---
title: ExEP Science Gap List Overview
url: https://www.emergentmind.com/topics/exep-science-gap-list
type: topic
---

# ExEP Science Gap List Overview

Searching arXiv for the core ExEP Science Gap List and closely related ExEP precursor and SAG reports.
First, I’ll identify the central ExEP Science Gap List paper and a few companion papers explicitly connected to ExEP science gaps, HWO target characterization, and earlier ExoPAG/SAG framing.
The Exoplanet Exploration Program (ExEP) Science Gap List is the NASA Exoplanet Exploration Program’s annual compilation of “science gaps,” defined as either “the difference between knowledge needed to define requirements for specified future NASA exoplanet missions and the current state of the art,” or “knowledge which is needed to enhance the exoplanet science return of current and future NASA exoplanet missions.” It is annually updated, and input is solicited from the exoplanet community via ExoPAG. In its 2025 revision, the list contains seventeen current gaps spanning atmospheric spectroscopy, occurrence rates, yield estimation, host-star characterization, stellar jitter, confirmation and orbit determination, direct-imaging target preparation, exozodiacal dust, planet radii, atmospheric opacities and aerosols, interior structure, stellar contamination, biosignatures, and planet formation [2507.18665].

## 1. Institutional role and antecedents

ExEP is chartered by the NASA Astrophysics Division to carry out science, research, and technology tasks that advance NASA’s science goals for exoplanets [2507.18665]. Within that programmatic setting, the Science Gap List functions as a requirements-facing document: some gaps directly inform future mission definition, while others are aimed at increasing the science return of current and future observatories.

Earlier ExoPAG and SAG activities established much of the structure later formalized in the gap list. The SAG15 report on future direct-imaging missions organized its questions into three groups—properties of planetary systems, properties of individual planets, and planetary processes—and asked what data quality, sample size, cadence, and spectral coverage would be required to answer them [1708.02821]. SAG17, focused on “Resources Needed for Planetary Confirmation and Characterization,” addressed the observational infrastructure required after transit discovery, especially for TESS, and emphasized that while a planet discovery may be a one-time event, deeper understanding of a planetary system is an ongoing process requiring observations with better precision over longer time spans [1810.08689].

These antecedents show that ExEP gap analysis has never been limited to a single measurement class. It includes mission-driving unknowns such as occurrence rates and biosignature interpretation, but also observational bottlenecks such as host-star characterization, high-resolution imaging, photometric ephemeris maintenance, precision radial velocities, and data infrastructure.

## 2. The seventeen current science gaps

The 2025 ExEP Science Gap List enumerates the following seventeen gaps [2507.18665].

| ID | Science gap |
|---|---|
| SCI-01 | Spectroscopic observations of the atmospheres of small exoplanets |
| SCI-02 | Modeling exoplanet atmospheres |
| SCI-03 | Spectral signature retrieval |
| SCI-04 | Planetary system architectures: occurrence rates for exoplanets of all sizes |
| SCI-05 | Occurrence rates and uncertainties for temperate rocky planets |
| SCI-06 | Yield estimation for exoplanet direct imaging missions |
| SCI-07 | Intrinsic properties of known exoplanet host stars |
| SCI-08 | Mitigating stellar jitter as a limitation to sensitivity of dynamical methods to detect small temperate exoplanets and measure their masses and orbits |
| SCI-09 | Dynamical confirmation of exoplanet candidates and determination of their masses and orbits |
| SCI-10 | Observations and analyses of direct imaging targets |
| SCI-11 | Understanding the abundance and distribution of exozodiacal dust |
| SCI-12 | Measurements of accurate transiting planet radii |
| SCI-13 | Properties of atoms, molecules and aerosols in exoplanet atmospheres |
| SCI-14 | Exoplanet interior structure and material properties |
| SCI-15 | Quantify and mitigate the impacts of stellar contamination on transmission spectroscopy for measuring the composition of exoplanet atmospheres |
| SCI-16 | Complete the inventory of remotely observable exoplanet biosignatures and their false positives |
| SCI-17 | Understanding planet formation and disk properties |

Taken sequentially, the list begins with the acquisition and interpretation of atmospheric spectra, moves through the demographic quantities that set mission yields, then addresses stellar and dynamical limitations on planet discovery and characterization, and ends with the laboratory, theoretical, and formation context needed to interpret exoplanets as physical systems. The document therefore couples observational astrophysics, statistics, stellar physics, instrument simulation, laboratory spectroscopy, planetary interiors, and astrobiology within a single planning framework [2507.18665].

Several of the gaps are explicitly requirement-setting for the Habitable Worlds Observatory. SCI-05 identifies $\eta_e$ as a driver of telescope diameter, coronagraph contrast, and mission lifetime, with current estimates spanning approximately $0.1$–$1.5$ and a factor-of-$3$ systematic uncertainty that should be reduced to less than a factor of $2$ [2507.18665]. SCI-06 treats yield estimation as a formal mission-design problem, while SCI-10 and SCI-11 translate target screening and exozodiacal dust into direct-imaging integration-time and false-positive constraints. Others, such as SCI-13 through SCI-16, determine whether future spectra can be interpreted robustly once they are obtained.

