Exoplanet atmosphere evolution: emulation with neural networks
File(s)2110.15162.pdf (3.16 MB)
Accepted version
Author(s)
Rogers, James G
Munoz, Claudia Jano
Owen, James E
Makinen, T Lucas
Type
Journal Article
Abstract
Atmospheric mass-loss is known to play a leading role in sculpting the demographics of small, close-in exoplanets. Knowledge of how such planets evolve allows one to ‘rewind the clock’ to infer the conditions in which they formed. Here, we explore the relationship between a planet’s core mass and its atmospheric mass after protoplanetary disc dispersal by exploiting XUV photoevaporation as an evolutionary process. Historically, this inference problem would be computationally infeasible due to the large number of planet models required; however, we use a novel atmospheric evolution emulator which utilizes neural networks to provide three orders of magnitude in speedup. First, we provide a proof of concept for this emulator on a real problem by inferring the initial atmospheric conditions of the TOI-270 multi-planet system. Using the emulator, we find near-indistinguishable results when compared to the original model. We then apply the emulator to the more complex inference problem, which aims to find the initial conditions for a sample of Kepler, K2, and TESS planets with well-constrained masses and radii. We demonstrate that there is a relationship between core masses and the atmospheric mass they retain after disc dispersal. This trend is consistent with the ‘boil-off’ scenario, in which close-in planets undergo dramatic atmospheric escape during disc dispersal. Thus, it appears that the exoplanet population is consistent with the idea that close-in exoplanets initially acquired large massive atmospheres, the majority of which is lost during disc dispersal, before the final population is sculpted by atmospheric loss over 100 Myr to Gyr time-scales.
Date Issued
2023-03
Date Acceptance
2023-01-05
Citation
Monthly Notices of the Royal Astronomical Society, 2023, 519 (4), pp.6028-6043
ISSN
0035-8711
Publisher
Oxford University Press
Start Page
6028
End Page
6043
Journal / Book Title
Monthly Notices of the Royal Astronomical Society
Volume
519
Issue
4
Copyright Statement
Copyright © 2023 Oxford University Press. This is a pre-copy-editing, author-produced version of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version James G Rogers, Clàudia Janó Muñoz, James E Owen, T Lucas Makinen, Exoplanet atmosphere evolution: emulation with neural networks, Monthly Notices of the Royal Astronomical Society, Volume 519, Issue 4, March 2023, Pages 6028–6043 is available online at: https://doi.org/10.1093/mnras/stad089
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000922683300004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
Astronomy & Astrophysics
DETERMINISTIC MODEL
GIANT PLANETS
HOT
IN SUPER-EARTHS
LONG-PERIOD
MINI-NEPTUNE
Physical Sciences
planet star interactions
planets and satellites: atmospheres
planets and satellites: physical evolution
POWERED MASS-LOSS
RADIUS DISTRIBUTION
Science & Technology
SUB-NEPTUNES
TERRESTRIAL PLANET
Publication Status
Published
Date Publish Online
2023-01-11