Search

We present HZ_evolution, a Python package to characterize the habitable histories of exoplanets. Given inputs of a planet's current effective flux and host star properties, HZ_evolution calculates its instellation history, the evolution of the star's Habitable Zone, and the duration the planet spends inside or outside the Habitable Zone., Comment: Accepted for publication in Research Notes of the AAS

Future large, space-based observatories with starlight suppression technology, e.g., the Habitable Worlds Observatory (HWO), will directly image and characterize nearby Earth-like exoplanets. Prior limits on planet masses and system architectures from radial velocity (RV) measurements of potential exo-Earth hosts are critical to the success of HWO's science goals. Here, we present a uniform analysis of archival RVs from HIRES/Keck and HARPS/ESO of the most promising targets for the HWO exo-Earth survey. We analyze RVs and stellar activity indicators of 90 stars in the NASA ExEP Mission Star List and SPORES-HWO Catalog, finding 33 Keplerian signals associated with known planets and 12 signals associated with stellar activity. We also identify 5 new RV signals that we classify as either planet candidates or sources requiring confirmation, noting that the RV observations are biased toward cooler and less active stars. Assessing the sensitivity of the HIRES and HARPS data, we calculate RV limits ranging from $K_{\rm RV} = 0.6 \,{\rm m\,s}^{-1}$ (HD 10700) to $371 \,{\rm m\,s}^{-1}$ (HD 17925) in the middle of the conservative habitable zone (HZ), corresponding to projected planet masses of $5.4 \,{\rm M_\oplus}$ and $10.6 \,{\rm M_{Jup}}$ for those stars. The median HZ sensitivity limit of our sample is $M_{\rm p} \sin i \simeq 66 \,{\rm M_\oplus}$. This work demonstrates the need for future extreme precision radial velocity (EPRV) monitoring of high-priority targets for the next generation of DI missions that will search for habitable extrasolar systems. We advocate for the use of these results in developing future EPRV strategies., Comment: 55 pages, 12 figures, 9 tables, submitted to AAS Journals

The Habitable Worlds Observatory (HWO) will seek to detect and characterize potentially Earth-like planets around other stars. To ensure that the mission achieves the Astro2020 Decadal's recommended goal of 25 exoEarth candidates (EECs), we must take into account the probabilistic nature of exoplanet detections and provide "science margin" to budget for astrophysical uncertainties with a reasonable level of confidence. In this study, we explore the probabilistic distributions of yields to be expected from a blind exoEarth survey conducted by such a mission. We identify and estimate the impact of all major known sources of astrophysical uncertainty on the exoEarth candidate yield. As expected, eta_Earth uncertainties dominate the uncertainty in EEC yield, but we show that sampling uncertainties inherent to a blind survey are another important source of uncertainty that should be budgeted for during mission design. We adopt the Large UV/Optical/IR Surveyor Design B (LUVOIR-B) as a baseline and modify the telescope diameter to estimate the science margin provided by a larger telescope. We then depart from the LUVOIR-B baseline design and identify six possible design changes that, when compiled, provide large gains in exoEarth candidate yield and more than an order of magnitude reduction in exposure times for the highest priority targets. We conclude that a combination of telescope diameter increase and design improvements could provide robust exoplanet science margins for HWO., Comment: 90 pages, 26 figures, 4 tables, Submitted to JATIS

A planet's history dictates its current potential to host habitable conditions and life. The concept of the Continuously Habitable Zone (CHZ) has been used to define the region around a star most likely to host planets with long-term habitability. However, definitions of the CHZ vary in the literature and often conflict with each other. Calculating the fraction of habitable zone planets in the CHZ as a function of stellar properties, we find that the quality of a star as a host for planets with long-term habitability and biosignatures depends strongly on the formulation of the CHZ used. For instance, older M stars are either excellent or sub-optimal hosts for CHZ planets, depending on whether one's definition of habitability prioritizes the total time spent in the habitable zone or the continuity of habitable conditions from the delivery of volatiles to its current age. In this study, we focus on Belatedly Habitable Zone (BHZ) planets, i.e. planets which enter the habitable zone after formation due to the evolution of their host star. We find that between ~29-74% of planets in the habitable zone belong to this class of BHZ planets depending on the timescale for the delivery of volatiles. Whether these planets can retain their volatiles and support habitable conditions is unclear. Since BHZ planets comprise a large portion of the planets we expect to survey for biosignatures with future missions, the open question of their habitability is an important factor for mission design, survey strategies, and the interpretation of results., Comment: Accepted for publication in The Astrophysical Journal

Future exoplanet direct imaging missions, such as HabEx and LUVOIR, will select target stars to maximize the number of Earth-like exoplanets that can have their atmospheric compositions characterized. Because one of these missions' aims is to detect biosignatures, they should also consider the expected biosignature yield of planets around these stars. In this work, we develop a method of computing relative biosignature yields among potential target stars, given a model of habitability and biosignature genesis, and using a star's habitability history. As an illustration and first application of this method, we use MESA stellar models to calculate the time evolution of the habitable zone, and examine three simple models for biosignature genesis to calculate the relative biosignature yield for different stars. We find that the relative merits of K stars versus F stars depend sensitively on model choice. In particular, use of the present-day habitable zone as a proxy for biosignature detectability favors young, luminous stars lacking the potential for long-term habitability. Biosignature yields are also sensitive to whether life can arise on Cold Start exoplanets that enter the habitable zone after formation, an open question deserving of more attention. Using the case study of biosignature yields calculated for $\theta$ Cygni and 55 Cancri, we find that robust mission design and target selection for HabEx and LUVOIR depends on: choosing a specific model of biosignature appearance with time; the terrestrial planet occurrence rate as a function of orbital separation; precise knowledge of stellar properties; and accurate stellar evolutionary histories.

Transit timing variations (TTVs) are a valuable tool to determine the masses and orbits of transiting planets in multi-planet systems. TTVs can be readily modeled given knowledge of the interacting planets' orbital configurations and planet-star mass ratios, but such models are highly nonlinear and difficult to invert. Markov chain Monte Carlo (MCMC) methods are often used to explore the posterior distribution for model parameters, but, due to the high correlations between parameters, nonlinearity, and potential multi-modality in the posterior, many samplers perform very inefficiently. Therefore, we assess the performance of several MCMC samplers that use varying degrees of geometric information about the target distribution. We generate synthetic datasets from multiple models, including the TTVFaster model and a simple sinusoidal model, and test the efficiencies of various MCMC samplers. We find that sampling efficiency can be greatly improved for all models by sampling from a parameter space transformed using an estimate of the covariance and means of the target distribution. No one sampler performs the best for all datasets, but several samplers, such as Differential Evolution Monte Carlo and Geometric adaptive Monte Carlo, have consistently efficient performance. For datasets with near Gaussian posteriors, Hamiltonian Monte Carlo samplers with 2 or 3 leapfrog steps obtained the highest efficiencies. Based on differences in effective sample sizes per time, we show that the right choice of sampler can improve sampling efficiencies by several orders of magnitude.