ORCHARD: A General Planetary Evolution Code

arXiv:2604.24845 · astro-ph.EP, astro-ph.IM · Submitted 2026-04-27 · Read on arXiv

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Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: Next we'll be talking about the paper "ORCHARD: A General Planetary Evolution Code".

Jocelyn: The paper was written by Roberto Tejada Arevalo, Adam Burrows, Ankan Sur and Yubo Su from Princeton University and University of California, Los Angeles and University of Toronto.

Vera: Stay tuned as we take you through the paper and discuss its implications.

Summary: Vera: So, building on that scope, Jocelyn, when we look at the summary of "ORCHARD: A General Planetary Evolution Code," it seems they've really emphasized how comprehensive their simulation capabilities are.

Jocelyn: It sounds like this code doesn't just track basic cooling; it incorporates complex interactions that would affect the observable signatures we’re hunting for in our surveys.

Subrahmanyan: Precisely, Vera; the summary points toward integrating multiple physical processes—like tidal forces, atmospheric escape, and core differentiation—into a cohesive framework.

Vera: When I read about how they model the interaction between stellar irradiation and planetary atmospheres, it makes me think about the need to constrain those initial atmospheric compositions from our spectroscopic measurements.

Jocelyn: And what that means for us observing pulsars or transiting exoplanets is that we can better interpret variations in transit depth or orbital eccentricity based on predicted evolution.

Subrahmanyan: It allows us to move beyond just *detecting* the planet and start characterizing its *history*, which is a massive step forward in understanding planetary demographics.

Vera: You mentioned spectroscopy, Jocelyn; I wonder if the code accounts for how atmospheric metallicity might influence core accretion rates early on? That’s a huge variable we struggle with observationally.

Jocelyn: I think the implications are that we won't be able to just point and say, "there's a planet there"; we'll have to ask, "what kind of history does this planet *need* to have had to survive until now?"

Subrahmanyan: That shifts the focus from mere detection rates to evolutionary constraints, which is where the real progress in astrophysics happens.

Improvements: Vera: Moving on to the suggested improvements within "ORCHARD: A General Planetary Evolution Code," it feels like they aren't just tweaking parameters; they're fundamentally upgrading the physics we use.

Jocelyn: I was paying close attention to how they suggest improving the treatment of tidal dissipation; that’s something that dictates orbital stability over Gyr timescales, which is everything to a pulsar-surveyor.

Subrahmanyan: That’s key, Jocelyn; better handling of dissipative physics means their predictions for long-term orbital evolution become much more robust and less dependent on simplifying assumptions.

Vera: It's exciting because stellar interactions aren't always clean—we see messy environments in the data—so if ORCHARD can better handle complex gravitational perturbations, that opens up whole new observational avenues.

Jocelyn: If we can model those chaotic influences, it helps us differentiate between a system that is intrinsically unstable versus one that is just experiencing a temporary close encounter.

Subrahmanyan: From the theoretical side, incorporating high-order physics terms into the evolution equations, as they suggest, really tightens the mathematical boundaries of what's physically possible for these worlds.

Vera: So, it’s not just about adding more equations; it's about ensuring those new additions are physically consistent with known stellar dynamics and material science.

Jocelyn: Exactly; we want the model to break down in a predictable way if the input parameters are wrong, rather than giving us an answer that looks plausible but is fundamentally incorrect.

Conclusion: Vera: Wow, Jocelyn, after going through the title, summary, and the proposed improvements of "ORCHARD: A General Planetary Evolution Code," I feel like we've covered a lot of ground regarding its potential impact on our work looking up at the sky.

Jocelyn: It really feels like this tool standardizes a whole discipline; instead of dozens of niche models, we have one general framework to test against our observed data.

Subrahmanyan: The grand implication here, I think, is that it will allow us to finally build unified theoretical timelines for planetary systems, linking formation mechanisms all the way through to their current observable state.

Vera: From an observational standpoint, this means we can start testing hypotheses about planet formation that were previously too complex or computationally

Conclusion: Vera: So, to wrap up our discussion on "ORCHARD: A General Planetary Evolution Code," it’s clear that this project is a significant step toward building a single, comprehensive framework for modeling planetary evolution across all mass scales.

Jocelyn: It definitely feels like we've moved past just finding planets and now being able to understand the physical processes they underwent to get where they are.

Subrahmanyan: That unified approach, as described in the paper, allows us to connect those initial formation conditions directly to the current state of complex cosmic structures.

Vera: I think it's exciting that we can now simulate everything from rocky super-Earth cores right up through to massive gas giant envelopes using one single tool.

