NEARBY Platform: Algorithm for Automated Asteroids Detection in Astronomical Images

TL;DR

The paper introduces the NEARBY platform's automated algorithm for detecting Near Earth Objects in astronomical images, enhancing discovery capabilities through big data processing and visual analytics.

Contribution

It presents a novel NEO detection algorithm integrated into an automated pipeline, improving the speed and accuracy of asteroid discovery.

Findings

Successfully identified multiple NEOs in test datasets

Enhanced detection speed through automation and big data techniques

Facilitated rapid human validation with visual analytics

Abstract

In the past two decades an increasing interest in discovering Near Earth Objects has been noted in the astronomical community. Dedicated surveys have been operated for data acquisition and processing, resulting in the present discovery of over 18.000 objects that are closer than 30 million miles of Earth. Nevertheless, recent events have shown that there still are many undiscovered asteroids that can be on collision course to Earth. This article presents an original NEO detection algorithm developed in the NEARBY research object, that has been integrated into an automated MOPS processing pipeline aimed at identifying moving space objects based on the blink method. Proposed solution can be considered an approach of Big Data processing and analysis, implementing visual analytics techniques for rapid human data validation.