Characterizing Electrons in Primary and Secondary Magnetic Islands During Magnetic Reconnection

The physics underlying particle-in-cell simulations that are widely employed in studying plasma dynamics are reviewed. Results from a two-dimensional particle-in-cell simulation of fully kinetic, undriven, collisionless magnetic reconnection are studied to compare the electrons in a primary magnetic island formed from an ion current sheet and the electrons in a secondary island formed in an electron current layer. We find that the secondary island is born with a strong out-of-plane current density due to localized peaks in the electron density and out-of-plane electron velocity; the secondary island retains these features as it evolves, distinguishing it from the primary island. For the first time distinct features in electron velocity distributions are established for both types of islands. These magnetic island comparisons and their connection to in situ Cluster observations are analysis techniques valuable to NASA’s Magnetospheric Multiscale mission to launch in 2014 which is capable of resolving the various types of electron distributions discussed in this thesis

Smoking-Gun Observables of Magnetic Reconnection: Spatiotemporal Evolution of Electron Characteristics Throughout the Diffusion Region

How does magnetic reconnection happen in a collisionless plasma? Knowledge of electron-scale dynamics is necessary to answer this outstanding question of plasma physics. Based on fully kinetic particle-in-cell (PIC) simulations of symmetric reconnection, the spatiotemporal evolution of velocity distribution functions in and around the electron diffusion region (EDR) elucidates how electrons are accelerated and heated by the cooperating reconnection electric and normal magnetic fields. The discrete, triangular structures characteristic of EDR distributions rotate and gyrotropize in velocity space as electrons remagnetize, forming multicomponent arc and ring structures. Further downstream, exhaust electrons are found to exhibit highly structured, time-dependent anisotropies that can be used to infer the temporal stage of reconnection. Cluster spacecraft measurements from a magnetotail reconnection exhaust region agree with these simulation predictions. In PIC simulations of asymmetric reconnection, EDR distributions acquire crescent-shaped populations, indicative of accelerated magnetosheath electrons mixing with electrons of magnetospheric origin. NASA’s successfully launched Magnetospheric Multiscale (MMS) mission caught an EDR at the magnetopause and confirmed the signature crescent electron populations. A virtual spacecraft trajectory through the PIC domain is determined quantitatively by inputting MMS magnetic field measurements into an algorithm that outputs a trajectory along which the input measurements are matched. The crescent structures observed by MMS in the EDR are consistent with the simulation distributions at the corresponding time along the computed trajectory. This work demonstrates that electron characteristics can serve as “smoking-gun” observables of the EDR at the heart of the magnetic reconnection mystery

DIRECTOR: Generator-Classifiers For Supervised Language Modeling

Current language models achieve low perplexity but their resulting generations still suffer from toxic responses, repetitiveness and contradictions. The standard language modeling setup fails to address these issues. In this paper, we introduce a new architecture, {\sc Director}, that consists of a unified generator-classifier with both a language modeling and a classification head for each output token. Training is conducted jointly using both standard language modeling data, and data labeled with desirable and undesirable sequences. Experiments in several settings show that the model has competitive training and decoding speed compared to standard language models while yielding superior results, alleviating known issues while maintaining generation quality. It also outperforms existing model guiding approaches in terms of both accuracy and efficiency