Ethan Lee is currently a Junior at Cornell University pursuing my degree in Physics. His goal is to use his degree to do physics and better understand the world around us. He also is interested in teaching Physics. Ethan was born and raised in Honolulu, HI and graduated from Punahou High School. In his free time, he enjoys surfing, competing in broccoli-eating contests, playing trumpet, and staying rat lung-worm free. He has always loved the sciences and especially physics and can’t wait to expand his knowledge in the field further.

Home Island: Oahu

High School: Punahou High School

Institution when accepted: Cornell University

Akamai Project: Characterizing Data Ramp Non-Linearity and Correcting Bad Pixels for CryoNIRSP

Project Site: Institute for Astronomy – Pukalani, Maui

Mentors: Dr. Jeff Kuhn & Dr. Andre Fehlmann

Project Abstract: 

The Institute for Astronomy on Maui is currently building CryoNIRSP, an instrument that will be placed inside the Daniel K. Inouye Solar Telescope for measuring the Sun’s magnetic field. In order to gather information on the Sun, the instrument uses a sensor which measures light intensity. This data is taken in the form of Non-Destructive-Reads, which read the voltage of the sensor without resetting the voltage of the sensor. Data taken in this way is called a ramp. My project involves finding characteristics of the linearity of these ramps, and learning more about the characteristics of different pixels. We are characterizing the linearity of the ramps through measurements of linearity over simulated changes in brightness level, and hope that this characterization of the linearity also distinguishes defective pixels. We are also attempting to remove the non-linear part of the ramp using a threshold created through observations of ramp linearity. Results of our ramp and pixel studies will be presented. Eventually, by characterizing the linearity of the ramps of both good and bad pixels, we hope to gain the ability to distinguish bad pixels and also establish criteria for correcting for the non-linearity of all the ramps, and through this, improve the accuracy of the data taken with CryoNIRSP.