Detecting atmospheres around Earth-sized exoplanets is a critical step toward identifying potentially habitable worlds, yet such atmospheres produce extremely weak observational signals. This study investigates the detectability of an Earth-like atmosphere using ultraviolet transmission spectroscopy and examines which wavelength ranges provide the strongest atmospheric signatures during planetary transits.
An Earth-like atmospheric model was implemented using realistic altitude profiles of temperature, pressure, and molecular abundances based on terrestrial atmospheric data. Ultraviolet absorption cross sections of key atmospheric species (including oxygen, ozone, nitrogen oxides, and sulfur dioxide), derived from existing laboratory spectroscopic databases, were incorporated
into the model to compute wavelength-dependent absorption of stellar radiation. The cumulative absorption was translated into an effective atmospheric height, representing the apparent increase in planetary radius during transit, while accounting for instrumental spectral resolution and realistic stellar ultraviolet emission.
The results show that atmospheric detectability is strongly wavelength dependent. Ultraviolet wavelengths yield significantly larger transmission signals than longer wavelengths due to efficient absorption by oxygen- and ozone-related features, which dominate the atmospheric signature. Other trace gases contribute smaller effects. The predicted transit depth variations reach several parts per million for an Earth-sized planet, with detectability strongly influenced by host star brightness through its impact on signal-to-noise ratio.
This study demonstrates how Earth serves as a reference system for interpreting exoplanet observations. These findings highlight ultraviolet transmission spectroscopy as a promising approach for future space missions, including concepts such as the Habitable Worlds Observatory, aimed at detecting Earth-like atmospheres and assessing planetary habitability beyond the Solar System.
In addition, the model explicitly evaluates photon budgets and noise sources, enabling quantitative estimates of the observation time required to detect atmospheric signatures at a given signal-to-noise ratio.
