NASA · NASA-10 · record R0108

ExoMiner discovery of ExoPlanets via data from the Kepler and TESS space telescopes

National Aeronautics and Space Administration · ARC: Ames Research Center

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

ExoMiner is being used to statistically validate exoplanets detected by the Kepler space telescope and to identify promising exoplanet candidates for the TESS space telescope

Expected benefits

ExoMiner has been used to validated 370 new exoplanets identified in data from the Kepler space telescope. It is now being used to idenitify promising exoplanet candidates in TESS mission data. It promises to significantly reduce the manual effort required while improving the accuracy of identify promising TESS exoplanet candidates and to reject astrophysical false positives and instrumental false alarms.

System outputs

ExoMiner produces scores between 0.0 and 1.0 for a number of possible categories relevant to transiting exoplanet searches.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
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.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported