Bayesian Analysis for Remote Biosignature Identification on exoEarths

The Habitable Worlds Observatory (HWO) is the next flagship mission after the Nancy Grace Roman Space Telescope that has the unique goal of characterizing Earth-like planets around Sun-like stars. On board, there will be a coronagraph which blocks the light from the star so that we can see the light emitted from the orbiting planets.

When characterizing these exoplanets, we want to be able to detect biosignatures - substances that indicate evidence for the presence of life - including water, methane, oxygen, ozone, and carbon dioxide. Each molecule has a unique spectrum and have spectral features at specific wavelengths. When determining the optimal long-wavelength cut-off for the HWO coronagraph, we want to prioritize being able to detect all of these molecules.

However, as we go longer in wavelength in the NIR, we start to see more noise being produced by the coronagraph which requires cooling. Thus, my job is to understand what conditions are required to strongly detect carbon dioxide at varyings SNRs, wavelengths, and molecular abundances of carbon dioxide, methane, and water while being mindful about cooling requirements.