This is a Jupyter Book version of the Masters Thesis titled “Educational Visualization Tools for Conveying Research: Jupyter Notebooks on Synestias” by Gigja Hollyday, Department of Earth and Planetary Sciences, U. California Davis, 2020.

The set of Jupyter Notebooks was developed as an interactive educational resource on the topic of synestias, a recently discovered type of planetary object. The thesis Notebooks are available at

The original content has been lightly edited for the Book format.

Please send corrections/comments/questions to Professor Sarah T. Stewart, sts .at.


A synestia is a newly discovered planetary object that can evolve into a planet-moon system. If the Moon-forming giant impact resulted in a synestia, then the Moon would have formed within Earth. Moons condense and grow inside a synestia and a synestia eventually cools and transitions into a planet. However interesting this implication may be, much of the public and many researchers are not familiar with synestias and also have diffculty understanding the complex physics which dictate a synestia’s behavior. To make learning about synestias more accessible and intuitive, I have developed a set of online Jupyter Notebooks (text, media, and code) for a user to visually explore various physics-based aspects of synestias. In this set of notebooks, a user will learn about: what synestias conceptually look like, the gravitational and extreme thermal profiles of a synestia, the criteria for an object to be a synestia, how synestias form, the balance of forces within a synestia, how gas drag affects moon formation within synestias, and how angular momentum affects the orbital dynamics of moons forming within synestias.


Research is difficult; explaining it is even more so. How can researchers distill complex, nuanced research results into a friendly and easy-to-understand format for others? Jupyter Notebooks, documents that are part text, media, and code that can be placed online (Kluyver et al., 2016), are an effective visualization educational tool for conveying ideas and research results (Barba & Forsyth, 2018; Freeman et al., 2014). Part of what makes Jupyter Notebooks effective learning tools is their interactive python widgets, ipywidgets. These widgets can be manipulated so that a user can interact with the Jupyter Notebook. Widgets are commonly used in this set of notebooks to allow a user to manipulate plots. A user can adjust the inputs for an analysis, and changes in the plots are shown in real time. This allows the user to explore concepts visually and gain an intuitive understanding of how various parameters affect a particular outcome. Jupyter Notebooks also have a higher readability and approachability than scientific papers, which tend to be less inviting for non-specialists.


The goal of this particular set of Jupyter Notebooks is to help people at the undergraduate level and above understand a newly discovered type of planetary object called a synestia. This discovery is not well understood by those outside of the lunar research community, although it has received a decent amount of publicity [TED (Stewart, 2019); Scientific American (Lock & Stewart, 2019)]. Every new idea takes a while to absorb into the public sphere, still, those who do know about synestias struggle with grasping the physics of their internal dynamics. This is to be expected; we have no real-life reference for synestias (e.g. telescopic images). We are not familiar with this new type of planetary object, so we don’t have any intuition for how a synestia might behave. The physics that can explain how synestias work are unfamiliar to us; what we observe on Earth on a daily basis is very different from how those same objects would behave within a synestia’s oblate, high-pressure, high-temperature environment. These notebooks seek to bridge that gap – to help you analyze and gain intuition about the inner workings of synestias so that they seem less esoteric.


Simulations in this paper made use of the REBOUND code which is freely available at GitHub (Rein & Liu, 2012). REBOUND is an open-source software that computes force interactions (gravity in particular) among spherical particles (can represent stars, planets, moons, ring or dust particles). It is efficient and easy to customize. The integrator in the simulations is IAS15, which uses an adaptive time step (Rein & Spiegel, 2015).

Interactive Python Widgets

To launch the interactive book pages and run them in a web browser using Binder, click on the rocket icon in the upper right corner of the page and select Binder. Not every page has an interactive component. Note that it may take a few minutes to load the interactive notebook.

If you wish to use the original Jupyter Notebooks on your own computer, you will need to install Python, Jupyter, and REBOUND. Follow the instructions given in

What’s to Come

In the Jupyter notebooks, you are guided through the various dynamics and attributes of synestias, mainly via interactive figures.

  1. What are Synestias? How Were Synestias Discovered? (Giant Impact Origin of the Moon)

  2. Physical Properties of Synestias (Temperature, Pressure, Density, Gravity)

  3. What Determines Whether a Planetary Structure is a Synestia? (Corotation Limit)

  4. How Do Synestias Form? (Giant Impacts)

  5. What are the Forces Acting Within Synestias? (Hydrostatic Equilibrium)

  6. How Does Gas in a Synestia Affect Moon Formation? (Gas Drag)

  7. How Do Moonlets Forming Within Synestias Interact? (Synestias Must Conserve Angular Momentum)

There are a few smaller, bite-sized notebooks that define terms or concepts that are commonly invoked in explanations throughout the main notebooks, namely:

  • Angular Velocity

  • Keplerian Orbit

  • Midplane

  • Poincarè-Wavre Theorem

  • Spherical and Cylindrical Coordinates

A few potential Moon-forming synestia example cases (output from smoothed-particle hydrodynamics simulations) are used throughout the notebooks, especially as part of the interactive plots. Refer to Synestia_Moon_Ex_Cases.ipynb for details about these synestia example cases.

