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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.17359 |
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| _version_ | 1866913668848943104 |
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| author | Chen, Kaitlyn Cardenas, Oswaldo Bonifacio, Brandon Hall, Nikolas Kang, Rori Tamayo, Daniel |
| author_facet | Chen, Kaitlyn Cardenas, Oswaldo Bonifacio, Brandon Hall, Nikolas Kang, Rori Tamayo, Daniel |
| contents | 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 $\sim 3$ million CPU-hours through direct N-body integrations, but was achieved with only $\approx 50$ CPU-hours by removing unstable configurations using the Stability of Planetary Orbital Configurations Klassifier (SPOCK) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Carving Out the Inner Edge of the Period Ratio Distribution through Giant Impacts Chen, Kaitlyn Cardenas, Oswaldo Bonifacio, Brandon Hall, Nikolas Kang, Rori Tamayo, Daniel Earth and Planetary Astrophysics 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 $\sim 3$ million CPU-hours through direct N-body integrations, but was achieved with only $\approx 50$ CPU-hours by removing unstable configurations using the Stability of Planetary Orbital Configurations Klassifier (SPOCK) 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. |
| title | Carving Out the Inner Edge of the Period Ratio Distribution through Giant Impacts |
| topic | Earth and Planetary Astrophysics |
| url | https://arxiv.org/abs/2501.17359 |