CARMApy: An Open-Source Python Framework for Simulating Microphysical Clouds in Planetary Atmospheres

CARMApy is a new open-source python code that performs bin-scheme microphysical modeling of clouds in exoplanet atmospheres. It models key cloud properties such as particle size distributions and microphysical rates from first principles. The new analysis still awaits peer review, but it already lays out the central claim clearly.

It matters because exoplanet science has moved beyond the era of simple discovery into a period of comparative characterization. With more than five thousand confirmed planets known, the scientifically productive questions now concern atmospheric composition, internal structure, orbital history and the statistical properties of populations rather than the existence of individual worlds. A new detection or spectral measurement is most valuable when it adds a well-constrained data point to those comparative frameworks, not when it stands alone as an anecdote. It models key cloud properties such as particle size distributions and microphysical rates from first principles. The code is a wrapper of ExoCARMA, a well tested Fortran code with an almost half century long heritage.

CARMApy includes the microphysical processes of homogeneous and heterogeneous nucleation, condensational growth, evaporation, coagulation, and vertical transport. CARMApy has 10 built-in default condensates and allows the user to specify additional condensates.

In this work we describe CARMApy and the data products that it can generate, along with the history of its code heritage. We additionally compile a complete description of the theory and methods used in CARMA.

Lastly we benchmark CARMApy and show that its results are consistent with previous versions of CARMA, while executing the code ~1.9 times faster single threaded ~3.

The broader interest lies in making the target less anecdotal and more comparable with the rest of the known planetary population. Population-level questions, such as the frequency of atmospheres around small rocky planets or the prevalence of water-rich worlds in the habitable zone, require well-characterized individual data points before statistical patterns become meaningful. Each new planet with a measured radius, mass and, ideally, atmospheric constraint is a brick in that larger structure, and the accumulation of bricks eventually allows theorists to test formation models against real distributions rather than projections.

Because this is still a preprint, the result should be read with genuine interest and proportionate caution. Peer review is not a guarantee of correctness, but it is a process that forces authors to respond to technical criticism from specialists who have no stake in a particular outcome. Preprints that survive that process, often with substantive revisions, emerge with a stronger evidential base than the version that first appeared. Until that stage is complete, the responsible reading keeps uncertainty explicitly visible rather than treating the claims as established findings.

The next step is to improve independent constraints on the mass, radius, atmospheric composition and orbital dynamics of the target. Transmission spectroscopy with JWST, radial velocity campaigns with high-resolution ground-based spectrographs and phase-curve measurements from space photometry represent the observational toolkit that can move characterization from plausible to robust. That convergence of techniques is the standard the community now expects before a planetary atmosphere result is treated as confirmed. Until peer review and independent follow-up address those open questions, skepticism is not a failure of appreciation for the work; it is part of how science decides what to keep.

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