Nasa-Funded Asteroid Discovery Research

Odalys Benitez

I worked under Dr. Carrie Nugent, improving their existing pipeline for discovering asteroids. Their research was centered around the NEAT dataset, an archive with thousands of images from different locations. With modern and sophisticated methods of image processing, we can discover thousands of new asteroids. My work consisted of obtaining images from the NEAT dataset, running them through image processing algorithms, and calibrating their parameters on space software for better asteroid/star recognition. The software we used was source extractor, scamp, and Aladin.

Sample outout of image processing with Source Extractor

By fine tuning the algorithms, I was able to obtain images like the one above, where multiple stars and asteroids are circled. A main challenge I faced was running into distortion and noise in the image, that can be due to multiple reasons like location and weather.

One of my Python scripts divides images into quadrants, to make it easier for the image-processing to customize its parameters across the image. While it does not account for all distortion cases, it made it easier for my successor to focus on star-removal algorithms and checking images with existing asteroid databases.

Read Dr. Carrie Nugent and co’s recent published paper