## 3. Quantitative frameworks

Three quantitative frameworks structure a substantial fraction of the list: occurrence-rate inference, habitable-zone rocky-planet frequency, and direct-imaging yield estimation [2507.18665].

For planetary demographics, SCI-04 formulates the point-wise occurrence density function $\Phi(R_p,P)$ in terms of completeness- and reliability-corrected detections:
$$
\hat\Phi(R_p,P) \;=\; \frac{1}{\Delta R_p\,\Delta P}
\sum_{i\,\in\,\mathrm{bin}} \frac{1}{N_\star\,C_i\,R_i}.
$$
A continuous representation can then be fit with a parametric form such as
$$
\Phi(R_p,P) \;=\; k\,R_p^{\alpha}\,P^{\beta}\,\exp\bigl[-\gamma\,\bigl(R_p + P\bigr)\bigr].
$$
The explicit need is for uniformly processed catalogs and metadata from Kepler, TESS, RV, microlensing, and Gaia astrometry, together with population-synthesis codes that can ingest multi-technique demographics and output synthetic universes for mission simulators [2507.18665].

For temperate rocky planets, SCI-05 defines a nonparametric habitable-zone occurrence estimator
$$
\hat\eta_e \;=\; \frac{1}{N_\star}
\sum_{i\,\in\,\mathrm{HZ},\,0.8\le R_p\le1.4}
\frac{1}{C_i\,R_i}.
$$
The state of the art cited in the list includes Kepler-based analyses yielding $\eta_e \approx 0.24^{+0.46}_{-0.16}$ at $68\%$ confidence and factor-$3$ uncertainty, as well as an atmospheric-loss-informed estimate of $\eta_e \approx 0.09\pm0.03$ [2507.18665]. The formal problem is therefore not simply to enlarge a sample, but to close an error budget involving habitable-zone boundaries, candidate reliability, small-planet completeness, and stellar radii.

For direct imaging, SCI-06 writes a canonical yield integral
$$
Y \;=\; \iint \Phi(R_p,P)\;S(\alpha,R_p,P)\;C_\mathrm{obs}(\alpha)\;
\Theta(\mathrm{S/N} \ge \Gamma)\;dR_p\,dP,
$$
where $\Phi(R_p,P)$ is the occurrence density, $S(\alpha,R_p,P)$ is the geometric albedo phase-function term, $C_\mathrm{obs}(\alpha)$ is the instrument contrast at angular separation $\alpha$, and the indicator function enforces the available-time signal-to-noise constraint [2507.18665]. In this framework, yield is not a single astrophysical number but the output of coupled assumptions about occurrence rates, exozodi, binaries, coronagraph or starshade performance, scheduling, and detector noise. ExEP therefore identifies community-maintained, open-source yield simulators as a required capability, and cites Adaptive Yield Optimization, ExoSIMS, Bioverse, and ExoVista as the relevant methodological baseline [2507.18665].

## 4. Precursor-science implementations

Mission-specific precursor studies show how the abstract science gaps translate into concrete missing data and resource deficits.

For HWO target characterization, Harada and collaborators assembled an enriched catalog of $164$ “most accessible” targets and quantified six major missing-data categories. Only $33/164$ stars have reliable space-based UV measurements; only $40/164$ have a mid-IR measurement; phosphorus abundance measurements exist for only $11/164$ stars; TESS flare rates and X-ray detections exist for $46/164$ stars each; and optical variability metrics exist for $78/164$ stars [2401.03047]. These numbers directly instantiate SCI-07 and SCI-10, and they propagate into habitability modeling, direct-imaging contrast and integration times, sample selection, and mission risk.

For transit-survey follow-up, SAG17 quantified the observing load required to confirm and characterize TESS candidates. Seeing-limited time-series photometry for postage-stamp candidates was estimated at $5{,}000$–$10{,}000$ h, corresponding to $\sim500$–$1{,}000$ telescope-nights, with a deficit of $\sim800$–$1{,}000$ nights yr$^{-1}$ on small telescopes for postage stamps alone. Precision RV spectroscopy was estimated at $\sim2{,}000$ h for $\sim200$ postage-stamp targets, while current RV capacity was $\sim50$–$100$ nights yr$^{-1}$ and the deficit was $\sim150$–$300$ nights yr$^{-1}$ for postage stamps [1810.08689]. High-angular-resolution imaging and stellar spectroscopy show comparable large-telescope and 1–4 m bottlenecks. These deficits map directly onto SCI-09 and SCI-12, but they also bear on SCI-07 because stellar parameters and companion vetting control planet radii and density estimates.

