Browsing UA Faculty Research by Subjects
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Earths in Other Solar Systems’ N-body Simulations: The Role of Orbital Damping in Reproducing the Kepler Planetary SystemsThe population of exoplanetary systems detected by Kepler provides opportunities to refine our understanding of planet formation. Unraveling the conditions needed to produce the observed exoplanet systems will allow us to make informed predictions as to where habitable worlds exist within the galaxy. In this paper, we examine, usingN-body simulations, how the properties of planetary systems are determined during the final stages of assembly, when planets accrete from embryos and planetesimals. While accretion is a chaotic process, trends emerge allowing certain features of an ensemble of planetary systems to provide a memory of the initial distribution of solid mass around a star prior to accretion. We also useepos, the Exoplanet Population Observation Simulator, to account for detection biases and show that different accretion scenarios can be distinguished from observations of the Kepler systems. We show that the period of the innermost planet, the ratio of orbital periods of adjacent planets, and masses of the planets are determined by the total mass and radial distribution of embryos and planetesimals at the beginning of accretion. In general, some amount of orbital damping, via either planetesimals or gas, during accretion is needed to match the whole population of exoplanets. Surprisingly, all simulated planetary systems have planets that are similar in size, showing that the "peas in a pod" pattern can be consistent with both a giant impact scenario and a planet migration scenario. The inclusion of material at distances larger than what Kepler observes (>1 au) has a profound impact on the observed planetary architectures and thus on the formation and delivery of volatiles to possible habitable worlds.
A Search for Multiplanet Systems with TESS Using a Bayesian N-body Retrieval and Machine LearningTransiting exoplanets in multiplanet systems exhibit non-Keplerian orbits as a result of the gravitational influence from companions, which can cause the times and durations of transits to vary. The amplitude and periodicity of the transit time variations are characteristic of the perturbing planet's mass and orbit. The objects of interest from the Transiting Exoplanet Survey Satellite (TESS) are analyzed in a uniform way to search for transit timing variations (TTVs) with sectors 1–3 of data. Due to the volume of targets in the TESS candidate list, artificial intelligence is used to expedite the search for planets by vetting nontransit signals prior to characterizing the light-curve time series. The residuals of fitting a linear orbit ephemeris are used to search for TTVs. The significance of a perturbing planet is assessed by comparing the Bayesian evidence between a linear and nonlinear ephemeris, which is based on an N-body simulation. Nested sampling is used to derive posterior distributions for the N-body ephemeris and in order to expedite convergence, custom priors are designed using machine learning. A dual-input, multi-output convolutional neural network is designed to predict the parameters of a perturbing body given the known parameters and measured perturbation (O − C). There is evidence for three new multiplanet candidates (WASP-18, WASP-126, TOI 193) with nontransiting companions using the two-minute cadence observations from TESS. This approach can be used to identify stars in need of longer radial velocity and photometric follow-up than those already performed.
Superparticle Method for Simulating CollisionsFor problems in astrophysics, planetary science, and beyond, numerical simulations are often limited to simulating fewer particles than in the real system. To model collisions, the simulated particles (a.k.a. superparticles) need to be inflated to represent a collectively large collisional cross section of real particles. Here we develop a superparticle-based method that replicates the kinetic energy loss during real-world collisions, implement it in an N-body code, and test it. The tests provide interesting insights into dynamics of self-gravitating collisional systems. They show how particle systems evolve over several freefall timescales to form central concentrations and equilibrated outer shells. The superparticle method can be extended to account for the accretional growth of objects during inelastic mergers.