Transiting Exoplanet Survey Satellite (TESS) Neural Network (NN)

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

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.

Technology

AI classification

Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported