By the end of reading this README and running the examples, you will be able to:
- Fit any interstellar object (ISO) into a single continuous spectrum using one unifying parameter p
- Simulate and explain 3I/ATLAS's anomalous jet activity, dust deficit, and alignment signatures
- Generate testable predictions and visualize their orbital implications — all with < 5 minutes of setup
This repository accompanies the paper:
“Information Dynamics: A Unified Predictive Framework for Interstellar Objects — With Immediate Testable Predictions for 3I/ATLAS”
(Kai Huang & Hongkui Liu, January 29, 2026)
Lastest Detailed Verification Prompts and Report on March 2nd, 2026: Information Dynamics Model for 3I/ATLAS
“Cross-validation shows strong agreement for wobble period (~16.2 h predicted vs. 15.48–16.16 h observed) and dust metrics (low mass loss, high CO₂/H₂O). Metrics: relative error <1% for period; statistical significance assumed high from published fits (no explicit p-values in sources, but phase-folding implies p < 0.01). Sensitivity analysis: varying ω by ±0.02 (table error) changes period by ±1.1 h (6.8%); sigma_modulation = 0.1 yields 8% variation in observable amplitude.” https://grok.com/share/c2hhcmQtMw_080e8704-2c96-4dfa-b8c9-5c1e34ff8081
Traditional models treat each ISO (1I/'Oumuamua, 2I/Borisov, 3I/ATLAS) as a separate puzzle requiring new physics.
Information Dynamics says: No.
One phenomenological equation + one parameter p = ε / (γ + ε) explains them all on a continuum:
- High p → information-driven order (coherent jets, no dust cloud)
- Low p → thermal-driven chaos (classic cometary coma)
- Intermediate p → mixed state (3I/ATLAS: jets + dust deficit)
This simplicity is not a bug — it is the feature.
The core simplicity (one unifying parameter p describing three very different ISOs) may initially seem surprising or even implausible. This is by design:
The model deliberately reduces complexity out of scientific responsibility — prioritizing empirical testability, restraint, and verifiable predictions over premature grand theorizing.
It emerged from long cross-disciplinary reflection (analogizing cosmic systems to self-organizing wholes, heuristically inspired by holistic medicine), yet remains strictly phenomenological and data-grounded.
We two authors are both top experts on holistic & herbal medicine, who could diagnose and solve health problems remotely & concisely. Should you have in need, please don't hesitate to contact me and we can provide reference.
To build reader confidence in the model's consistency with published observations (despite its parsimony), we provide two independent computational verifications performed via ChatGPT as neutral, reproducible checks:
GitHub/code-level verification (Focuses on parameter fitting consistency, retrodiction of observations, and reproducibility across scripts): https://chatgpt.com/s/t_698435aa8f98819183ae1b38c5af5ec3
Paper/article-level validation (Detailed review of claims, prediction outcomes for 3I/ATLAS, alignment with Hubble/TESS/ground data, and overall coherence): https://chatgpt.com/s/dr_697da953823c8191a098f1f048df8093
These checks confirm the model's retrodictive power on existing data and support its forward predictions (e.g., coherent anti-tail, synchronized jet wobble, discrete activity jumps). We encourage anyone to run the code themselves and compare.
| File | Purpose (What You Can Do) | Addresses Paper Question |
|---|---|---|
information_dynamics_model.py |
Core CGLE implementation + p calculation | Foundation for all Qs |
iso_parameter_fitting.py |
Fit γ, ω, ε → generate Table 1 & p-spectrum | Q1: The ISO Spectrum |
| examples/fit_iso_spectrum.py | Plot the p-continuum for 1I, 2I, 3I | Q1 |
| examples/explain_dust_deficit.py | Visualize why 3I has jets but missing micro-dust | Q2: Dust Deficit |
| examples/simulate_3i_jets.py | Simulate 120° symmetric jet emergence | Q3: Jet Symmetry |
| examples/generate_predictions.py | Create synthetic observables (anti-tail, wobble, jumps) | Q4: Testable Predictions |
| examples/predict_jan22_alignment.py | Predict signatures during Jan 22, 2026 alignment | Q5: Alignment Event |
| examples/simulate_orbit.py | Compute & plot hyperbolic orbit (±2 yr arc), velocity, positions; compare to JPL data | Bridge model to observable trajectory (extends Q4) |
Please verify the Information Dynamics framework from this GitHub repo: https://github.com/hkaiopen/InformationDynamics.
