AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping

TL;DR

This paper presents an AI-powered cloud-based system for automating data processing, visualization, and public engagement in meteor shower mapping, leading to over 200 new discoveries and validation of known showers.

Contribution

It introduces an automated AI pipeline and interactive web portal that enhance meteor data analysis and discovery efforts in the CAMS project.

Findings

Discovered over 200 new meteor showers.

Validated dozens of previously reported showers.

Improved data processing efficiency and public engagement.

Abstract

The Cameras for Allsky Meteor Surveillance (CAMS) project, funded by NASA starting in 2010, aims to map our meteor showers by triangulating meteor trajectories detected in low-light video cameras from multiple locations across 16 countries in both the northern and southern hemispheres. Its mission is to validate, discover, and predict the upcoming returns of meteor showers. Our research aimed to streamline the data processing by implementing an automated cloud-based AI-enabled pipeline and improve the data visualization to improve the rate of discoveries by involving the public in monitoring the meteor detections. This article describes the process of automating the data ingestion, processing, and insight generation using an interpretable Active Learning and AI pipeline. This work also describes the development of an interactive web portal (the NASA Meteor Shower portal) to facilitate the visualization of meteor radiant maps. To date, CAMS has discovered over 200 new meteor showers and has validated dozens of previously reported showers.