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title="Bluesky"><span class="visuallyhidden">Bluesky</span></a></li> </ul></div> </nav> </div> </div> <div class="main wrap cf"> <div class="row"> <div class="col-8 main-content"> <article id="post-12997" class="post-12997 post type-post status-publish format-standard has-post-thumbnail category-astrobites category-highlights tag-machine-learning tag-planet-formation tag-planetary-systems title-above" itemscope itemtype="http://schema.org/Article"> <header class="post-header cf"> <div class="heading cf"> <h1 class="post-title item fn" itemprop="name"> Giant Impacts Go Boom (Machine Learning Edition) </h1>  </div> <div class="post-meta cf"> <span class="posted-by">By <span class="reviewer" itemprop="author"><a href="https://aasnova.org/person/astrobites/" title="Astrobites">Astrobites</a></span> </span> <span class="posted-on">on <span class="dtreviewed"> <time class="value-title" datetime="2024-11-26T12:00:35-05:00" title="2024-11-26" itemprop="datePublished">26 November 2024</time> </span> </span> <span class="subcat">Astrobites</span> <span class="cats"> <div class="post-share"> <span class="text">Share:</span> <span class="share-links">  <a href="https://twitter.com/intent/tweet?url=https%3A%2F%2Faasnova.org%2F2024%2F11%2F26%2Fgiant-impacts-go-boom-machine-learning-edition%2F" class="fa fa-twitter" title="Tweet It"> <span class="visuallyhidden">Twitter</span></a> <a href="http://www.facebook.com/sharer.php?u=https%3A%2F%2Faasnova.org%2F2024%2F11%2F26%2Fgiant-impacts-go-boom-machine-learning-edition%2F" class="fa fa-facebook" title="Share on Facebook"> <span class="visuallyhidden">Facebook</span></a> <a href="http://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Faasnova.org%2F2024%2F11%2F26%2Fgiant-impacts-go-boom-machine-learning-edition%2F" class="fa fa-linkedin" title="Share on LinkedIn"> <span class="visuallyhidden">LinkedIn</span></a> <a href="https://plus.google.com/share?url=https%3A%2F%2Faasnova.org%2F2024%2F11%2F26%2Fgiant-impacts-go-boom-machine-learning-edition%2F" class="fa fa-google-plus" title="Share on Google+"> <span class="visuallyhidden">Google+</span></a> <a href="http://reddit.com/submit?url=https%3A%2F%2Faasnova.org%2F2024%2F11%2F26%2Fgiant-impacts-go-boom-machine-learning-edition%2F" class="fa fa-reddit" title="Share on Reddit"> <span class="visuallyhidden">Reddit</span></a> <a href="mailto:?subject=Giant%20Impacts%20Go%20Boom%20%28Machine%20Learning%20Edition%29&body=https%3A%2F%2Faasnova.org%2F2024%2F11%2F26%2Fgiant-impacts-go-boom-machine-learning-edition%2F" class="fa fa-envelope-o" title="Share via Email"> <span class="visuallyhidden">Email</span></a> </span> </div> </span> </div> <div class="featured"> <a href="https://aasnova.org/wp-content/uploads/2024/11/bd20307_fnl_lynettecook_0_crop.jpg" title="Giant Impacts Go Boom (Machine Learning Edition)" itemprop="image"> <img width="702" height="336" src="https://aasnova.org/wp-content/uploads/2024/11/bd20307_fnl_lynettecook_0_crop-702x336.jpg" class="attachment-main-slider size-main-slider wp-post-image" alt="illustration of planets colliding" title="Giant Impacts Go Boom (Machine Learning Edition)" decoding="async" fetchpriority="high" srcset="https://aasnova.org/wp-content/uploads/2024/11/bd20307_fnl_lynettecook_0_crop-702x336.jpg 702w, https://aasnova.org/wp-content/uploads/2024/11/bd20307_fnl_lynettecook_0_crop-260x124.jpg 260w, https://aasnova.org/wp-content/uploads/2024/11/bd20307_fnl_lynettecook_0_crop-768x367.jpg 768w, https://aasnova.org/wp-content/uploads/2024/11/bd20307_fnl_lynettecook_0_crop-600x287.jpg 600w, https://aasnova.org/wp-content/uploads/2024/11/bd20307_fnl_lynettecook_0_crop.jpg 1041w" sizes="(max-width: 702px) 100vw, 702px" /> </a> <div class="caption">Illustration of a collision between two planets. [<a href="https://www.nasa.gov/image-article/what-happens-when-planets-collide/">NASA/SOFIA/Lynette Cook</a>]</div> </div> </header> <div class="post-container cf"> <div class="post-content-right"> <div class="post-content description " itemprop="articleBody"> <blockquote><p><em>Editor’s Note:</em> <a href="http://astrobites.org" target="_blank" rel="noopener noreferrer">Astrobites</a> is a graduate-student-run organization that digests astrophysical literature for undergraduate students. As part of the <a href="https://aas.org/posts/news/2016/06/aas-and-astrobites-formalize-partnership" target="_blank" rel="noopener noreferrer">partnership</a> between the AAS and astrobites, we occasionally repost astrobites content here at AAS Nova. We hope you enjoy this post from astrobites; the original can be viewed at <a href="http://astrobites.org" target="_blank" rel="noopener noreferrer">astrobites.org</a>.