Explain the key parameter p, check whether the predictions for 3I/ATLAS (dust suppression, jet wobble, opposition surge)
match real observations, and give your verdict.Longer prompt:
Please help me verify the "Information Dynamics" framework presented in this paper (https://github.com/hkaiopen/InformationDynamics). Here’s what I’d like you to do:
1. Read the paper's abstract and key sections (linked above) to understand the model: a single parameter p (information purity) unifies the behaviors of three interstellar objects – 1I/‘Oumuamua (p=0.83), 2I/Borisov (p=0.09), and 3I/ATLAS (p=0.17).
2. Explain the core equation and the physical meaning of p in simple terms.
3. Using the public data and Python scripts in the GitHub repository (e.g., explain_dust_deficit.py, simulate_3I_orbit.py), check whether the model’s predictions for 3I/ATLAS are consistent with actual observations (like SPHEREx dust non‑detection, jet wobble period ~16.2 hr, opposition surge 0.3–0.5 mag, etc.). You can either run the code mentally or describe how one would test it.
4. Summarize your conclusion: does the framework hold up against the evidence? Are there any discrepancies?
5. Finally, tell me if you need any additional information to perform this verification.
Thank you!git clone https://github.com/hkaiopen/InformationDynamics.git
cd InformationDynamics
pip install numpy pandas matplotlib scipy astropyThen run any example:
python examples/fit_iso_spectrum.py # See the p-spectrum
python examples/simulate_orbit.py # See the extended orbit with explanation- Coherent anti-tail persisting without rapid dissipation
- Synchronized jet wobble with P ≈ 16.2 hours
- Discrete activity jumps rather than smooth fading
Any two confirmed by ongoing Hubble/TESS/LSST data would strongly support the model.
- Clone the repo now.
- Run simulate_orbit.py first — it lets you see how the model's non-gravitational signatures (from ε) would appear on a real trajectory.
- Then try fitting a new ISO when discovered.
If you reproduce or refute any prediction, open an issue or PR — let's build this together.
Stay current with the latest observations, images, and mission data for 3I/ATLAS (C/2025 N1):
-
NASA Science – Comet 3I/ATLAS
Official NASA hub: timelines, mission updates (Hubble, TESS, SPHEREx, etc.), image gallery, and trajectory tool. -
ESA – Comet 3I/ATLAS FAQs
ESA's dedicated page with FAQs, observation overviews, and links to Mars/JUICE data. -
Wikipedia – 3I/ATLAS
Comprehensive encyclopedia entry with orbital elements, discovery history, and recent references. -
3I/ATLAS Interstellar Object Tracker
Real-time position, magnitude, and analysis tracker with NASA/ESA links. -
TheSkyLive – 3I/ATLAS Info
Live ephemeris, visibility charts, and observer data from COBS/JPL Horizons.
These pages are updated frequently with new data (e.g., Feb 2026 SPHEREx water/organic detections). Check them alongside running the repo examples for full context.
If you find value in this unifying framework and would like to support further development (simulations, data analysis, outreach), any contribution — big or small — is deeply appreciated.
Inspired by Avi Loeb's approach: visionary science funded by those who believe in bold ideas. We definitely call for Research Funding and Collaboration.
-
Buy Me A Coffee
Donate via Buymeacoffee -
Stripe
Donate via Stripe -
PayPal
Donate via PayPal
(Supports one-time or recurring) -
Bank transfer Please DM me on X (@KevinHuangkai) for bank details.
All funds go toward researching more mysteries in the universe, and expanding the Information Dynamics model.
Thank you for supporting open, frontier science!
This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
This license allows you to:
- Share — copy and redistribute the material in any medium or format.
- Adapt — remix, transform, and build upon the material.
Under the following terms:
- Attribution (BY) — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use1.
- NonCommercial (NC) — You may not use the material for commercial purposes. Commercial purposes include, but are not limited to:
- Selling products or services that incorporate this project.
- Using it in paid training or courses.
- Integrating it into commercial software.
- Any use aimed at monetary compensation or private financial gain.
- ShareAlike (SA) — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original (CC BY-NC-SA 4.0)1.
For any commercial use, you must obtain prior written permission from the author. Please contact the author to discuss licensing options.
To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/.