Science Concepts Covered

These notebooks will touch on various science concepts: linear and angular velocity, linear and centripetal acceleration, conservation of angular momentum, gravitational forces acting on multiple bodies, non-conservative forces (gas drag), pressure gradients and hydrostatic equilibrium, material phases and phase changes, extreme thermal regimes for rocky materials, orbits (Keplerian), solid body rotation, giant impacts, deep time (early solar system), formation of moons and planets, and planetary structures (planet, moon, planetary disk, synestia).

Student Learning Outcomes

In addition, these notebooks satisfy the following student learning outcomes based on CAS (Council for the Advancement of Standards in higher education) domains (Wells, 2015).

  1. Knowledge acquisition, construction, integration and application

    • Understanding knowledge from a range of disciplines: “Possesses knowledge of…the physical world; possesses knowledge of [a specific] one or more subjects” (e.g. synestias, formation of Earth-Moon system)

    • Constructing knowledge: “Personalizes learning; makes meaning from text, instruction, and experience; uses experience and other sources of information to create new insights; …recognizes one’s own capacity to create new understandings from learning activities”

    • Relating knowledge to daily life: “makes connections between classroom and out-of-classroom learning”

  2. Cognitive complexity

    • Critical thinking: “analyzes, interprets, and makes judgments of the relevance and quality of information; assesses assumptions and considers alternative perspectives and solutions”

    • Reflective thinking: “Applies previously understood information, concepts, and experiences to a new situation or setting; rethinks previous assumptions”

    • Effective reasoning: “Uses complex information from a variety of sources including personal experience and observation to form a decision or opinion; is open to new ideas and perspectives”

  3. Practical competence

    • Technological competence: “Demonstrates technological literacy and skills…stays current with technological innovations”


I included previously published figures and videos throughout these notebooks. For each copyrighted material that appears in this thesis, I have referenced its respective source. The respective copyright owners have granted permission for their materials’ use in this academic, non-profit publication. The materials have been reprinted with permission from: Donna Cox of the University of Illinois at Urbana-Champaign Visualization Center, The American Association for the Advancement of Science or AAAS (Science) and Robin M. Canup of Southwest Research Institute, American Geophysical Union or AGU (Journal of Geophysical Research) and Simon J. Lock, and Raluca Rufu. Email correspondence proving permission to use these copyrighted materials is located in the folder copyright_permissions.

This work is made possible by the community development in the python ecosystem, including NumPy (Van Der Walt et al. 2011), matplotlib (Hunter 2007), SciPy (Virtanen et al. 2020), the Jupyter project, and ipywidgets.

Jupyter Book and many of the tools it uses are stewarded by the Executable Book Project, which is supported in part by the Alfred P. Sloan foundation.


This set of Jupyter Notebooks is intended for non-profit educational purposes, in partial fulfillment of Gigja Hollyday’s M.S. thesis in Geology at the University of California, Davis. For inquiries or feedback, please email gohollyday at ucdavis dot edu.


Barba, L., & Forsyth, G. (2018). CFD python: The 12 steps to Navier-Stokes equations. Journal of Open Source Education, 1 (9), 21.

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111 (23), 8410-8415.

Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., and Willing, C. (2016). Jupyter notebooks – a publishing format for reproducible computational work flows. In F. Loizides & B. Schmidt (Eds.), Positioning and Power in Academic Publishing: Players, Agents and Agendas (p. 87-90). IOS Press.

Lock, S. J., & Stewart, S. T. (2019). When Earth and the Moon were one. Retrieved from (Scientific American)

Rein, H., & Liu, S. F. (2012). REBOUND: an open-source multi-purpose N-body code for collisional dynamics. Astronomy and Astrophysics, 537 (A128), 1-10.

Rein, H., & Spiegel, D. S. (2015). IAS15: a fast, adaptive, high-order integrator for gravitational dynamics, accurate to machine precision over a billion orbits. Monthly Notices of the Royal Astronomical Society, 446 (2), 1424-1437.

Stewart, S. T. (2019). Where did the Moon come from? A new theory. TED Salon: U.S. Air Force. Retrieved from (TED)

Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011) The NumPy Array: A Structure for Efficient Numerical Computation. Computing in Science & Engineering, 13, 22-30.

Virtanen, P., et al. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17, 261-272, doi:10.1038/s41592-019-0686-2.

Wells, J. B. (Ed.). (2015). CAS learning and development outcomes (9th ed.). Council for the Advancement of Standards in Higher Education. Retrieved from (CAS)