Development of an Automated Bolide Detection Pipeline for the GOES Geostationary Lightning Mapper

Abstract:

The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and 17 satellites has been shown to be capable of detecting bolides (bright meteors) in the atmosphere. Due to its large, continuous field of view and immediate public data availability, GLM provides a unique opportunity to detect a large variety of bolides, including those in the 0.1 to 3 m diameter range and complements current ground-based bolide detection systems, which are typically sensitive to larger events. We present the full end-to-end development lifecycle of a machine learning based bolide detection and light curve generation pipeline being developed at NASA Ames Research Center as part of NASA’s Asteroid Threat Assessment Project (ATAP). The goal is to generate a large catalog of calibrated bolide light curves to provide an unprecedented data set which will be used to inform meteor entry models on how incoming bodies interact with the atmosphere and to infer the pre-entry properties of the impacting bodies. Development of the training set, ML model training and iterative improvements in detection performance will be presented. The pipeline runs in an automated fashion and bolide light curves along other measured properties are promptly published on a NASA hosted publicly available website, https://neo-bolide.ndc.nasa.gov.