Week 0: No One Likes Meteorites

Hello earthly specimen! I know my title and abstract are a little jargony, but my project essentially boils down to — finding the asteroids that can hit Earth fast! Asteroids that have a possibility of hitting Earth are labeled as near-Earth objects or (NEOs). More specifically, asteroids are classified as NEOs if their distance of closest approach to the Sun is less than 1.3 AU (where AU is the distance from the Earth to the Sun). According to the B612 Foundation, a nonprofit foundation dedicated to planetary science and planetary defense, only 0.1% of an estimated 3 million NEOs have been detected. Now that’s scary! Many of these NEOs remain undetected due to their small size as well as their high rate of motion. 

NASA recently completed its Double Asteroid Redirection Test (DART) mission, proving that we can effectively divert asteroids headed on a collision course with Earth. All we gotta do now is find those suckers! And I think I might just have the right tools and insight to do so. My solution contains three main components:

1. simulating training data
2. developing an improved machine learning model for detecting near-Earth objects in telescope data
3. innovating an improved post-processing pipeline to streamline manual identification and verification of detected streaks.

Fast-moving NEOs that pass dangerously close to Earth leave a streak in telescope exposures. I’ll be trying to identify these streaks in telescope data from the Zwicky Transient Facility (ZTF). I will utilize convolutional neural networks (CNNs), a leading algorithm in computer vision, to identify streaks in telescope images from the ZTF database. CNNs and computer vision algorithms in general require a lot of training data to accurately classify new data. Since there is a limited number of confirmed detections of near-Earth objects, I will simulate training data streaks using the Gaussian Point Spread Function. This allows me to both have a larger training dataset as well as target specific demographics of streaks (e.g. short/faint streaks). 

Current algorithms for near-Earth object detection utilize the EfficientNet architecture, a high-performing, popular model for computer vision, but EfficientNet is specialized for processing 3-channel RGB images while data from ZTF is black-and-white. Using the standard Conv2D layers that Keras, a popular machine learning library, offers, I hope to develop a more lightweight and accurate model to process telescope data. 

Lastly, when the model finishes processing through full nights of data, it will output a bunch of images in which it thinks there is a streak. Going through these one-by-one manually is both challenging and time-consuming. I want to code up some type of interface to make the process more intuitive and efficient.

And yeah, that’s my Senior Project as well as the objectives I hope to complete. I’ll see you all next week!