Planet Populations as a Function of Stellar Properties

Exoplanets around different types of stars provide a window into the diverse environments in which planets form. This chapter describes the observed relations between exoplanet populations and stellar properties and how they connect to planet formation in protoplanetary disks. Giant planets occur more frequently around more metal-rich and more massive stars. These findings support the core accretion theory of planet formation, in which the cores of giant planets form more rapidly in more metal-rich and more massive protoplanetary disks. Smaller planets, those with sizes roughly between Earth and Neptune, exhibit different scaling relations with stellar properties. These planets are found around stars with a wide range of metallicities and occur more frequently around lower mass stars. This indicates that planet formation takes place in a wide range of environments, yet it is not clear why planets form more efficiently around low mass stars. Going forward, exoplanet surveys targeting M dwarfs will characterize the exoplanet population around the lowest mass stars. In combination with ongoing stellar characterization, this will help us understand the formation of planets in a large range of environments.Comment: Accepted for Publication in the Handbook of Exoplanet

The interface between device and circuit design

A proper understanding of transistor and other circuit-element behavior is critical in the design process of integrated circuits intended for high-volume production or exacting performance standards. Models of such elements are a key ingredient in the circuit-simulation task, which provides design-verification feedback to chip designers. Failures in this process can have costly consequences. Much of the effort put into modelling work contributes very little to real needs as practical failures are usually at the much more gross level of user input or program-coding problems. </jats:p

Deep Learning Crater Detection for Lunar Terrain Relative Navigation

© 2020, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. Terrain relative navigation can improve the precision of a spacecraft’s position estimate by providing supplementary measurements to correct for drift in the inertial navigation system. This paper presents a system, LunaNet, that uses a convolutional neural network to detect craters from camera imagery taken by an onboard camera. These detections are matched with known lunar craters, and these matches can be used as landmarks for localization. The motivation for generating such landmarks is to provide relative location measurements to a navigation filter, however the details of such a navigation filter are not explored within this work. Our results show that on average LunaNet detects approximately twice the number of craters in an intensity image as two other intensity image-based crater detectors. One of the challenges of cameras is that they can generate imagery with vastly different appearances depending on image qualities and noise levels. Differences in image qualities and noise levels can occur for reasons such as changes in irradiance of the lunar surface, heating of camera electronic elements, or the inherent fluctuation of discrete photons. These image noise effects are difficult to compensate for, making it important for a crater detection system to be robust to them. Convolutional neural networks have been demonstrated to be robust to these kinds of imagery variation. LunaNet is shown to be robust to four types of image manipulation that result in changes to image qualities and noise levels of the input imagery