Hot Electrons and Radial Transport in Saturn’s Inner Magnetosphere: Modeling the Effects on Ion Chemistry

The E-ring of Saturn, located just beyond the main rings at four Saturn radii, was known to be made mostly of water and its by-products before the Cassini spacecraft arrived at Saturn in 2005. Since then, Cassini has observed water geysers on the tiny moon of Enceladus ejecting ≈ 100 kg of water per second into orbit around Saturn, which most agree is the chief contributor to neutrals in the E-ring. Following several key reactions, many of these neutrals go on to populate large, tenuous structures, known as neutral clouds, extending 10s of Saturn radii.The other side of the story are the ions, which are largely created by the ionization of same neutrals sourced from Enceladus. A key distinction between the neutrals and ions is that ions are carried along by Saturn’s magnetic field, and revolve around Saturn at the rotation rate of the planet, while neutrals generally have much slower Keplerian speeds.It is the study of the chemical interaction of these separate, but related populations that is the subject of this thesis. We have developed a series of models to study how the coupling of these systems affect details of the other, such as composition.The first step (Chapter 2) was the development of a water-group physical chemistry model, which includes suprathermal electrons and the effect of radial ion transport. With this “one-box” model, we are able to reproduce observed water and hydrogen ion densities in Enceladus’s orbit, but only when the hot electron density is ≈ 0.5% of the total plasma density. Radial transport is found to be slow, requiring 26 days to remove ions from the orbit of Enceladus.Moving toward the development of a radial model of ion chemistry, in Chapter 4 we present a model of Saturn’s neutral clouds, which are made of material outgassing from Enceladus. The effects of dissociation and charge exchange are considered, where the details of the latter prove to be of great consequence on neutral cloud morphology. The oxygen cloud is found to the most extended, followed by H2O, and finally OH.The above efforts are combined in Chapter 5, where a neutral cloud model is used to construct a radial model of ion chemistry. It is shown that neutral H2O requires more spreading than yet modeled in order to recover observed water and hydrogen ion abundances near Enceladus. The relative abundance of water-group ion species presented will be useful for analyses of CAPS-IMS data, while loss rates derived from the model can be used to improve neutral cloud models. The case is made that ion chemistry models and neutral cloud models must be developed alongside one another in order to improve understanding of these interrelated populations at Saturn

Reduced convergence and the local smoothness parameter: bridging two different descriptions of weak lensing amplification

Weak gravitational lensing due to the inhomogeneous matter distribution in the universe is an important systematic uncertainty in the use of standard candles in cosmology. There are two different descriptions of weak lensing amplification, one uses a local smoothness parameter $\tilde{\alpha}$, the other uses reduced convergence $\eta= 1+ \kappa/|\kappa_{min}|$ (where $\kappa$ is convergence). The $\tilde{\alpha}$ description involves Dyer-Roeder distance $D_A(\tilde{\alpha}|z)$ ($\tilde{\alpha}=1$ corresponds to a smooth universe); it is simple and convenient, and has been used by the community to illustrate the effect of weak lensing on point sources such as type Ia supernovae. Wang (1999) has shown that the $\tilde{\alpha}$ description can be made realistic by allowing $\tilde{\alpha}$ to be a local variable, the local smoothness parameter. The $\eta$ description has been used by Wang, Holz, & Munshi (2002) to derive a universal probability distribution (UPDF) for weak lensing amplification. In this paper, we bridge the two different descriptions of weak lensing amplification by relating the reduced convergence $\eta$ and the local smoothness parameter $\tilde{\alpha}$. We give the variance of $\tilde{\alpha}$ in terms of the matter power spectrum, thus providing a quantitative guidance to the use of Dyer-Roeder distances in illustrating the effect of weak lensing. The by-products of this work include a corrected definition of the reduced convergence, and simple and accurate analytical expressions for $D_A(\tilde{\alpha}|z)$. Our results should be very useful in studying the weak lensing of standard candles.Comment: Revised and expanded version. ApJ accepte

Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection

Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may exceed $100$ MB, which corresponds to a time series with $T=100,000,000$ steps. To date, the closest approach to handling such a task is MalConv, a convolutional neural network capable of processing up to $T=2,000,000$ steps. The $\mathcal{O}(T)$ memory of CNNs has prevented further application of CNNs to malware. In this work, we develop a new approach to temporal max pooling that makes the required memory invariant to the sequence length $T$. This makes MalConv $116\times$ more memory efficient, and up to $25.8\times$ faster to train on its original dataset, while removing the input length restrictions to MalConv. We re-invest these gains into improving the MalConv architecture by developing a new Global Channel Gating design, giving us an attention mechanism capable of learning feature interactions across 100 million time steps in an efficient manner, a capability lacked by the original MalConv CNN. Our implementation can be found at https://github.com/NeuromorphicComputationResearchProgram/MalConv2Comment: To appear in AAAI 202