Imaging Venus-like Worlds: Spectral, Polarimetric, and UV Diagnostics for the Habitable Worlds Observatory

TL;DR

This paper discusses the Habitable Worlds Observatory's strategy to detect and analyze Venus-like exoplanets using spectral and polarimetric diagnostics, aiming to understand planetary habitability and atmospheric composition.

Contribution

It presents an updated list of Venus Zone exoplanets, proposes an integrated observation framework, and outlines requirements for future UV/optical/IR observations with HWO.

Findings

Increased known Venus Zone candidates to 370.

Framework for diagnosing sulfur chemistry and cloud physics.

Implications for HWO instrument requirements and target selection.

Abstract

Understanding planetary habitability requires a comparative approach that explores the divergent evolutionary outcomes of Earth and Venus. The Habitable Worlds Observatory (HWO) will be uniquely positioned to conduct a statistical and physical census of terrestrial exoplanets spanning the Venus Zone (VZ) and the Habitable Zone (HZ), enabling the detection and atmospheric characterization of post-runaway greenhouse worlds (``exoVenuses''). We present an updated list of VZ exoplanets, which raises the number of known candidates to 370. We describe a science case and an observing strategy for VZ exoplanets that integrates precursor exoplanet detection data and stellar characterization with HWO direct imaging, spectroscopy across the UV/optical/IR, and spectropolarimetry. Our proposed framework emphasizes a pathway toward the diagnosis of sulfur chemistry (SO$_2$) and aerosol physics (H$_2$SO$_4$ clouds/hazes), planetary redox states (O$_2$/O$_3$ false positives from hydrogen loss), and cloud microphysics detection (rainbow polarization). We quantify implications for HWO requirements, including UV access to 0.2--0.4 $\mu$m, optical/NIR coverage to $\gtrsim$1.5 $\mu$m, inner working angle (IWA) reaching 0.3--1.5 AU around nearby Sun-like stars, and the SNR/resolution needed for key features. Finally, we outline a community-driven path to producing robust demographic inferences and target selection for optimizing HWO observations.