Dynamical origin of Theia, the last giant impactor on Earth

Cosmochemical studies have proposed that Earth accreted roughly 5-10% of its mass from carbonaceous (CC) material, with a large fraction delivered late via its final impactor, Theia (the Moon-forming impactor). Here, we evaluate this idea using dynamical simulations of terrestrial planet formation, starting from a standard setup with a population of planetary embryos and planetesimals laid out in a ring centered between Venus and Earth's orbits, and also including a population of CC planetesimals and planetary embryos scattered inward by Jupiter. We find that this scenario can match a large number of constraints, including i) the terrestrial planets' masses and orbits; ii) the CC mass fraction of Earth; iii) the much lower CC mass fraction of Mars, as long as Mars only accreted CC planetesimals (but no CC embryos); iv) the timing of the last giant (Moon-forming) impact; and v) a late accretion phase dominated by non-carbonaceous (NC) bodies. For this scenario to work, the total mass in scattered CC objects must have been ~ 0.2 - 0.3 M$_{\oplus}$ , with an embryo-to-planetesimal mass ratio of at least 8, and CC embryos in the ~ 0.01 - 0.05 M$_{\oplus}$ mass range. In that case, our simulations show there are roughly 50-50 odds of Earth's last giant impactor (Theia) having been a carbonaceous object - either a pure CC embryo or an NC embryo that previously accreted a CC embryo. Our simulations thus provide dynamical validation of cosmochemical studies.

RigaBrain®: Uzlabo smadzeņu darbību ar neurofeedback NeurOptimal® Rīgā

Trenē savas smadzenes tā, lai tās spētu pašorganizēties! - Lielākais blogs latviešu valodā par un ap smadzeņu darbību! Koncentrēšanās, miegs, atmiņa, personības attīstība, pašattīstība. RigaBrain smadzeņu treniņu centrs, neurofeedback, NeurOptimal

RigaBrain®: Uzlabo smadzeņu darbību ar neurofeedback NeurOptimal® Rīgā

Trenē savas smadzenes tā, lai tās spētu pašorganizēties! - Lielākais blogs latviešu valodā par un ap smadzeņu darbību! Koncentrēšanās, miegs, atmiņa, personības attīstība, pašattīstība. RigaBrain smadzeņu treniņu centrs, neurofeedback, NeurOptimal

Reconstructing higher-order interactions in coupled dynamical systems - Nature Communications

Higher-order interactions are broadly present in biological and social networks, however patterns of such interaction are challenging to recover from observed data. The authors propose a method to infer the high-order structural connectivity of a complex system from its time evolution.

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics

Context Changes Everything

From the influential author of Dynamics in Action, how the concepts of constraints provide a way to rethink relationships, opening the way to intentional, me...

AMMCS 2023 | SS-ASDEDS - Algebraic Structure of Discrete-Event Dynamical Systems, and Applications | August 14-18, 2023

Spectral learning of Bernoulli linear dynamical systems models for...

Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety...

Unifying physical systems’ inductive biases in neural ODE using...

Conservation of energy is at the core of many physical phenomena and dynamical systems. There have been a significant number of works in the past few years aimed at predicting the trajectory of...