Sparse data
Exoplanet observations are limited in time and spectral coverage, and will remain so for decades.
Scientific rationale & goals
Interpreting worlds beyond the Solar System.
We are at a frontier where the atmospheres and surfaces of planets around other stars can be probed directly. Turning that data into understanding needs a new kind of model.
Four challenges in interpreting exoplanet data
Closed models
Models are often bespoke to the group analysing the data, which limits reproducibility and interpretability.
Static solves
The prevailing paradigm solves structural equations statically, missing the strong hysteresis of planetary climate and structure.
Costly forward models
Expensive forward models cannot be used in formal inversion schemes, blocking model comparison and parameter estimation.
These challenges combine to create uncertainty and disagreement, most acutely around the biogenicity of signals detected from exoplanets, where robust, reproducible definitions of abiotic processes are essential.
The response
Open, evolutionary, process-complete, invertable.
Interra shifts the paradigm for planetary modelling toward a framework in which large ensembles of simulated planets are compared with data, and are documented so that missing physics and chemistry is known. By probing the successes and failures of successive generations of ensembles, we deepen our understanding of the processes that shape planets over geological time.
The deliverable
Comprehensive ensembles, improving every generation.
Interra’s central output is large ensembles of modelled planetary evolution paths: sets of simulated planets that span a relevant range of physical and chemical conditions, such as mass, composition, and instellation (the stellar energy a planet receives). Each generation improves on the last in depth (how physically and chemically complete the models are) and breadth (how much of parameter space they cover). The models are post-processed through telescope simulators, the instrument pipelines of facilities like JWST, the ELTs, PLATO, and Ariel, so they can be compared directly against real observations. Where models and data systematically disagree, the mismatch points to the missing physics and chemistry that the next generation must add.
What the ensembles enable
Three strands of discovery.
Connected processes of planetary evolution
Understand how planets, emerging from their magma-ocean epochs during accretion, evolve into the worlds we observe today. Many interacting processes govern the long-term interior, atmosphere, and surface state of low-mass planets; bringing models of each into one coherent framework captures that interconnectedness. Initial generations focus on the mantles of super-Earths and sub-Neptunes, and the volatile cycling between atmosphere and interior over geological time, building toward describing the emergence of temperate, potentially habitable surface conditions.
Quantifying model uncertainty
Many parameters, from rock rheology to molecular cross-sections, underpin every ensemble. By sensitivity-testing evolutionary outcomes against the uncertainty in those parameters, Interra reveals how "fuzzy" the abiotic baseline is: the range of planetary states reachable without life. Knowing which parameters matter most, yet are least constrained, shows where to be cautious in our inferences and motivates the experimental work that would sharpen them.
Evolutionary inversion
Today’s exoplanet interpretation leans on free retrieval, inferring an atmosphere’s composition from its spectrum without asking whether that atmosphere could physically have come to exist. Interra builds the ensembles into a framework for evolutionary inversion: inference constrained by how a planet actually evolves. It rules out histories that are physically implausible, carries forward-model uncertainty through to the result, and ships fast emulators (statistical surrogates of the full simulations) alongside each data release, so the community can use them in their own retrieval workflows.