studies?","Question",{"text":75,"@type":76},"Because modeling the giant-impact phase requires large samples of synthetic planetary systems, and the giant-impact evolution involves costly N-body gravitational interactions and potential subsequent collisions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed machine-learning model predict?",{"text":80,"@type":76},"It predicts which two planets in a multiplanet system will undergo a collision and the state of the planets after the collision, using only a short integration of the initial conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"How much faster is the machine-learning giant-impact emulator compared with direct simulations?",{"text":84,"@type":76},"By combining the collision-outcome model with a long-term stability predictor, the resulting emulator achieves a speedup of up to four orders of magnitude while maintaining 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