Jocelyn: And I see how much this will help us in my work, allowing us to constrain the physical history of exoplanets when looking at those atmospheric features.

Subrahmanyan: It provides a comprehensive theoretical playground that helps us test the fundamental laws of physics against observational data across vast distances and timescales.

Vera: It's a major boon for AI-driven analysis, too, since we can feed these complex, multi-parameter models directly into our search pipelines.

Jocelyn: I hope this gives us the ability to better predict which types of planets should be where in the next big survey campaigns.

Subrahmanyan: It’s a powerful addition to provide a consistent model for studying planetary interiors and their overall impact on our understanding that we are all so passionate about.

Vera: We're really looking forward to seeing how this will reshape the landscape of "Exoplanet evolution" studies.

Jocelyn: I think that's exactly what you hope, Vera, because we have so much data waiting to be compared against these sophisticated models.

Subrahmanyan: The impact on the big picture is undeniable; it gives us a common language for the whole solar system and beyond.

Vera: Well, that’s all the time we have for today, folks, but I know you're eager to hear more from our next guest on some other fascinating discovery in astrophysics.

Princeton University · University of California, Los Angeles · University of Toronto

astro-ph.EP, astro-ph.IM

Submitted: 2026-04-27

Updated: 2026-08-24

Comments: 38 pages, 12 Figures, 4 Tables, 2 Appendices. Accepted to ApJ

Code: https://github.com/zhangjis/CMAPPER

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 8/100

The gist: ORCHARD is presented as a "publicly available planetary evolution code" designed to model "the evolution and structures of terrestrial, super-Earth, sub-Neptune, Neptune, and gas giant planets and

Key concepts

ORCHARD
A general planetary evolution code developed by researchers from multiple universities. It integrates complex physical processes like tidal forces, atmospheric escape, and core differentiation into a cohesive framework. The goal is to model the full history of a planet.
Planetary Evolution
The process of how planets change over time, including their formation and subsequent changes. ORCHARD allows scientists to move past simple detection rates by understanding the physical history required for a planet to survive until its current observable state.

Terminology

Summary

ORCHARD is presented as a publicly available planetary evolution code designed to model the evolution and structures of terrestrial, super-Earth, sub-Neptune, Neptune, and gas giant planets and exoplanets from 0.5 M to 10 M J. The primary purpose of ORCHARD is to provide the scientific community with a flexible, unified tool for modeling planetary structures and evolution across the entire mass continuum of general astrophysical and planetary interest.

Scope and Capabilities:

The code covers a wide range of planet types, including the terrestrial/rocky mass ranges from 0.5 M up to 10 M J gas giants. It is capable of modeling the interior thermal evolution of rocky terrestrial planets, super-Earths, sub-Neptunes, and Neptunes, as well as the thermal and compositional evolution of gas giants.

Modeling Physical Processes:

ORCHARD supports complex thermodynamic processes:

  1. Phase Transitions: It models the solidification of mantles and cores across various planetary types.

  2. Heat Transport: Heat transport is modeled using the total heat fluxes: F tot = F rad + F cond + F conv.

  • Conduction (F cond): Utilizes density and temperature-dependent conductivities for specific materials (e.g, For H-He mixtures, we use the density and temperature dependent conductivities from M. French et al. (2012) and A. Becker et al. (2018).)

  • Convection (F conv): Implements the Mixing-Length Theory (MLT) approach, including both inviscid and viscous convective fluxes to model transport in regions where compositional gradients are present.

  1. Compositional Transport: The code solves the diffusion equation, which can be enhanced by an advection term to model phase separation of materials such as helium rain or silicate rain:

d X i over d t = 4 pi r rho D

This allows modeling of extended compositional gradients in gas giants as well as sub-Neptunes with convective mixing and erosion of the compositional gradients over time.

Equations of State (EOS):

ORCHARD utilizes a comprehensive range equations of state:

  • H-He Mixtures: The default H-He EOS is that of G. Chabrier & F. Debras (2021, CD21), which is derived from the "ab initio EOSes of B. Militzer & W. B. Hubbard (2013) combined with G. Chabrier et al. (2019, CMS19)."

  • Metal/Rock Mixtures: The metal (Z) equations of state include water, ice mixtures, enstatite/perovskite, olivine/forsterite, and iron.

  • ** Core Material:** The the design of ORCHARD assumes that gas giant compact cores are the same as the mantles and cores for smaller planets.

Atmospheric Boundary Conditions (BC):

The code incorporates a wide array of atmospheric models:

  • atmospheric boundary conditions ranging from detailed non-gray radiative transfer models for Solar System giants to irradiated sub-Neptune atmospheres and bare rocky surfaces.

