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arxiv: 2606.04105 · v1 · pith:Z2L6RII2new · submitted 2026-06-02 · 🌌 astro-ph.EP · astro-ph.IM

Predictive Rankings of the Probability for Temperate Terrestrial Worlds for the HWO ExEP Mission Star List

Jamie Dietrich , Katelyn Ruppert , Austin Ware , Patrick A. Young This is my paper

Pith reviewed 2026-06-28 07:51 UTC · model grok-4.3

classification 🌌 astro-ph.EP astro-ph.IM
keywords exoplanetshabitable zonedirect imagingoccurrence ratestemperate terrestrial planetsHWOplanetary system architecture
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The pith

Simulations rank some stars on the HWO target list at over 50 percent probability of hosting a temperate terrestrial planet.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper models planetary systems by placing small planets at periods interior to giant planets according to measured occurrence rates, then applies this model to every star on the HWO ExEP Mission Star List. It calculates the fraction of simulated systems that place a rocky planet inside the star's circumstellar habitable zone. The resulting probabilities reach above 50 percent for some targets, which would directly affect how the mission allocates its blind-search time. The work also examines cases in which a giant planet sits in or just outside the habitable zone and could host a temperate moon.

Core claim

Assuming a simple model of planetary systems with small planets well-ordered in period interior to giant planets based on their respective occurrence rates, some systems on the HWO ExEP Mission Star List are upwards of 50 percent likely to host a temperate terrestrial planet. The same modeling framework assigns probabilities to the possibility that a giant planet hosts a temperate terrestrial moon.

What carries the argument

Simulated planetary systems that place small planets interior to giant planets according to occurrence rates, then count how often a small planet lands in the habitable zone.

If this is right

  • Prioritizing the highest-ranked stars on the list could raise the expected number of temperate terrestrial planets found by HWO direct imaging.
  • Refined measurements of small-planet occurrence rates at separations less than or equal to 1 AU would produce updated probability rankings.
  • The same framework assigns non-zero probability that a giant planet hosts a temperate terrestrial moon.
  • Conditional occurrence rates between small and giant planets directly control the final probabilities assigned to each star.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future occurrence-rate surveys focused on the inner AU could be designed to minimize uncertainty in exactly these rankings.
  • The same simulation approach could be applied to target lists for other direct-imaging concepts to compare expected yields.
  • If the ordering assumption is relaxed, the probability spread across the star list would widen or narrow depending on the new architecture model.

Load-bearing premise

Planetary systems consist of small planets that are well-ordered in period interior to giant planets according to their separate occurrence rates.

What would settle it

A measurement of the occurrence rate of small planets at orbital distances less than or equal to 1 AU that differs substantially from the rates used in the simulations.

Figures

Figures reproduced from arXiv: 2606.04105 by Austin Ware, Jamie Dietrich, Katelyn Ruppert, Patrick A. Young.

Figure 1
Figure 1. Figure 1: The simulated occurrence rates of planets in both simulated (blue) and known (orange) planetary systems around nearby stars. Top left: All planet periods. Top right: All planet radii. Bottom left: Small planet (R < 6 R⊕) periods. Bottom right: Giant planet (R > 6 R⊕) periods. Additional predicted planets in the systems with known planets are not shown. (C. K. Harada et al. 2024), which report uniformly de￾… view at source ↗
Figure 2
Figure 2. Figure 2: Probabilities of a temperate terrestrial planet for the conservative (top) and optimistic (bottom) assumptions for searching for potentially habitable worlds in nearby star systems. Simulated values are for systems with no known planets, predicted values are for systems with known planets. Systems with a probability of 0 are not shown. The number of counts in each probability bin is the number of EMSL star… view at source ↗
Figure 3
Figure 3. Figure 3: The simulated and predicted probability for Earth-like planets compared to the stellar effective temper￾ature for each of the 164 targets on the HWO EMSL. The habitable zone moves outwards with increasing temperature, but the occurrence rate of giant planets also increases with orbital distance, thus limiting the simulated probability of finding small rocky planets in the habitable zones of hotter stars. a… view at source ↗
Figure 4
Figure 4. Figure 4: The CHZ2 metric compared to the stellar ef￾fective temperature. In general, the CHZ2 has a positive trend with stellar temperature until ∼ 6500 Kelvin, where the stars start to become too short-lived on the main se￾quence to have a stable circumstellar habitable zone for 2 billion years. This is in contrast to the trend of the probabil￾ity in finding a temperate terrestrial planet, which decreases with ste… view at source ↗
read the original abstract

