HELIOSICA ☀️ — Documentation

☀️ Overview

Nine parameters to decode the solar wind

"The Sun is not a distant backdrop to planetary life — it is an active plasma engine that periodically launches billion-tonne magnetised clouds toward Earth at velocities up to 3,000 km/s."

HELIOSICA is a nine-parameter solar MHD framework for real-time prediction of geomagnetic storm intensity, magnetopause standoff distance, and Kp index evolution from the moment a CME departs the solar corona to the moment its shock front compresses Earth's magnetosphere.

88.4%

GSSI Accuracy

Across 312 storm events

4.2h

RMSE

CME arrival time

312

Events

Validation catalogue

48h

Lead Time

From CME departure

9

Parameters

SPIN framework

0.91

r² Kp

Prediction accuracy

📄 Research Paper

Journal of Geophysical Research — Space Physics (AGU)

HELIOSICA Research Paper

Submitted to Journal of Geophysical Research - Space Physics · March 14, 2026

Title: HELIOSICA — Nine Parameters to Decode the Solar Wind and Shield Our Digital World
Author: Samir Baladi
Affiliation: Ronin Institute / Rite of Renaissance
DOI: 10.5281/zenodo.19042948
License: MIT License
Status: Under review
Keywords: space weather, coronal mass ejection, CME transit, solar wind, geomagnetic storm, Kp index, Dst index, drag-based model, magnetopause standoff, magnetic reconnection, Forbush decrease, heliophysics

📊 Key Results

Validation performance metrics

4.2h

RMSE

CME arrival time vs 7.1h WSA-Enlil

0.91

r² Kp

Kp prediction accuracy

88.4%

GSSI Acc

Storm classification

0.71

R_E RMSE

Magnetopause standoff

0.871

r E_y

Dominant predictor

0.963

AUC

G4+ classification

🔬 SPIN Parameters

Solar Plasma Intelligence Nonet

Parameter	Symbol	GSSI Weight	Description
CME Launch Velocity	V₀	0.13	Initial ejecta speed at 21.5 R☉
Southward IMF	Bz	0.19	Reconnection driver
Ram Pressure	P_ram	0.16	Magnetospheric compression
Drag Coefficient	γ	0.10	Heliospheric deceleration
Angular Spread	ω	0.08	CME geometry
Proton Temperature	Tp	0.06	Thermal signature
Reconnection E-field	E_y	0.23	Energy injection rate
Forbush Decrease	Fd	0.03	Cosmic ray suppression
Kp Index	Kp	0.02	Planetary disturbance

📈 GSSI Composite

Geomagnetic Storm Severity Index

// Geomagnetic Storm Severity Index // HELIOSICA Master Equation GSSI = 0.23 · E_y* // Reconnection Electric Field + 0.19 · Bz* // Southward IMF + 0.16 · P_ram* // Ram Pressure + 0.13 · V₀* // CME Launch Velocity + 0.10 · γ* // Drag Coefficient + 0.08 · ω* // Angular Spread + 0.06 · Tp* // Proton Temperature + 0.03 · Fd* // Forbush Decrease + 0.02 · Kp* // Kp Index Baseline // All parameters normalized to [0, 1] using 312-event validation catalogue // E_y carries dominant weight (r = 0.871 with Kp)

<0.20

G0-G1

Minor - No action

0.20-0.45

G2-G3

Moderate-Strong

0.45-0.70

G4

Severe - Satellite Alert

>0.70

G5

Extreme - Grid Protection

⚠️ Alert Thresholds

SPIN reference thresholds

Parameter	Quiet (G0)	Strong (G3)	Extreme (G5)	Alert
V₀	<400 km/s	800-1500	>2000	>1000 km/s → transit <48h
Bz	>-2 nT	-10 to -20	<-30	<-10 nT sustained → G3+
P_ram	1-3 nPa	8-20	>30	>20 nPa → R_MP <7.0 R_E
E_y	<0.5 mV/m	3-7	>12	>2 mV/m → ring current activation
Fd	<1%	2-5%	>7%	>3% → cloud confirmed

📦 Installation

Quick setup

# Install from PyPI pip install heliosica # Clone repository git clone https://github.com/gitdeeper9/heliosica.git cd heliosica # Install with pip pip install -r requirements.txt pip install -e . # Or using Docker docker-compose up -d # Verify installation python -c "import heliosica; print(heliosica.__version__)"

🔧 API Reference

Python interface

DBMSolver

Drag-Based Model for CME transit time prediction

from heliosica import DBMSolver solver = DBMSolver() result = solver.predict( V0=1200.0, Vsw=450.0, omega=60.0, np=8.0 ) print(f"Arrival: {result.arrival_time_50:.1f} hours")

StormForecaster

Real-time Kp and GSSI prediction

from heliosica import StormForecaster forecaster = StormForecaster() storm = forecaster.evaluate( Ey=6.5, Bz=-14.0, Pram=12.3, V=620.0 ) print(f"Kp = {storm.Kp_pred:.1f} GSSI = {storm.GSSI:.2f}")

MagnetopauseTracker

Standoff distance and satellite alerts

from heliosica import MagnetopauseTracker tracker = MagnetopauseTracker() rmp = tracker.compute(Pram=12.3) print(f"R_MP = {rmp.R_MP_RE:.1f} R_E Alert={rmp.satellite_alert}")

ForbushMonitor

Forbush decrease detection

from heliosica import ForbushMonitor monitor = ForbushMonitor() fd = monitor.detect(counts=neutron_data) print(f"Fd = {fd.Fd_percent:.1f}% B_cloud = {fd.B_cloud_nT:.1f} nT")

🧩 Core Modules

Physics engine

DBM

Transit

Drag-Based Model

R_MP

Magnetopause

Standoff distance

E_y

Reconnection

Electric field

Kp

Predictor

Index forecast

Fd

Forbush

Cloud detection

GSSI

Composite

Severity index

👤 Author

Principal investigator

☀️

Samir Baladi

Interdisciplinary AI Researcher — Space Weather Physics, Solar MHD & Heliophysics Modeling

Ronin Institute / Rite of Renaissance

Samir Baladi is an independent researcher affiliated with the Ronin Institute, developing the Rite of Renaissance interdisciplinary research program. HELIOSICA is the fifth framework in a series of open‑source geophysical frameworks. The framework was validated against 312 historical geomagnetic storm events (1996-2025) from the OMNI heliospheric dataset, SOHO/LASCO CME catalogue, and NOAA Kp/Dst archives.

No conflicts of interest declared.

📧 gitdeeper@gmail.com 🔗 ORCID: 0009-0003-8903-0029 🐙 GitHub 🦊 GitLab

📝 Citation

How to cite

@software{baladi2026heliosica, author = {Baladi, Samir}, title = {HELIOSICA: Nine Parameters to Decode the Solar Wind and Shield Our Digital World}, year = {2026}, version = {1.0.0}, doi = {10.5281/zenodo.19042948}, url = {https://github.com/gitdeeper9/heliosica}, license = {MIT} }

"Nine parameters to decode the solar wind and shield our digital world — HELIOSICA makes it computable."

HELIO-SICA