Machine Learning and Instrument Autonomy Group

The Data Ordering Genetic Optimization (DOGO) can tell you the mission data to trust well, little, or not at all.

Principal Investigator

Lukas Mandrake

DOGO was developed on the Orbiting Carbon Observatory 2 (OCO-2) mission to identify features that predict when the received CO2 goes awry. It was later adopted to advise science users on the trustability of individual observations in the mission record via the Warn Level formula: a simple prescription that assigns each observation an integer from 0 to 9 estimating how much confounding forces are present. DOGO's greatest significance is its ability to predict data trust without the need for ground truth data, instead using a series of expert-derived sanity checks on the data to narrow down on the best and worse behaved observations. DOGO is currently applying to AMMOS for implementation funds to spread its technology to other future missions.