Transiting Exoplanet Survey Satellite (TESS) Neural Network (NN)
Description
The Transiting Exoplanet Survey Satellite (TESS) is a NASA mission focused on exploring and finding exoplanets around nearby stars using the transiting method. TESS telescope covers a large field of view of 96 sq. deg in a single exposure. It has four cameras arranged vertically pointing from the ecliptic plane toward the poles.
Detailed example
This NN has an architecture that uses two 3D U-Nets stacked (W-Net) with skip connections that output a 3D segmentation mask with asteroid detections. We will introduce the NN model and present results from predictions using years 1 and 2 of TESS data. Additionally, we will show preliminary light curves extracted from new asteroids detected by our model.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Thanks to this configuration and observing schedule, TESS is able to observe asteroids with a high duty cycle. Current techniques to search for asteroid signals on images rely on the shift-and-stack method, which relies on testing all possible combinations of direction and speed an object can move across the image to maximize the detection signal and find the asteroid’s track. This method is computationally expensive, and only attainable when the parameter space (direction-velocity) is constrained, usually to the main direction (e.g. orbits parallel to the ecliptic plane) and low speeds (main belt asteroids). This introduces a bias against fast-moving asteroids and high-inclination orbits (vertical tracks). To solve this, we implemented a rotationally invariant neural network (NN) model that performs semantic segmentation to find moving objects in TESS FFIs.We constructed a custom training set using 64x64x64 cubes of pixel flux time series and truth masks with the tracks of known asteroids from the JPL Horizon Ephemeris system. Our NN model can find known and new asteroids with all kinds of track orientations, showing no bias against objects moving at high inclination orbits, or fast-moving asteroids, or tracks with a change in direction. This NN model detects ~90% of known asteroids down to apparent visual magnitude 20th and has a detection limiting magnitude of ~20.5. This is on par with current implementations of the shift-and-stack method but without the bias introduced by limiting the range of track direction and velocity.
Controls / human review
ATO: Not reported; PIA: Not published