Ultraviolet Reflected-Light Spectroscopy of Modern Earth Analogs with Starshade Observations
Year: 2026
Type: preprint
Abstract: Characterizing rocky, habitable zone planets in reflected light is a key science objective for future direct imaging missions, and the ultraviolet (UV) is uniquely suited for supporting the characterization of Earth-like atmospheres. The Hartley-Huggins ozone band at $0.25 ~μ\mathrm{m}$ remains detectable at low spectral resolution down to $0.1\%$ PAL $\mathrm{O}_2$, providing a sensitive proxy for oxygen-bearing chemistry even for Proterozoic-like atmospheres where the visible and near-infrared $\mathrm{O}_2$ features become inaccessible. This work uses an end-to-end framework for UV starshade spectroscopy of modern Earth analogs that integrates high-fidelity observation simulation, conservative post-processing, and Bayesian spectral retrieval to quantify ozone detection significance. The framework is applied across 108 simulated scenes spanning a range of planet phase angles, system inclinations, and exozodiacal dust densities. The results establish that robust ozone detection with UV-only, $R=7$ reflected light spectroscopy requires a photometric $\mathrm{SNR} \sim 30$. UV starshade observations with the Habitable Worlds Observatory achieve this threshold within 3-day integrations for solar-type systems at 10 parsecs with inclinations up to $60^\circ$ at favorable phases ($\leq 90^\circ$). Above this SNR threshold, ozone detections are robust to systematic bias from imperfect exozodi calibration within $\sim30\%$, and the median retrieved ozone abundance remains accurate to within one order of magnitude of the true value.
Source: arXiv (Cornell University)
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: Acquisition of a Scalable Storage Cluster for Data Intensive NIH Research, G23037955, G2676722403, Research Infrastructure: MRI: Acquisition of a Big Data HPC Cluster for Interdisciplinary Research and Training, MRI: Acquisition of a Big Data Compute Cluster for Interdisciplinary Research, Graduate Research Fellowship Program (GRFP), G6605062335, G6661425536, G830101876, G8438684293
OpenAlex ID: W7221770233