For target prioritization, the HWO ExEP Mission Star List has already been used in a predictive ranking framework based on simulated planetary systems. For each star, $N_{\rm MC}=10^5$ systems are generated, with a rocky habitable-zone probability
$$
P_{\rm rocky}(HZ\mid\star)
$$
and a habitable-moon probability
$$
P_{\rm moon}(HZ\mid{\rm giant}),
$$
combined with a continuous-habitable-zone factor
$$
M_{\rm joint} = \bigl[P_{\rm rocky}(HZ)+P_{\rm moon}(HZ)\bigr]\times CHZ_2.
$$
Across $164$ stars, the resulting metric spans $[0,\,0.30]$, with median $\approx0.02$ and mean $\approx0.05$; the highest-ranked stars are $61$ Vir, $\rho$ CrB, HD $219134$, $\tau$ Cet, and $55$ Cnc [2606.04105]. This is a direct operationalization of SCI-04 through SCI-06: occurrence models feed ranking, ranking feeds survey strategy, and survey strategy feeds yield.

## 5. Cross-gap dependencies and prioritization

The list is explicitly organized as an interdependent system rather than a set of isolated topics. The documented dependency network includes SCI-04 and SCI-05 feeding SCI-06; SCI-06 together with SCI-11 and SCI-10 feeding HWO architecture trades; SCI-07 feeding SCI-01, SCI-12, SCI-15, SCI-08, and SCI-09; SCI-08 and SCI-09 feeding mass measurements, scheduling, and atmospheric interpretation; SCI-02, SCI-03, and SCI-13 feeding interpretation of all spectra; SCI-14 feeding density and atmosphere-model constraints; SCI-15 feeding transit-spectroscopy stability requirements; SCI-16 imposing wavelength coverage and spectral resolution; and SCI-17 informing priors on composition and architecture [2507.18665].

The recommended priority order for the next $3$–$5$ years is similarly explicit. First is SCI-05, to reduce the factor-of-$3$ uncertainty on $\eta_e$ to factor $\lesssim2$ and thereby fix telescope diameter and mission time. Second is SCI-06, to converge on standard yield tools and metrics. Third are SCI-08 and SCI-09, which define the mass-and-orbit strategy and the mitigation of stellar jitter. Fourth is SCI-11, because exozodi directly drives mission-time overruns and spectral signal-to-noise degradation. Fifth are SCI-07 and SCI-10, which complete companion census and stellar characterization for the top $\sim100$ HWO targets. Sixth are SCI-02, SCI-03, SCI-13, and SCI-15, which prepare robust end-to-end interpretation pipelines. Seventh are SCI-01 and SCI-12. Eighth are SCI-14, SCI-16, and SCI-17, which are described as having broad science return and context but lower short-term impact on mission design trades [2507.18665].

This prioritization is strongly mission-facing. It does not imply that lower-ranked gaps are scientifically secondary; rather, it reflects which unknowns most strongly perturb design closure, technology readiness, and survey planning on near-term timescales.

## 6. Scientific implications and broader context

Closing the ExEP science gaps has direct consequences for mission architecture and scientific return. The list states that defining $\eta_e$ and $\Phi(R_p,P)$, together with exozodi statistics, directly sets telescope diameter, inner working angle, throughput, and mission duration trades required to ensure at least $25$ habitable-zone terrestrial spectra [2507.18665]. Mature yield simulators enable quantitative comparison of coronagraph versus starshade, aperture versus lifetime, and survey versus characterization time. Host-star characterization and target screening refine target lists and wavefront-control and pointing-jitter budgets. Transit ephemerides and precise radii protect the efficiency of JWST, ARIEL, and HWO spectroscopy. Retrieval frameworks, opacity databases, and stellar-contamination models set the spectral resolving power and signal-to-noise that future missions must actually deliver.

The list also embeds a broader physical agenda. SCI-14, concerning exoplanet interior structure and material properties, intersects directly with work on the exoplanet radius valley. Zeng and collaborators argue that the $\sim2\,R_\oplus$ valley is a compositional divide between predominantly rocky planets and ice-rich “water worlds,” rather than a smooth continuum of finely tuned envelope masses, and use equilibrium temperature and water equations of state to interpret the transition [2201.02125]. This does not replace the official gap taxonomy, but it exemplifies the kind of theory-laboratory-observation coupling that SCI-14 calls for.

At the level of scientific lineage, the present list can also be read as a formalization of earlier direct-imaging science questions. SAG15 asked about the diversity of planetary architectures, the properties of exo-zodiacal disks, rotational periods and obliquities, oceans and continents, clouds and hazes, terrestrial photochemistry, atmospheric circulation, rocky-planet evolution, and geological activity, and tied each question to explicit requirements in contrast, spectral resolution, cadence, wavelength coverage, and target sample [1708.02821]. The current Science Gap List extends that style of analysis beyond direct imaging into a program-wide framework that links discovery, characterization, interpretation, and mission design.

In that sense, the ExEP Science Gap List is both a scientific inventory and a planning instrument. It specifies what must be measured, modeled, simulated, and benchmarked before future flagship exoplanet missions can be designed with defensible requirements and before the resulting spectra, masses, radii, and orbital architectures can be interpreted with controlled systematics [2507.18665].

Source: https://www.emergentmind.com/topics/exep-science-gap-list