The Habitable Worlds Observatory (HWO) is NASA's flagship mission design from the Decadal Survey on Astronomy and Astrophysics 2020, meant to observe temperate terrestrial planets via direct imaging and use direct spectroscopy of exoplanet reflected light to investigate their atmospheres for biosignatures. However, there are no known stars in the solar neighborhood conducive to direct imaging observations that are currently known to host rocky planets in their circumstellar habitable zones. Thus, HWO will most likely be running a blind survey; however, prioritizing the rankings of its target stars will help to potentially increase the yield of temperate terrestrial planets observed. Here we use simulated planetary systems with both small and giant planets to test which stellar systems among the HWO Exoplanet Exploration Program (ExEP) Mission Star List are most likely to host a rocky planet with the right temperature to sustain life on its surface. Assuming a simple model of planetary systems with small planets well-ordered in period interior to giant planets based on their respective occurrence rates, we find that some systems are upwards of 50% likely to host a temperate terrestrial planet. We also consider the possibility of a giant planet in or just beyond the circumstellar habitable zone that could host a temperate terrestrial moon capable of hosting life. Additional observations to refine the occurrence rates of small planets at orbital distances $\lesssim$ 1 AU and conditional rates between small and giant planets will refine these analyses and provide updates to these rankings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript develops probabilistic rankings for stars on the HWO ExEP Mission Star List according to the likelihood that each hosts a temperate terrestrial planet. It employs Monte Carlo simulations of planetary systems that draw small-planet and giant-planet occurrence rates from the literature and enforce a strict period-ordering rule placing all small planets interior to all giant planets; the resulting probabilities reach upwards of 50% for some systems. The work also considers the secondary case of temperate moons orbiting giant planets located in or near the habitable zone.

Significance. If the ordering assumption and input occurrence rates prove robust, the rankings would supply a concrete, observationally motivated prioritization scheme for the Habitable Worlds Observatory target list, directly addressing the absence of known temperate terrestrials around nearby stars suitable for direct imaging. The approach is transparent in its use of published occurrence statistics and could be updated as new rate measurements become available.

major comments (2)
  1. [Abstract] Abstract: the central quantitative claim that 'some systems are upwards of 50% likely to host a temperate terrestrial planet' is generated solely by feeding literature occurrence rates into the assumed ordering model; the manuscript provides neither empirical calibration of the strict interior-period-ordering rule nor sensitivity tests to violations of that rule or to the conditional rate P(small|giant).
  2. [Model description (methods section)] Model description (methods section): the Monte Carlo procedure enforces 'small planets well-ordered in period interior to giant planets' without reference to observed multi-planet architectures that include giants interior to the habitable zone or to measured conditional occurrence statistics; this assumption is load-bearing for all reported probabilities and rankings.
minor comments (1)
  1. [Abstract] The abstract states that 'additional observations to refine the occurrence rates... will refine these analyses' but does not indicate where in the manuscript the current rate values and their uncertainties are tabulated or propagated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our predictive ranking approach. We address each major comment below and outline planned revisions to strengthen the presentation of model assumptions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central quantitative claim that 'some systems are upwards of 50% likely to host a temperate terrestrial planet' is generated solely by feeding literature occurrence rates into the assumed ordering model; the manuscript provides neither empirical calibration of the strict interior-period-ordering rule nor sensitivity tests to violations of that rule or to the conditional rate P(small|giant).

    Authors: The reported probabilities are explicitly model-dependent, as stated in the abstract ('Assuming a simple model...'). The strict ordering draws from the dominant observed trend in Kepler multi-planet systems where small planets are typically interior to giants. We agree that sensitivity tests are warranted; in revision we will add a dedicated subsection quantifying how rankings change under relaxed ordering (e.g., allowing 10-20% of giants interior to small planets) and under varied P(small|giant) drawn from available conditional statistics. revision: yes

  2. Referee: [Model description (methods section)] Model description (methods section): the Monte Carlo procedure enforces 'small planets well-ordered in period interior to giant planets' without reference to observed multi-planet architectures that include giants interior to the habitable zone or to measured conditional occurrence statistics; this assumption is load-bearing for all reported probabilities and rankings.

    Authors: The ordering assumption is presented as a first-order simplification motivated by occurrence-rate studies showing small planets preferentially at shorter periods. We will expand the methods section to cite specific observed systems with inner giants and to reference the limited conditional occurrence measurements currently available. As noted above, we will also include sensitivity tests that relax the strict ordering to demonstrate robustness of the top-ranked targets. revision: partial

Circularity Check

1 steps flagged

Reported probabilities are direct re-expression of input occurrence rates under the assumed ordering model

specific steps
  1. fitted input called prediction [Abstract]
    "Assuming a simple model of planetary systems with small planets well-ordered in period interior to giant planets based on their respective occurrence rates, we find that some systems are upwards of 50% likely to host a temperate terrestrial planet."

    The 50% likelihood figures are generated by feeding fitted occurrence rates into the Monte-Carlo simulation under the ordering assumption; the output probabilities are therefore a direct functional re-expression of the input rates rather than an independent prediction.

full rationale

The paper's central quantitative outputs (per-star probabilities and rankings) are produced by Monte-Carlo sampling that directly ingests literature occurrence rates for small and giant planets and applies the stated ordering assumption. No independent derivation or calibration of the ordering rule or conditional rates is provided; the numerical results therefore reduce to a re-expression of those fitted inputs. This matches the fitted-input-called-prediction pattern but does not involve self-citation chains or self-definitional loops, so the circularity is partial rather than total.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central probabilities rest on occurrence rates taken from prior observational studies and on the untested ordering assumption between small and giant planets; no new entities are postulated.

free parameters (2)
  • small-planet occurrence rates at <1 AU
    Fitted values from literature surveys that directly determine the simulated probabilities
  • giant-planet occurrence rates
    Fitted values from literature that set the conditional placement of giants relative to small planets
axioms (1)
  • domain assumption small planets are well-ordered in period interior to giant planets
    Invoked in the abstract as the basis for the simulation architecture

pith-pipeline@v0.9.1-grok · 5802 in / 1321 out tokens · 18403 ms · 2026-06-28T07:51:57.036362+00:00 · methodology

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