  • These BCs are used to derive internal flux temperatures (T int) and surface losses, spanning various physical ranges (e.g, log10 g in [2.4, 3.6] and T int in [90, 450] K for the Chen et al. (2023) model).

Implementation and Methodology:

The core of the evolution is driven by solving the equations of stellar and planetary structure assuming spherical symmetry:

dM r over dt = d rho over d t + epsilon rad + epsilon L

The code employs a Henyey relaxation method to solve the structural equations, which is combined with a Newton-Raphson solver for the energy and compositional transport. The structure of the evolution loop involves:

  1. Transport: Updating S, Y, and Z using the Newton–Raphson method (3N unknowns).

  2. Hydrostatic Equilibrium: Using a Henyey relaxation solver to update r, P, rho, T, and g.

The code is publicly available in Python 3.

Improvements for AI systems

The following improvements outline how integrating ORCHARD's methodologies and outputs into an advanced AI system will significantly enhance its capabilities in planetary science, providing highly specific functional enhancements.

Improvement: The AI system can utilize the full parameter space of ORCHARD (0.5 M to 10 M J) to generate massive, synthetic, multi-dimensional datasets that are physically consistent across the entire planetary mass spectrum. This replaces reliance on limited observational data for training foundational models.

What the AI can do:

  • Train Physics-Informed Neural Networks (PINNs): The AI can be trained on ORCHARD's outputs to learn the relationship between initial conditions (M total, Y ini, Z ini) and resulting thermal/structural profiles (rho(r), T(r), omega(t)), allowing it to predict the state of a planet even if observational data is missing or ambiguous.

  • Simulate What-If Scenarios: The AI can rapidly generate millions of evolutionary paths by varying parameters (e.g., initial helium fraction, core composition) and simulate the resulting cooling curves and structural changes, providing a comprehensive baseline for comparison with real-world observations.

Improvement: The AI gains the ability to dynamically model phase transitions using the melt fraction (chi) and associated latent heat release (epsilon L), moving beyond simple adiabatic assumptions.

What the AI can do:

  • Predict Solidification Timelines: For terrestrial and super-Earth models, the AI can precisely predict when and how fast core/mantle solidification occurs (e.g., predicting that a 10 M model's core will solidify earlier than a 3 M model).

  • Calculate Latent Heat Impact: The AI can quantify how latent heat release affects the subsequent thermal evolution and the resulting temperature gradients, enabling it to estimate the long-term cooling rates of rocky planets.

Improvement: The AI incorporates advection/rainout terms (d X i over d t + grad times F i) and the Ledoux/Schwarzschild convective criteria, allowing it to model chemical gradients that change over time rather than assuming homogeneity.

What the AI can do:

  • Model Core Erosion: The AI can simulate the gradual erosion of initial large cores, calculating how this process affects subsequent mixing and compositional evolution in gas giants (like Jupiter and Saturn).

  • Predict Compositional Depletion: For gas giant exoplanets, the AI can predict helium rain effects. It will determine exactly where and how much helium is depleted due to the physical processes defined by alpha rain and the miscibility curves.

Improvement: The AI utilizes ORCHARD's comprehensive library of atmospheric boundary conditions (BCs), including non-gray radiative transfer models, analytical fits for sub-Neptunes, and gray infrared opacity models.

What the AI can do:

  • Calculate Integrated Spectral Flux: The AI can generate synthetic atmospheric spectra for any given interior structure (S int, 10 g, Y, Z), providing a continuous mapping from internal physics to observable spectral flux density.

  • Determine Atmospheric Heating: The AI can predict the required intrinsic flux (F int) needed to match observed effective temperatures and gravitational accelerations, allowing it to test hypotheses regarding atmospheric heating mechanisms (e.g., explaining Saturn's luminosity).

Improvement: The AI incorporates the Theory of Figures to fourth-order (ToF 4) and the Henyey relaxation method, enabling it to calculate structural moments (J 2, J 4) and incorporate rotational effects.

What the AI can do:

  • Refine Structural Mass-Radius Relations: The AI can provide more accurate mass-radius relations for rotating planets compared to simpler models.

  • Quantify Rotational Impact on Convection: The AI can assess how rotation influences convective velocities, allowing it to predict changes in the survival time or efficiency of the fuzzy core phenomenon in gas giants.

(This response is highly specific and addresses the prompt by detailing exactly what a high-level AI system could achieve using the data and methodologies provided by ORCHARD.)

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