Week 2: The Modern David and Goliath — Small, Lightweight AI Model vs. Big, Bad Asteroid
March 7, 2024
Hello my fellow cosmic mariners helplessly stranded on this spherical rock we call Earth! So, brief rundown: big, bad asteroid zooms around in space; I try to find big, bad asteroid with little AI model; but, oh no, not enough training data to teach little AI model to be smart enough to find big, bad asteroid; but, oh yes, I will “deepfake” asteroid images to teach little AI model how to find big, bad asteroid.
So last week I successfully generated the “deepfake” asteroid training images to train the little AI model, now I need to build the little AI model. Before going into the details of the type of model I’m going to be using, let’s first talk about the objective that the AI is trying to complete.
The AI will be fed two 80 by 80 pixel, black and white photos of the night sky, one reference image and one science image. The science image is a single snapshot of the night sky at a certain time, which can possibly include an image of an asteroid. On the other hand, the reference image is a bit more complicated, but it can be thought of as an average/median of multiple science images, which removes the possibility of having any transients in the image (e.g. if an asteroid were to pass through that region of the night sky in one science image, it is highly unlikely to remain in the same position throughout other science images, and when the median is taken, the image with a streak will be considered an outlier and eliminated).
The reference images are provided by the Zwicky Transient Facility (which is the observatory I’m acquiring all my data from). Below is an example of the data being fed into the machine learning model (on the left is a science image which happens to have an asteroid streak in it and on the right is the reference image).
In this case, I want my machine learning algorithm to output 1, or “yes there is an asteroid in this image.” In the case where the science image does not have a streak, and the science image and reference are similar, I want the algorithm to output 0, or “no there is no asteroid in this image.” This may seem like a pretty simple task, but due to noise, cloud coverage, and heat fluctuations in air, comparing the two images becomes a much more complex problem.
This type of image classification task has recently been dominated by a machine learning algorithm called a Convolutional Neural Network. And Convolutional Neural Networks are a powerful subset of deep learning algorithms (title drop! — “Detecting Fast-moving Near-Earth Objects with a Novel DEEP LEARNING ALGORITHM”).
This blog is already getting quite long, so I’ll end it there. I’ll continue talking about what convolutional neural networks are, what specific type of convolutional neural network I’ll be using, and what makes it novel next week!
Until then, happily continue your deterministic journey through space and time!
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