Kaitlyn Chen, Oswaldo Cardenas, Brandon Bonifacio, Nikolas Hall, Rori Kang and Daniel Tamayo
{"title":"Carving Out the Inner Edge of the Period Ratio Distribution through Giant Impacts","authors":"Kaitlyn Chen, Oswaldo Cardenas, Brandon Bonifacio, Nikolas Hall, Rori Kang and Daniel Tamayo","doi":"10.3847/1538-4357/adae8a","DOIUrl":null,"url":null,"abstract":"The distribution of orbital period ratios between adjacent observed exoplanets is approximately uniform, but exhibits a strong falloff toward close orbital separations. We show that this falloff can be explained through past dynamical instabilities carving out the period ratio distribution. Our suite of numerical experiments would have required ∼3 million CPU hr through direct N-body integrations, but was achieved with only ≈50 CPU hr by removing unstable configurations using the Stability of Planetary Orbital Configurations Klassifier machine learning model. This highlights the role of dynamical instabilities in shaping the observed exoplanet population, and shows that the inner part of the period ratio distribution provides a valuable observational anchor on the giant impact phase of planet formation.","PeriodicalId":501813,"journal":{"name":"The Astrophysical Journal","volume":"125 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Astrophysical Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3847/1538-4357/adae8a","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The distribution of orbital period ratios between adjacent observed exoplanets is approximately uniform, but exhibits a strong falloff toward close orbital separations. We show that this falloff can be explained through past dynamical instabilities carving out the period ratio distribution. Our suite of numerical experiments would have required ∼3 million CPU hr through direct N-body integrations, but was achieved with only ≈50 CPU hr by removing unstable configurations using the Stability of Planetary Orbital Configurations Klassifier machine learning model. This highlights the role of dynamical instabilities in shaping the observed exoplanet population, and shows that the inner part of the period ratio distribution provides a valuable observational anchor on the giant impact phase of planet formation.