Looking to the Future — Google / Navigating Tomorrow

About Ron Medford Ron Medford joined Google in January 2013 as its Safety Director for the Self-Driving Car Program. In this position Ron leads the program’s safety team that is working with other program development teams to ensure the safety of the self-driving car. Prior to taking on this role at Google, Mr. Medford served as the Deputy Administrator of the National Highway Traffic Safety Administration (NHTSA), U.S. Department of Transportation from January 2010 until December 2012. Ron began his career at NHTSA in May 2003 as the Senior Associate Administrator for Vehicle Safety. In this position, he was responsible for overseeing all aspects of the U.S. auto safety programs. This included vehicle safety research, regulations, enforcement as well as the National Center for Statistics and Analysis. Before joining NHTSA, Mr. Medford was the Assistant Executive Director for Hazard Identification & Reduction at the U.S. Consumer Product Safety Commission (CPSC). He was responsible for the regulatory and technical work of the Agency, such as overseeing the Directorates for Engineering Sciences, Health Sciences, Epidemiology, Economic Analysis as well as the Agency’s Chemistry and Engineering Laboratories. Mr. Medford spent more than 25 years in a variety of technical management positions at the CPSC. Just prior to joining NHTSA, Mr. Medford spent 10 months on a government-sponsored sabbatical to work with Dean Kamen, an inventor from Manchester, New Hampshire. Mr. Kamen is president of Deka Research and Development Corporation and is known for his inventions of the IBOTTM wheel chair and the SegwayTM Human Transporter (HT). Mr. Medford holds a B.S. and M.S. from the University of Maryland

Removing Atmospheric Fringes from Zwicky Transient Facility i-Band Images using Principal Component Analysis

The Zwicky Transient Facility is a time-domain optical survey that has substantially increased our ability to observe and construct massive catalogs of astronomical objects by use of its 47 square degree camera that can observe in multiple filters. However the telescope's i-band filter suffers from significant atmospheric fringes that reduce photometric precision, especially for faint sources and in multi-epoch co-additions. Here we present a method for constructing models of these atmospheric fringes using Principal Component Analysis that can be used to identify and remove these image artifacts from science images. In addition, we present the Uniform Background Indicator as a quantitative measurement of correlated background noise and its relationship to reduced photometric error after removing fringes. We conclude by evaluating the effect of our method on measuring faint sources through the injection and recovery of artificial faint sources in both single-image epochs and co-additions. Our method for constructing atmospheric fringe models, and using those models for cleaning images, is available for public download in the open source python package fringez.Comment: 12 pages, 10 figures, submitted to PAS