An Agnostic Machine Learning Model of Photosynthetic Habitability
The search for exoplanet biosignatures is guided by whether planetary environments can sustain photosynthesis.
As such, the Photosynthetic Habitable Zone (PHZ) was recently proposed, as the overlap between the canonical habitable zone and the orbital range where stellar irradiance is sufficient to drive photosynthesis. Existing PHZ estimates rely on empirical light-response curves from Earth phytoplankton, and thus include implicit Earth-centric biases.
We introduce an agnostic PHZ derived from a generalized model of photosynthesis grounded in thermodynamics and redox chemistry, without reference to model organisms. The model is built on a generic photochemical reaction in which photon capture couples oxidation of a donor molecule to the reduction of CO2.
The optical properties and CO2 reduction rate are optimized against irradiance spectra for exoplanets orbiting main-sequence stars, using a genetic algorithm that mimics evolution by natural selection. Our simulations predict that photosynthetic organisms compensate for reduced flux by evolving larger light-harvesting structures.
As a result, photosynthetic viability declines only linearly with orbital distance, despite stellar flux falling off quadratically. As such, the agnostic PHZ expands well beyond previous Earth-based estimates. Earth-like (visible light) oxygenic photosynthesis is flux-limited at the outer habitable zone for cool M-dwarf stars; however, both anoxygenic photosynthesis and a hypothetical, NIR-driven oxygenic photosynthesis are viable across the entire habitable zone for M, K, and G stars.
This implies that M-dwarf exoplanets could sustain robust oxygenic photosynthesis, though it would be different to that found on Earth, presenting reflectance biosignatures in the NIR band rather than the visible.
Callum Gray, Cassandra Hall, Stefano Santabarbara, Klaus Schmidt-Rohr, Andrew Ringham, Edward Gillen, Thomas J. Haworth, Christopher D. P. Duffy
Comments: 17 pages main body, 5 figures. Submitted to MNRAS
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2606.24458 [astro-ph.EP] (or arXiv:2606.24458v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2606.24458
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Submission history
From: Callum Gray
[v1] Tue, 23 Jun 2026 11:45:54 UTC (3,925 KB)
https://arxiv.org/abs/2606.24458
Astrobiology, Astronomy, Exoplanet, Biosignature,
