Determining the Detectability of H2O with Photometric Observations using Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE)

TL;DR

The paper addresses whether water vapor can be detected in Earth-like exoplanet atmospheres via photometric observations, as opposed to traditional spectroscopy, by evaluating detectability across photometric bandwidths, point placements, and normalized exposure using a BARBIE framework. It combines Bayesian spectral retrievals on PSG-generated grids with yield optimizations (AYO) under Habitable Worlds Observatory-like instrument assumptions to compare photometry and spectroscopy for the 0.94 μm H2O feature. The key finding is that H2O is strongly detectable photometrically with a minimum of three spectral points across 10%, 5%, and 3% bandwidths at moderate exposure times, though very low abundances (below ~$1\times10^{-3}$ VMR) favor spectroscopy; detector noise and telescope aperture strongly influence the preferred observing method. The work highlights that photometry can be a practical, efficient approach for initial H2O assessment under higher-noise conditions, while high-precision spectroscopy remains essential for low-noise regimes and for detecting other biosignatures like O2; these results inform mission design tradeoffs for HWO and future exoplanet biosignature surveys.

Abstract

We examine the detectability of water (H2O) in the reflected-light spectrum of an Earth-like exoplanet assuming a photometric observational approach rather than spectroscopic. By quantifying the detectability as a function of normalized exposure time, resolving power (R), and amount of spectral points, we can constrain whether spectroscopy or photometry is the more efficient observing procedure to detect H2O at varying abundances by measuring the broad 0.94 microns absorption feature using the Habitable Worlds Observatory (HWO). We simulate low-resolution spectroscopy (R = 10, 20, 30, presented as photometric bandwidth fraction 10%, 5%, 3% herein) as a proxy for narrow-band photometric observations, and constrain the wavelength range from 0.85 - 1.05 microns, to narrow focus on the 0.9 microns feature. We then constrain the number of spectral points to 2 or 3 points at each bandwidth fraction to investigate the impact of spectral point placement on detectability. Additionally, we take the signal-to-noise ratios (SNRs) for strong H2O detection and calculate the resultant exoplanet yields assuming photometric observation and compare to the yields from higher-resolution spectroscopic observations under different noise instances, characterization wavelength, noise floors, and aperture sizes. We find that H2O is strongly detectable at all bandwidth fractions depending on the spectral point placement, requiring a minimum of 3 spectral points, at a variety of normalized exposure time depending on the abundance of H2O. We also find that the detector noise is the main driver in determining whether photometry or spectroscopy results in higher yields. Photometry is the preferred observational method in high-noise cases, while spectroscopy is preferred in low-noise scenarios.

Figures (8)

• Figure 1: The H2O absorption feature with our simulated photometric points overlaid. Plotted are a spectrum at R = 140 resolving power, spectral points at 10%, 5%, and 3% bandwidths in light pink squares, purple triangles, and dark pink stars respectively. The width of the wavelength bin is shown in horizontal lines for the respective photometric points in the bottom panel. Top panel: our full spectra with all sequential points.Middle panel: our two-point photometry observation scenario. Bottom panel: our three-point photometry observation scenario. All consist of a 50% cloudy model. The x-axis is wavelength, constrained from 0.8 to 1.05 $\umu\math{\umu} \textrm{m}$, and the y-axis is geometric albedo (I/F). At wider bandwidths, the feature is more smoothed.
• Figure 2: Heat map plots illustrating detection strength as a function of normalized exposure time and varying resolving abundance at varying Earth H2O abundances with 2 spectral points permitted at each bandwidth. $\mathrm{lnB}$$\ge$5 is a strong detection, 2.5$\le$$\mathrm{lnB}$$\le$5 is a weak detection, and $\mathrm{lnB}$$\le$2.5 is unconstrained. A modern abundance of H2O is considered to be $3\times10^{-3}$. With two spectral points regardless of bandwidth, H2O detectability is low, requiring higher abundances.
• Figure 3: Heat map plots illustrating detection strength as a function of normalized exposure time and varying resolving abundance at varying Earth H2O abundances with 3 spectral points permitted at each bandwidth. $\mathrm{lnB}$$\ge$5 is a strong detection, 2.5$\le$$\mathrm{lnB}$$\le$5 is a weak detection, and $\mathrm{lnB}$$\le$2.5 is unconstrained. Note that the y-axis is cropped to 3.84, to more directly compare to Figure \ref{['fig:heatmap2pt']} which caps at 4.0. The key factor to photometric H2O detection is spectral point quantity.
• Figure 4: ExoEarth candidate (EEC) yields for three photometric scenarios (solid pink lines) and three spectroscopic scenarios (dashed purple lines) as a function of detector noise. Spectral observations result in higher yields at low noise cases, but photometric observations result in higher yields at higher noise cases.
• Figure 5: We present the exoEarth candidate (EEC) yields (y-axis) for 3 photometric bandwidths (10%, 5%, 3%) and 3 spectroscopic resolving powers (R = 50, 70, 140) over multiple different noise cases (x-axis), with other simulation variations. The left panel (a) has moved the detection wavelength to the depth of the feature vs the long wavelength point shown previously. The center panel (b) has no noise floor limitation. The right panel (c) has an increased aperture size, from a 6 meters to 8 meters inscribed. All other aspects are identical to Figure \ref{['fig:yields_long']}. Characterization wavelength placement and noise floor are not the main drivers in photometric vs spectroscopic observation preference - aperture contributes heavily, but the noise is the main decider in preferred observational method.