Towards the Habitable Worlds Observatory: Retrieval of Reflection Spectra from Evolving Earth Analogues using 1D CNNs

The Habitable Worlds Observatory (HWO) will characterize Earth-like exoplanets from low-resolution reflected-light spectra. To assess how atmospheric evolution shapes what can be recovered, we trained a convolutional neural network on 109,945 spectra generated with the Planetary Spectrum Generator for Archean, Proterozoic, and Modern atmospheres under LUVOIR-B and HabEx/SS observing assumptions, separating the contributions of observational noise and model variability. We find that recovery depends jointly on abundance and spectral structure. Across eras, O 2 recovery weakens in Proterozoic atmospheres and CH 4 becomes reliable only when abundant, while an isolated UV band yields robust Modern O 3 recovery (R 2 0.98) despite poor Modern CH 4 recovery (R 2=0.32 - 0.35). Without being supplied feature locations, the network assigns 58 - 71% of total attribution to visible wavelengths, while the ultraviolet remains decisive for O 3 and the near-infrared for CH 4 and CO 2. Aggregate retrieval leverage declines beyond approximately 1.7,μ m, supporting a practical long-wavelength boundary in this setup. These wavelength dependencies also shape detectability. At baseline noise, 83 - 85% of Modern O 2 cases satisfy a 3σ criterion, while Proterozoic O 2 remains below 50% when noise is halved and Modern CH 4 remains below 3σ as observational noise approaches zero because model uncertainty caps its significance. Together, these results imply molecule-specific priorities for HWO trade studies. Low-abundance O 2 retains considerable leverage from improved observational quality while motivating better inference, whereas at the 1,000-hour benchmark O 3 and Modern CH 4 show predominantly model-associated uncertainty in these networks, motivating broader training coverage and improved uncertainty calibration.