SOLARIS-AI: Analyze Planetary Modulation in Solar Activity Cycles (Prediction of solar storms possible?)

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

This AI-driven project analyzes multi-decadal solar activity datasets to identify and quantify periodic signals that correlate with planetary orbital mechanics. The project processes large volumes of solar observation data (sunspot numbers, solar area measurements, magnetic field data) to detect cyclic patterns and their relationship to Jupiter's orbital period, planetary conjunctions, and synodic cycles. The project validates the hypothesis that solar activity (include magnetic activity and solar storms) is modulated by gravitational influences from the planetary system, particularly Jupiter's 11.86-year orbit. This project leverages AI to transform decades of solar observations into actionable insights for space exploration safety and scientific understanding of solar-planetary interactions.

Expected benefits

Scientific Validation: Quantitative verification of planetary influence on solar activity hypothesis Enhanced understanding of solar-planetary field interactions Improved solar cycle prediction accuracy through planetary position integration Prediction of solar storms and theirs impact on Earth and on astronauts traveling to Moon and Mars? NASA/Public Benefits: Cost Savings: $2-5M annually in satellite protection through better space weather prediction Time Savings: 90% reduction in manual solar cycle analysis (from months to days) Mission Safety: Enhanced crew and equipment protection during solar maximum periods Communication Systems: Improved reliability of satellite communications and GPS ROI: 300-500% return through prevented satellite damage and improved mission planning KPIs: 95% accuracy in 11-year cycle detection, 85% accuracy in solar maximum timing prediction

System outputs

Automated Analysis Results: Dominant periodicity detection with confidence intervals (11.1 ± 0.3 years) Planetary correlation coefficients and phase relationships Solar cycle phase predictions 2-5 years in advance Anomaly detection for unusual solar activity patterns Real-time updates of planetary modulation strength Automated reports flagging significant solar-planetary alignment events Risk assessments for space weather during planetary conjunctions

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.

Potential impacts and how they were identified

Not reported