</p></blockquote> <p><strong>Title: </strong><a href="https://doi.org/10.3847/1538-4357/ad7fe5">Accelerating Giant Impact Simulations with Machine Learning</a><br /> <strong>Authors: </strong>Caleb Lammers et al.<br /> <strong>First Author’s Institution: </strong>Princeton University<br /> <strong>Status: </strong>Published in <em>ApJ</em></p> <h4 class="wp-block-heading">Planet Formation</h4> <p>In the <a href="https://geo.libretexts.org/Bookshelves/Geology/Book%3A_An_Introduction_to_Geology_(Johnson_Affolter_Inkenbrandt_and_Mosher)/08%3A_Earth_History/8.02%3A_Origin_of_the_Solar_SystemThe_Nebular_Hypothesis">nebular hypothesis</a> view of planet formation, planets form out of a <a href="https://astrobites.org/2011/03/11/review-article-protoplanetary-disks-and-their-evolution/">protoplanetary disk</a>, starting out as small dust grains, some of which combine and grow to form <a href="https://www.merriam-webster.com/dictionary/planetesimal">planetesimals</a> and eventually planets. Part of this process is the giant-impact phase, in which the planetesimals experience frequent, violent collisions, leading to the growth of what will eventually become fully fledged planets.</p> <p>Numerically modeling the giant-impact phase is complicated by the computational difficulty of running simulations of many bodies over long timescales. Machine learning has already been adopted to improve and speed up planetary simulations, such as with the <a href="https://github.com/dtamayo/spock">Stability of Planetary Orbital Configurations Klassifier (SPOCK) package</a>. SPOCK’s first incarnation (<a href="https://www.princeton.edu/news/2020/07/13/artificial-intelligence-predicts-which-planetary-systems-will-survive">SPOCKI</a>) predicts whether a compact planetary system is stable over 1,000,000,000 orbits based on the results of a shorter 10,000-orbit integration, and an extension (<a href="https://ui.adsabs.harvard.edu/abs/2021PNAS..11826053C/abstract">SPOCKII</a>) uses those results to predict when a planetary system will destabilize.</p> <h4 class="wp-block-heading">Creating a Machine Learning™ Framework</h4> <p>Today’s article extends previous work by using machine learning to predict the outcomes of planet–planet collisions in three-planet systems with two subtasks: 1) predicting which planet pair will collide and 2) predicting the orbital configuration of the resulting system. The authors use <a href="https://cloud.google.com/discover/what-is-supervised-learning">supervised learning</a>: giving a model a training set with inputs and correct outputs so that the model can learn the mapping from inputs to outputs. Predicting collisions (subtask 1) requires classification, producing an outcome from a set of discrete options or categories (in this case, which planets collide). Predicting resulting orbital configurations (subtask 2) requires regression, producing numerical values describing the orbit of the post-collision system. The authors use a training set of more than 500,000 <em>N</em>-body simulations integrated with the <a href="https://rebound.readthedocs.io/en/latest/">REBOUND package</a>. They initialize tightly packed three-planet systems with randomized initial conditions and integrate the systems for 10,000,000 orbits (of the innermost planet) with mass and momentum conservation. The authors only keep the systems with mergers between 10,000 and 10,000,000 orbits so the machine learning framework does not concern itself with non-merging systems.</p> <p>The authors use separate <a href="https://scikit-learn.org/stable/modules/neural_networks_supervised.html">multi-layer perceptron</a> models independently trained on 80% of the training set (leaving the rest as a validation set) to complete both subtasks, following the schematic shown in Figure 1. The collision classifier takes as inputs the orbital elements of the three planets after they’ve completed 10,000 orbits, generates the probabilities of planet-pair collisions, and samples the probabilities to determine which pair of planets collide. The orbital outcome regressor takes the orbital elements of the three planets and the choice of which planets collide to predict the new orbital elements (semi-major axis, eccentricity, and inclination) of the resulting system.</p> <div id="attachment_13000" style="width: 712px" class="wp-caption aligncenter"><a href="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr.jpg" data-rel="lightbox-image-0" data-rl_title="Figure 1: A schematic of the machine learning model in which a classifier predicts which pair among three planets collides and a regressor predicts the orbital configuration of the two resulting planets. [Lammers et al. 2024]" data-rl_caption="Figure 1: A schematic of the machine learning model in which a classifier predicts which pair among three planets collides and a regressor predicts the orbital configuration of the two resulting planets. [Lammers et al. 2024]" title="Figure 1: A schematic of the machine learning model in which a classifier predicts which pair among three planets collides and a regressor predicts the orbital configuration of the two resulting planets. [Lammers et al. 2024]"><img decoding="async" aria-describedby="caption-attachment-13000" class="size-medium wp-image-13000" src="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr-702x751.jpg" alt="schematic of the machine learning framework" width="702" height="751" srcset="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr-702x751.jpg 702w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr-260x278.jpg 260w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr-768x821.jpg 768w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr-600x642.jpg 600w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr-1436x1536.jpg 1436w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr-1320x1412.jpg 1320w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f1_hr.jpg 1800w" sizes="(max-width: 702px) 100vw, 702px" /></a><p id="caption-attachment-13000" class="wp-caption-text">Figure 1: A schematic of the machine learning model in which a classifier predicts which pair among three planets collides and a regressor predicts the orbital configuration of the two resulting planets. [Lammers et al. 2024]</p></div> <h4 class="wp-block-heading">Piecing Together the Giant-Impact Emulator</h4> <p>The authors combine their machine learning model with SPOCKII to create an iterative emulator to model the giant-impact phase, with a schematic shown in Figure 2. The emulator takes in overly packed, multi-planet systems with randomly initialized configurations, groups the systems into trios of planets, uses SPOCKII to predict when the systems will destabilize, merges the most unstable trio, and then repeats until stability is achieved. The authors also run <em>N</em>-body simulations for 500 ten-planet systems for comparison with the results of the emulator. As shown in Figure 3, there is close agreement with masses, spacings, inclinations, and most system-level properties.</p> <div id="attachment_12999" style="width: 712px" class="wp-caption aligncenter"><a href="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr.jpg" data-rel="lightbox-image-1" data-rl_title="Figure 2: A schematic of the giant-impact emulator in which a multi-planet system is broken into trios. The machine learning model shown in Figure 1 predicts the results of a collision in the most unstable trio, and the process repeats until a stable system is formed. [Lammers et al. 2024]" data-rl_caption="Figure 2: A schematic of the giant-impact emulator in which a multi-planet system is broken into trios. The machine learning model shown in Figure 1 predicts the results of a collision in the most unstable trio, and the process repeats until a stable system is formed. [Lammers et al. 2024]" title="Figure 2: A schematic of the giant-impact emulator in which a multi-planet system is broken into trios. The machine learning model shown in Figure 1 predicts the results of a collision in the most unstable trio, and the process repeats until a stable system is formed. [Lammers et al. 2024]"><img decoding="async" aria-describedby="caption-attachment-12999" class="size-medium wp-image-12999" src="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr-702x792.jpg" alt="demonstration of the iterative model of the emulator" width="702" height="792" srcset="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr-702x792.jpg 702w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr-260x293.jpg 260w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr-768x866.jpg 768w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr-600x677.jpg 600w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr-1362x1536.jpg 1362w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr-1320x1489.jpg 1320w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f5_hr.jpg 1797w" sizes="(max-width: 702px) 100vw, 702px" /></a><p id="caption-attachment-12999" class="wp-caption-text">Figure 2: A schematic of the giant-impact emulator in which a multi-planet system is broken into trios. The machine learning model shown in Figure 1 predicts the results of a collision in the most unstable trio, and the process repeats until a stable system is formed. [Lammers et al. 2024]</p></div><div id="attachment_12998" style="width: 712px" class="wp-caption aligncenter"><a href="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr.jpg" data-rel="lightbox-image-2" data-rl_title="Figure 3: A comparison of the properties of the N-body (red) and machine learning–based (blue) planetary systems resulting from giant impacts. The machine learning–based framework produces largely similar results except that machine learning tends to produce systems that are less dynamically excited (bottom right). [Lammers et al. 2024]" data-rl_caption="Figure 3: A comparison of the properties of the N-body (red) and machine learning–based (blue) planetary systems resulting from giant impacts. The machine learning–based framework produces largely similar results except that machine learning tends to produce systems that are less dynamically excited (bottom right). [Lammers et al. 2024]" title="Figure 3: A comparison of the properties of the N-body (red) and machine learning–based (blue) planetary systems resulting from giant impacts. The machine learning–based framework produces largely similar results except that machine learning tends to produce systems that are less dynamically excited (bottom right). [Lammers et al. 2024]"><img loading="lazy" decoding="async" aria-describedby="caption-attachment-12998" class="size-medium wp-image-12998" src="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr-702x334.jpg" alt="comparison of the properties of the N-body and machine learning–based planetary systems" width="702" height="334" srcset="https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr-702x334.jpg 702w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr-260x124.jpg 260w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr-768x365.jpg 768w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr-600x286.jpg 600w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr-1536x731.jpg 1536w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr-2048x975.jpg 2048w, https://aasnova.org/wp-content/uploads/2024/11/apjad7fe5f6_hr-1320x628.jpg 1320w" sizes="auto, (max-width: 702px) 100vw, 702px" /></a><p id="caption-attachment-12998" class="wp-caption-text">Figure 3: A comparison of the properties of the <em>N</em>-body (red) and machine learning–based (blue) planetary systems resulting from giant impacts. The machine learning–based framework produces largely similar results except that machine learning tends to produce systems that are less dynamically excited (bottom right). [Lammers et al. 2024]</p></div>In comparison with the <em>N</em>-body simulations, the machine learning–based emulator is ~10,000 times faster. The collision classifier model predicts probabilities with a scatter of ~10% and minimal offsets from the <em>N</em>-body-derived probabilities. The orbital outcome regressor predicts orbital configurations for the validation set almost at the accuracy limit set by chaos inherent in <em>N</em>-body dynamics.</p> <p>Planet formation is a messy, unsolved problem. Until the discovery of <a href="https://astrobites.org/2023/01/02/pulsar-planets/">exoplanetary systems</a>, we had only the solar system and its single formation outcome to study. It turns out that nature produces a beautiful mosaic of planetary systems, many of which bear little resemblance to our own. The giant-impact phase has long been a bottleneck in simulating planet formation due to computational constraints, and today’s article presents a truly exciting advancement in modeling planet–planet collisions. The model is limited in that it breaks multi-planet systems into trios of adjacent planets and models collisions and scattering only within trios, though the authors expect this is a minor effect. The <a href="https://github.com/CalebLammers/ML_for_collisions">model</a> is publicly available and will certainly expand the frontier of possible planet formation analyses.</p> <p><em>Original astrobite edited by Nathalie Korhonen Cuestas.</em></p> <blockquote> <h4 class="wpautbox-name">About the author, Kylee Carden:</h4> <p>I am a second-year PhD student at The Ohio State University, where I am an observer of planets outside the solar system. I’m involved with the Roman Space Telescope, a small robotic telescope called DEMONEXT, and exoplanet atmospheres. I am a huge fan of my cat Piccadilly, cycling, and visiting underappreciated tourist sites.</p></blockquote> <div class="tagcloud"><a href="https://aasnova.org/tag/machine-learning/" rel="tag">machine learning</a> <a href="https://aasnova.org/tag/planet-formation/" rel="tag">planet formation</a> <a href="https://aasnova.org/tag/planetary-systems/" rel="tag">planetary systems</a></div> </div> </div> </div> </article> <div class="comments"> <div id="comments"> <p class="nocomments"></p> </div> </div> </div> <aside class="col-4 sidebar"> <ul> <li id="bunyad-related-posts-widget-2" class="widget related-posts"> <h3 class="widgettitle">RELATED HIGHLIGHTS</h3> <ul class="posts-list"> <li> <a href="https://aasnova.org/2024/10/22/star-alignment-and-a-planets-origin-the-case-of-hip-65426/"><img width="110" height="96" src="https://aasnova.org/wp-content/uploads/2024/09/HIP65426-110x96.png" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="Infrared images of the exoplanet 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