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Register a new userRealistic On-the-fly Outcomes of Planetary Collisions II: Bringing Machine Learning to N-body Simulations
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Added by
Alexandre Emsenhuber
Publication date
2020
fields
Physics
and research's language is
English
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Terrestrial planet formation theory is at a bottleneck, with the growing realization that pairwise collisions are treated far too simply. Here, and in our companion paper (Cambioni et al. 2019) that introduces the training methodology, we demonstrate the first application of machine learning to more realistically model the late stage of planet formation by giant impacts. We present surrogate models that give fast, reliable answers for the masses and velocities of the two largest remnants of a giant impact, as a function of the colliding masses and their impact velocity and angle, with the caveat that our training data do not yet include pre-impact rotation or variable thermal conditions. We compare canonical N-body scenarios of terrestrial planet formation assuming perfect merger (Chambers 2001) with our more realistic treatment that includes inefficient accretions and hit-and-run collisions. The result is a protracted tail of final events lasting ~200 Myr, and the conversion of about half the mass of the initial population to debris. We obtain profoundly different solar system architectures, featuring a much wider range of terrestrial planet masses and enhanced compositional diversity.
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We describe a major upgrade of a Monte Carlo code which has previously been used for many studies of dense star clusters. We outline the steps needed in order to calibrate the results of the new Monte Carlo code against $N$-body simulations for large $N$ systems, up to $N=200000$. The new version of the Monte Carlo code (called MOCCA), in addition to the features of the old version, incorporates the direct Fewbody integrator (Fregeau et al. 2004) for three- and four-body interactions, and a new treatment of the escape process based on Fukushige & Heggie (2000). Now stars which fulfil the escape criterion are not removed immediately, but can stay in the system for a certain time which depends on the excess of the energy of a star above the escape energy. They are called potential escapers. With the addition of the Fewbody integrator the code can follow all interaction channels which are important for the rate of creation of various types of objects observed in star clusters, and ensures that the energy generation by binaries is treated in a manner similar to the $N$-body model. There are at most three new parameters which have to be adjusted against $N$-body simulations for large $N$: two (or one, depending on the chosen approach) connected with the escape process, and one responsible for the determination of the interaction probabilities. The values adopted for the free parameters have at most a weak dependence on $N$. They allow MOCCA to reproduce $N$-body results with reasonable precision, not only for the rate of cluster evolution and the cluster mass distribution, but also for the detailed distributions of mass and binding energy of binaries. Additionally, the code can follow the rate of formation of blue stragglers and black hole - black hole binaries.
Photometric galaxy surveys constitute a powerful cosmological probe but rely on the accurate characterization of their redshift distributions using only broadband imaging, and can be very sensitive to incomplete or biased priors used for redshift calibration. Sanchez & Bernstein (2019) presented a hierarchical Bayesian model which estimates those from the robust combination of prior information, photometry of single galaxies and the information contained in the galaxy clustering against a well-characterized tracer population. In this work, we extend the method so that it can be applied to real data, developing some necessary new extensions to it, especially in the treatment of galaxy clustering information, and we test it on realistic simulations. After marginalizing over the mapping between the clustering estimator and the actual density distribution of the sample galaxies, and using prior information from a small patch of the survey, we find the incorporation of clustering information with photo-$z$s to tighten the redshift posteriors, and to overcome biases in the prior that mimic those happening in spectroscopic samples. The method presented here uses all the information at hand to reduce prior biases and incompleteness. Even in cases where we artificially bias the spectroscopic sample to induce a shift in mean redshift of $Delta bar z approx 0.05,$ the final biases in the posterior are $Delta bar z lesssim0.003.$ This robustness to flaws in the redshift prior or training samples would constitute a milestone for the control of redshift systematic uncertainties in future weak lensing analyses.
We present a ray tracing code to compute integrated cosmological observables on the fly in AMR N-body simulations. Unlike conventional ray tracing techniques, our code takes full advantage of the time and spatial resolution attained by the N-body simulation by computing the integrals along the line of sight on a cell-by-cell basis through the AMR simulation grid. Moroever, since it runs on the fly in the N-body run, our code can produce maps of the desired observables without storing large (or any) amounts of data for post-processing. We implemented our routines in the RAMSES N-body code and tested the implementation using an example of weak lensing simulation. We analyse basic statistics of lensing convergence maps and find good agreement with semi-analytical methods. The ray tracing methodology presented here can be used in several cosmological analysis such as Sunyaev-Zeldovich and integrated Sachs-Wolfe effect studies as well as modified gravity. Our code can also be used in cross-checks of the more conventional methods, which can be important in tests of theory systematics in preparation for upcoming large scale structure surveys.
During the late stage of planet formation when Mars-size cores appear, interactions among planetary cores can excite their orbital eccentricities, speed their merges and thus sculpture the final architecture of planet systems. This series of work contributes to the final assembling of planet systems with N-body simulations, including the type I and II migration of planets, gas accretion of massive cores in a viscous disk. In this paper, the standard formulations of type I and II migrations are adopted to investigate the formation of planet systems around solar mass stars. Statistics on the final distributions of planetary masses, semi-major axes and eccentricities are derived, which are comparable to those of the observed systems. Our simulations predict some orbital signatures of planet systems around solar mass stars: 36% of the survival planets are giant planets (Mp>10Me). Most of the massive giant planets (Mp>30Me) locate at 1-10AU. Terrestrial planets distribute more or less evenly at <1-2 AU. Planets in inner orbits (<1 AU) may accumulate at the inner edges of either the protostellar disk (3-5 days) or its MRI dead zone (30-50 days). There is a planet desert in the mass-eccecntricity diagram, i.e., lack of planets with masses 0.005 - 0.08 MJ in highly eccentric orbits (e > 0.3 - 0.4). The average eccentricity (~ 0.15) of the giant planets (Mp>10Me) are bigger than that (~ 0.05) of the terrestrial planets (Mp< 10Me). A planet system with more planets tends to have smaller planet masses and orbital eccentricities on average.
The population of exoplanetary systems detected by Kepler provides opportunities to refine our understanding of planet formation. Unraveling the conditions needed to produce the observed exoplanets will sallow us to make informed predictions as to where habitable worlds exist within the galaxy. In this paper, we examine using N-body simulations how the properties of planetary systems are determined during the final stages of assembly. While accretion is a chaotic process, trends in the ensemble properties of planetary systems provide a memory of the initial distribution of solid mass around a star prior to accretion. We also use EPOS, 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, either via 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 has a profound impact on the observed planetary architectures, and thus on the formation and delivery of volatiles to possible habitable worlds.
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